diff --git a/oelicense/.gitignore b/oelicense/.gitignore deleted file mode 100644 index 22941a8..0000000 --- a/oelicense/.gitignore +++ /dev/null @@ -1,2 +0,0 @@ -# oe license file -oe_license.txt diff --git a/other-materials/README.md b/other-materials/README.md index 9e90abd..29763f0 100644 --- a/other-materials/README.md +++ b/other-materials/README.md @@ -6,7 +6,7 @@ It may be crosslinked from course content, etc. ## Manifest - `python-intro`: Directory containing introductory material on Python; see [`python-intro/README.md`](python-intro/README.md). See also the Beckstein material linked below. -- `solvation-free-energies`: Directory containing example materials for hydration free energy calculations with Yank; see [`solvation-free-energies/README.md`][solvation-free-energies/README.md]i +- `solvation-free-energies`: Directory containing example materials for hydration free energy calculations with Yank; see [`solvation-free-energies/README.md`][solvation-free-energies/README.md]; Yank would need to be installed first, and support for it is not currently very good, so hopefully we can improve on this soon. ## Other useful materials: - For very helpful material on Linux and Python relevant to our area, refer to [Oliver Beckstein's Computational Methods in Physics course](https://asu-compmethodsphysics-phy494.github.io/ASU-PHY494/overview/), which has sectionns on Linux/Unix, Python, git, etc. diff --git a/other-materials/solvation-free-energies/README.md b/other-materials/solvation-free-energies/README.md index acfe25e..d5a9255 100644 --- a/other-materials/solvation-free-energies/README.md +++ b/other-materials/solvation-free-energies/README.md @@ -1,6 +1,6 @@ # Solvation free energy calculations with Yank -This provides example materials for solvation free energy calculations with implicit and explicit solvent in Yank. +This provides example materials for solvation free energy calculations with implicit and explicit solvent in Yank. You would need to install Yank first. Demonstrations are set up for hydration free energy calculations on FreeSolv and/or FreeSolvMini. I validated this, in the implicit solvent case, for phenol from IUPAC name, SMILES, and FreeSolv (mobley_20524.mol2) and get consistent values -- basically -10.08+/-0.01 kT in all cases. diff --git a/other-materials/solvation-free-energies/run_hydration.py b/other-materials/solvation-free-energies/run_hydration.py index 7e3770f..820efde 100644 --- a/other-materials/solvation-free-energies/run_hydration.py +++ b/other-materials/solvation-free-energies/run_hydration.py @@ -7,7 +7,7 @@ from simtk.openmm import app import netCDF4 as netcdf from tempfile import TemporaryDirectory -import yank +import yank #Note before using this you should install yank import mdtraj from yank.experiment import * import textwrap diff --git a/uci-pharmsci/Getting_Started.ipynb b/uci-pharmsci/Getting_Started.ipynb index d194a35..e19c51f 100644 --- a/uci-pharmsci/Getting_Started.ipynb +++ b/uci-pharmsci/Getting_Started.ipynb @@ -17,7 +17,7 @@ "source": [ "# Geting Started with Google Colab for PHARMSCI 275\n", "\n", - "This notebook **provides information on getting started with Google Colab** and shows code cells that need to be run before attempting to complete any of the assignments for this class **IF you are using Google Colab**. Typically, you will need to insert this code -- or the similar code in [the equivalent condacolab notebook](Getting_Started_condacolab.ipynb) into your \n", + "This notebook **provides information on getting started with Google Colab** and shows code cells that need to be run before attempting to complete any of the assignments for this class **IF you are using Google Colab**. Typically, you will need to insert this code into your notebook to start off.\n", "\n", "**Please note that it is important to make sure that all of the code cells used in this notebook are present in your assignment notebooks and have been executed before coming to class** " ] @@ -42,17 +42,7 @@ "id": "ycSxhNWL0fvm", "outputId": "086ea934-bdf6-406b-f164-3e8d6a32d5e7" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/usr/local/bin/python\n", - "Python 3.7.12\n", - "/env/python\n" - ] - } - ], + "outputs": [], "source": [ "!which python\n", "!python --version\n", @@ -65,7 +55,7 @@ "id": "ncaEUELr4mOm" }, "source": [ - "Unsetting python $PYTHONPATH variable. This prevents any issues with miniconda installations" + "Unsetting python $PYTHONPATH variable. This prevents any issues with miniforge installations" ] }, { @@ -78,32 +68,41 @@ "id": "8osn0I9L4YJk", "outputId": "ad3df4b8-f05a-4e8f-8f3a-6b90eee3cf38" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "env: PYTHONPATH=\n" - ] - } - ], + "outputs": [], "source": [ "%env PYTHONPATH=" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Using fortran on Google Colab\n", + "\n", + "As of our 2023 class, we were having library issues with fortran compilers on Google Colab if we install anything else first. So for assignments using fortran libraries, you may wish to make sure you use f2py to compile the fortran library before doing or installing anything else.\n", + "\n", + "In my case, I occasionally found that some user-space material seems to be brought into Colab by any Jupyter notebook I'm using, so if I'm working in a notebook in which I previously installed numpy or similar (gfortran, etc.) and I open that notebook in Colab, I often run into library problems where I can't run f2py. It's only if I use f2py in a clean, new notebook that I can get it to work. \n", + "\n", + "For the energy minimization assignment, then, for example, I make my first command in a new notebook be:\n", + "`!f2py -c -m emlib emlib.f90`\n", + "and then do \n", + "`import emlib`\n", + "and it works." + ] + }, { "cell_type": "markdown", "metadata": { "id": "GW0kD_k25FBa" }, "source": [ - "## Installing Miniconda\n", - "the code below will download the installer script for the appropriate version of Miniconda and install it into /usr/local. Installing directly into /usr/local, rather than into the default location ~/miniconda3, insures that Conda and all its required dependencies will be automatically available for use within Google Colab.\n", + "## Installing mambaforge/miniforge\n", + "the code below will download the installer script for the appropriate version of mamba and install it into /usr/local. Installing directly into /usr/local, rather than into the default location, insures that mamba and all its required dependencies will be automatically available for use within Google Colab.\n", "\n", "### Apending System Path\n", - "Add the directory where Conda will install packages to the list of directories that Python will search when looking for modules to import.\n", + "Add the directory where mamba will install packages to the list of directories that Python will search when looking for modules to import.\n", "\n", - "Note that because the /usr/local/lib/python3.7/dist-packages directory containing the pre-installed Google Colab packages appears ahead of the /usr/local/lib/python3.7/site-packages directory where Conda installs packages, the version of a package available via Google Colab will take precedence over any version of the same package installed via Conda." + "Note that because the /usr/local/lib/python3.x/dist-packages directory containing the pre-installed Google Colab packages appears ahead of the /usr/local/lib/python3.x/site-packages directory where mamba installs packages, the version of a package available via Google Colab will take precedence over any version of the same package installed via mamba." ] }, { @@ -116,124 +115,14 @@ "id": "EkWDphve5Nje", "outputId": "affe7d25-d5a6-4a01-c8d2-0b1a4cafdfea" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "--2021-12-22 20:59:30-- https://repo.anaconda.com/miniconda/Miniconda3-py37_4.10.3-Linux-x86_64.sh\n", - "Resolving repo.anaconda.com (repo.anaconda.com)... 104.16.131.3, 104.16.130.3, 2606:4700::6810:8303, ...\n", - "Connecting to repo.anaconda.com (repo.anaconda.com)|104.16.131.3|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 89026327 (85M) [application/x-sh]\n", - "Saving to: ‘Miniconda3-py37_4.10.3-Linux-x86_64.sh’\n", - "\n", - "Miniconda3-py37_4.1 100%[===================>] 84.90M 300MB/s in 0.3s \n", - "\n", - "2021-12-22 20:59:30 (300 MB/s) - ‘Miniconda3-py37_4.10.3-Linux-x86_64.sh’ saved [89026327/89026327]\n", - "\n", - "PREFIX=/usr/local\n", - "Unpacking payload ...\n", - "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\bdone\n", - "Solving environment: - \b\b\\ \b\b| \b\bdone\n", - "\n", - "## Package Plan ##\n", - "\n", - " environment location: /usr/local\n", - "\n", - " added / updated specs:\n", - " - _libgcc_mutex==0.1=main\n", - " - _openmp_mutex==4.5=1_gnu\n", - " - brotlipy==0.7.0=py37h27cfd23_1003\n", - " - ca-certificates==2021.7.5=h06a4308_1\n", - " - certifi==2021.5.30=py37h06a4308_0\n", - " - cffi==1.14.6=py37h400218f_0\n", - " - chardet==4.0.0=py37h06a4308_1003\n", - " - conda-package-handling==1.7.3=py37h27cfd23_1\n", - " - conda==4.10.3=py37h06a4308_0\n", - " - cryptography==3.4.7=py37hd23ed53_0\n", - " - idna==2.10=pyhd3eb1b0_0\n", - " - ld_impl_linux-64==2.35.1=h7274673_9\n", - " - libffi==3.3=he6710b0_2\n", - " - libgcc-ng==9.3.0=h5101ec6_17\n", - " - libgomp==9.3.0=h5101ec6_17\n", - " - libstdcxx-ng==9.3.0=hd4cf53a_17\n", - " - ncurses==6.2=he6710b0_1\n", - " - openssl==1.1.1k=h27cfd23_0\n", - " - pip==21.1.3=py37h06a4308_0\n", - " - pycosat==0.6.3=py37h27cfd23_0\n", - " - pycparser==2.20=py_2\n", - " - pyopenssl==20.0.1=pyhd3eb1b0_1\n", - " - pysocks==1.7.1=py37_1\n", - " - python==3.7.10=h12debd9_4\n", - " - readline==8.1=h27cfd23_0\n", - " - requests==2.25.1=pyhd3eb1b0_0\n", - " - ruamel_yaml==0.15.100=py37h27cfd23_0\n", - " - setuptools==52.0.0=py37h06a4308_0\n", - " - six==1.16.0=pyhd3eb1b0_0\n", - " - sqlite==3.36.0=hc218d9a_0\n", - " - tk==8.6.10=hbc83047_0\n", - " - tqdm==4.61.2=pyhd3eb1b0_1\n", - " - urllib3==1.26.6=pyhd3eb1b0_1\n", - " - wheel==0.36.2=pyhd3eb1b0_0\n", - " - xz==5.2.5=h7b6447c_0\n", - " - yaml==0.2.5=h7b6447c_0\n", - " - zlib==1.2.11=h7b6447c_3\n", - "\n", - "\n", - "The following NEW packages will be INSTALLED:\n", - "\n", - " _libgcc_mutex pkgs/main/linux-64::_libgcc_mutex-0.1-main\n", - " _openmp_mutex pkgs/main/linux-64::_openmp_mutex-4.5-1_gnu\n", - " brotlipy pkgs/main/linux-64::brotlipy-0.7.0-py37h27cfd23_1003\n", - " ca-certificates pkgs/main/linux-64::ca-certificates-2021.7.5-h06a4308_1\n", - " certifi pkgs/main/linux-64::certifi-2021.5.30-py37h06a4308_0\n", - " cffi pkgs/main/linux-64::cffi-1.14.6-py37h400218f_0\n", - " chardet pkgs/main/linux-64::chardet-4.0.0-py37h06a4308_1003\n", - " conda pkgs/main/linux-64::conda-4.10.3-py37h06a4308_0\n", - " conda-package-han~ pkgs/main/linux-64::conda-package-handling-1.7.3-py37h27cfd23_1\n", - " cryptography pkgs/main/linux-64::cryptography-3.4.7-py37hd23ed53_0\n", - " idna pkgs/main/noarch::idna-2.10-pyhd3eb1b0_0\n", - " ld_impl_linux-64 pkgs/main/linux-64::ld_impl_linux-64-2.35.1-h7274673_9\n", - " libffi pkgs/main/linux-64::libffi-3.3-he6710b0_2\n", - " libgcc-ng pkgs/main/linux-64::libgcc-ng-9.3.0-h5101ec6_17\n", - " libgomp pkgs/main/linux-64::libgomp-9.3.0-h5101ec6_17\n", - " libstdcxx-ng pkgs/main/linux-64::libstdcxx-ng-9.3.0-hd4cf53a_17\n", - " ncurses pkgs/main/linux-64::ncurses-6.2-he6710b0_1\n", - " openssl pkgs/main/linux-64::openssl-1.1.1k-h27cfd23_0\n", - " pip pkgs/main/linux-64::pip-21.1.3-py37h06a4308_0\n", - " pycosat pkgs/main/linux-64::pycosat-0.6.3-py37h27cfd23_0\n", - " pycparser pkgs/main/noarch::pycparser-2.20-py_2\n", - " pyopenssl pkgs/main/noarch::pyopenssl-20.0.1-pyhd3eb1b0_1\n", - " pysocks pkgs/main/linux-64::pysocks-1.7.1-py37_1\n", - " python pkgs/main/linux-64::python-3.7.10-h12debd9_4\n", - " readline pkgs/main/linux-64::readline-8.1-h27cfd23_0\n", - " requests pkgs/main/noarch::requests-2.25.1-pyhd3eb1b0_0\n", - " ruamel_yaml pkgs/main/linux-64::ruamel_yaml-0.15.100-py37h27cfd23_0\n", - " setuptools pkgs/main/linux-64::setuptools-52.0.0-py37h06a4308_0\n", - " six pkgs/main/noarch::six-1.16.0-pyhd3eb1b0_0\n", - " sqlite pkgs/main/linux-64::sqlite-3.36.0-hc218d9a_0\n", - " tk pkgs/main/linux-64::tk-8.6.10-hbc83047_0\n", - " tqdm pkgs/main/noarch::tqdm-4.61.2-pyhd3eb1b0_1\n", - " urllib3 pkgs/main/noarch::urllib3-1.26.6-pyhd3eb1b0_1\n", - " wheel pkgs/main/noarch::wheel-0.36.2-pyhd3eb1b0_0\n", - " xz pkgs/main/linux-64::xz-5.2.5-h7b6447c_0\n", - " yaml pkgs/main/linux-64::yaml-0.2.5-h7b6447c_0\n", - " zlib pkgs/main/linux-64::zlib-1.2.11-h7b6447c_3\n", - "\n", - "\n", - "Preparing transaction: - \b\b\\ \b\b| \b\b/ \b\bdone\n", - "Executing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", - "installation finished.\n" - ] - } - ], + "outputs": [], "source": [ - "! wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.10.3-Linux-x86_64.sh\n", - "! chmod +x Miniconda3-py37_4.10.3-Linux-x86_64.sh\n", - "! bash ./Miniconda3-py37_4.10.3-Linux-x86_64.sh -b -f -p /usr/local\n", + "%env PYTHONPATH=\n", + "! wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh\n", + "! chmod +x Miniforge3-Linux-x86_64.sh\n", + "! bash ./Miniforge3-Linux-x86_64.sh -b -f -p /usr/local\n", "import sys\n", - "sys.path.append('/usr/local/lib/python3.7/site-packages/')" + "sys.path.append('/usr/local/lib/python3.12/site-packages/')" ] }, { @@ -246,24 +135,8 @@ "id": "oi07wwTT8ihQ", "outputId": "aba83038-7eae-4321-da84-d059191117b6" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "/usr/local/bin/conda\n", - "conda 4.10.3\n", - "/usr/local/bin/python\n", - "Python 3.7.10\n" - ] - } - ], - "source": [ - "!which conda\n", - "!conda --version\n", - "!which python\n", - "!python --version" - ] + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -272,9 +145,9 @@ }, "source": [ "# Installing Packages\n", - "In this section we will install libgfortran and openeye toolkits. **Depending on the lecture and/or assignment, you may or may not need either or both of these**. \n", + "In this section we will install gfortran and openeye toolkits. **Depending on the lecture and/or assignment, you may or may not need either or both of these**. \n", "\n", - "`libgfortran` is needed mainly for the energy minimization, MD and MC assignments and the MD and MC lectures, where we compile fortran code. If you are not working with `f2py3`, you will not need libgfortran.\n", + "`gfortran` is needed mainly for the energy minimization, MD and MC assignments and the MD and MC lectures, where we compile fortran code. If you are not working with `f2py`, you will not need libgfortran.\n", "\n", "The OpenEye toolkits are used more frequently, for general cheminformatics work. Still, they are only used in some notebooks, so it's only necessary to install them if the assignment or lecture contains `import openeye` or similar." ] @@ -287,12 +160,14 @@ "source": [ "## Installing Fortran\n", "\n", - "We will be utilizing fortran to complete some of the assignments in the course. Hence it is important to make sure that it is loaded and working fine." + "We will be utilizing fortran to complete some of the assignments in the course. Hence it is important to make sure that it is loaded and working fine.\n", + "\n", + "Prior to Python 3.1.2, we could simply install `gfortran` only and everything was good, but as of early 2025 apparently there's a new build system which requires slightly more wrangling, so now we use:" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -305,70 +180,797 @@ "name": "stdout", "output_type": "stream", "text": [ - "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", - "Solving environment: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", - "\n", - "\n", - "==> WARNING: A newer version of conda exists. <==\n", - " current version: 4.10.3\n", - " latest version: 4.11.0\n", - "\n", - "Please update conda by running\n", - "\n", - " $ conda update -n base -c defaults conda\n", "\n", + " __ __ __ __\n", + " / \\ / \\ / \\ / \\\n", + " / \\/ \\/ \\/ \\\n", + "███████████████/ /██/ /██/ /██/ /████████████████████████\n", + " / / \\ / \\ / \\ / \\ \\____\n", + " / / \\_/ \\_/ \\_/ \\ o \\__,\n", + " / _/ \\_____/ `\n", + " |/\n", + " ███╗ ███╗ █████╗ ███╗ ███╗██████╗ █████╗\n", + " ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗\n", + " ██╔████╔██║███████║██╔████╔██║██████╔╝███████║\n", + " ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║\n", + " ██║ ╚═╝ ██║██║ ██║██║ ╚═╝ ██║██████╔╝██║ ██║\n", + " ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚═════╝ ╚═╝ ╚═╝\n", "\n", + " mamba (1.4.2) supported by @QuantStack\n", "\n", - "## Package Plan ##\n", + " GitHub: https://github.com/mamba-org/mamba\n", + " Twitter: https://twitter.com/QuantStack\n", "\n", - " environment location: /usr/local\n", + "█████████████████████████████████████████████████████████████\n", "\n", - " added / updated specs:\n", - " - libgfortran\n", "\n", + "Looking for: ['gfortran']\n", "\n", - "The following packages will be downloaded:\n", - "\n", - " package | build\n", - " ---------------------------|-----------------\n", - " ca-certificates-2021.10.8 | ha878542_0 139 KB conda-forge\n", - " certifi-2021.10.8 | py37h89c1867_1 145 KB conda-forge\n", - " conda-4.11.0 | py37h89c1867_0 16.9 MB conda-forge\n", - " libgfortran-3.0.0 | 1 281 KB conda-forge\n", - " openssl-1.1.1l | h7f8727e_0 2.5 MB\n", - " python_abi-3.7 | 2_cp37m 4 KB conda-forge\n", - " ------------------------------------------------------------\n", - " Total: 20.0 MB\n", - "\n", - "The following NEW packages will be INSTALLED:\n", - "\n", - " libgfortran conda-forge/linux-64::libgfortran-3.0.0-1\n", - " python_abi conda-forge/linux-64::python_abi-3.7-2_cp37m\n", + "\u001b[?25l\u001b[2K\u001b[0G[+] 0.0s\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.1s\n", + "conda-forge/osx-64 \u001b[33m━━━━━━━━━━━━━╸\u001b[0m\u001b[90m━━━━━━━━━━━\u001b[0m 0.0 B / ??.?MB @ ??.?MB/s 0.1s\n", + "conda-forge/noarch 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145 KB | : 100% 1.0/1 [00:00<00:00, 13.88it/s]\n", - "python_abi-3.7 | 4 KB | : 100% 1.0/1 [00:00<00:00, 30.43it/s]\n", - "openssl-1.1.1l | 2.5 MB | : 100% 1.0/1 [00:00<00:00, 6.53it/s]\n", - "conda-4.11.0 | 16.9 MB | : 100% 1.0/1 [00:05<00:00, 5.20s/it]\n", - "libgfortran-3.0.0 | 281 KB | : 100% 1.0/1 [00:00<00:00, 12.41it/s]\n", - "Preparing transaction: - \b\bdone\n", - "Verifying transaction: | \b\b/ \b\bdone\n", - "Executing transaction: \\ \b\bdone\n" + "\u001b[?25l\u001b[2K\u001b[0G\u001b[?25h" ] } ], "source": [ - "!conda install -c conda-forge libgfortran --yes" + "#!mamba install -c conda-forge gfortran --yes\n", + "!mamba install numpy\">=1.25.0\" meson ninja scikit-build-core gfortran -y" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's check that things are working properly; we should get Python 3.12 and useful Python and numpy versions (numpy greater than 1.25)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!which python\n", + "!python --version\n", + "!meson --version\n", + "!python -c \"import numpy; print(numpy.__version__)\"" ] }, { @@ -399,16 +1001,9 @@ "id": "WYnhM6RCEXNF", "outputId": "060408d0-8df6-43ed-e88d-4d6dfba2ec09" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mounted at /content/drive\n" - ] - } - ], + "outputs": [], "source": [ + "# Mount Google Drive. NOTE: Do NOT move this before install of miniforge and other packages above or you may encounter path issues.\n", "from google.colab import drive\n", "drive.mount('/content/drive',force_remount = True)\n" ] @@ -419,7 +1014,7 @@ "id": "Ht5SkFfaFZBM" }, "source": [ - "Conda install the OpenEye toolkits:" + "mamba install the OpenEye toolkits:" ] }, { @@ -432,94 +1027,9 @@ "id": "BSgojnrBGeJi", "outputId": "33f0cf8c-bb5c-4fee-d522-bf9b07ac204b" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n", - "Solving environment: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", - "\n", - "## Package Plan ##\n", - "\n", - " environment location: /usr/local\n", - "\n", - " added / updated specs:\n", - " - openeye-toolkits\n", - "\n", - "\n", - "The following packages will be downloaded:\n", - "\n", - " package | build\n", - " ---------------------------|-----------------\n", - " attrs-21.2.0 | pyhd3eb1b0_0 46 KB\n", - " ca-certificates-2021.10.26 | h06a4308_2 115 KB\n", - " conda-4.11.0 | py37h06a4308_0 14.4 MB\n", - " importlib-metadata-4.8.2 | py37h06a4308_0 39 KB\n", - " importlib_metadata-4.8.2 | hd3eb1b0_0 12 KB\n", - " iniconfig-1.1.1 | pyhd3eb1b0_0 8 KB\n", - " openeye-toolkits-2021.2.0 | py37_0 79.6 MB openeye\n", - " packaging-21.3 | pyhd3eb1b0_0 36 KB\n", - " pluggy-1.0.0 | py37h06a4308_0 28 KB\n", - " py-1.10.0 | pyhd3eb1b0_0 76 KB\n", - " pyparsing-3.0.4 | pyhd3eb1b0_0 81 KB\n", - " pytest-6.2.5 | py37h06a4308_2 425 KB\n", - " toml-0.10.2 | pyhd3eb1b0_0 20 KB\n", - " typing_extensions-3.10.0.2 | pyh06a4308_0 31 KB\n", - " zipp-3.6.0 | pyhd3eb1b0_0 17 KB\n", - " ------------------------------------------------------------\n", - " Total: 94.9 MB\n", - "\n", - "The following NEW packages will be INSTALLED:\n", - "\n", - " attrs pkgs/main/noarch::attrs-21.2.0-pyhd3eb1b0_0\n", - " importlib-metadata pkgs/main/linux-64::importlib-metadata-4.8.2-py37h06a4308_0\n", - " importlib_metadata pkgs/main/noarch::importlib_metadata-4.8.2-hd3eb1b0_0\n", - " iniconfig pkgs/main/noarch::iniconfig-1.1.1-pyhd3eb1b0_0\n", - " openeye-toolkits openeye/linux-64::openeye-toolkits-2021.2.0-py37_0\n", - " packaging pkgs/main/noarch::packaging-21.3-pyhd3eb1b0_0\n", - " pluggy pkgs/main/linux-64::pluggy-1.0.0-py37h06a4308_0\n", - " py pkgs/main/noarch::py-1.10.0-pyhd3eb1b0_0\n", - " pyparsing pkgs/main/noarch::pyparsing-3.0.4-pyhd3eb1b0_0\n", - " pytest pkgs/main/linux-64::pytest-6.2.5-py37h06a4308_2\n", - " toml pkgs/main/noarch::toml-0.10.2-pyhd3eb1b0_0\n", - " typing_extensions pkgs/main/noarch::typing_extensions-3.10.0.2-pyh06a4308_0\n", - " zipp pkgs/main/noarch::zipp-3.6.0-pyhd3eb1b0_0\n", - "\n", - "The following packages will be UPDATED:\n", - "\n", - " ca-certificates conda-forge::ca-certificates-2021.10.~ --> pkgs/main::ca-certificates-2021.10.26-h06a4308_2\n", - "\n", - "The following packages will be SUPERSEDED by a higher-priority channel:\n", - "\n", - " conda conda-forge::conda-4.11.0-py37h89c186~ --> pkgs/main::conda-4.11.0-py37h06a4308_0\n", - "\n", - "\n", - "\n", - "Downloading and Extracting Packages\n", - "pluggy-1.0.0 | 28 KB | : 100% 1.0/1 [00:00<00:00, 17.83it/s]\n", - "packaging-21.3 | 36 KB | : 100% 1.0/1 [00:00<00:00, 22.45it/s]\n", - "conda-4.11.0 | 14.4 MB | : 100% 1.0/1 [00:00<00:00, 1.24it/s] \n", - "pyparsing-3.0.4 | 81 KB | : 100% 1.0/1 [00:00<00:00, 17.14it/s]\n", - "importlib-metadata-4 | 39 KB | : 100% 1.0/1 [00:00<00:00, 20.98it/s]\n", - "pytest-6.2.5 | 425 KB | : 100% 1.0/1 [00:00<00:00, 14.42it/s]\n", - "openeye-toolkits-202 | 79.6 MB | : 100% 1.0/1 [00:12<00:00, 12.96s/it]\n", - "iniconfig-1.1.1 | 8 KB | : 100% 1.0/1 [00:00<00:00, 28.93it/s]\n", - "ca-certificates-2021 | 115 KB | : 100% 1.0/1 [00:00<00:00, 23.00it/s]\n", - "toml-0.10.2 | 20 KB | : 100% 1.0/1 [00:00<00:00, 20.26it/s]\n", - "typing_extensions-3. | 31 KB | : 100% 1.0/1 [00:00<00:00, 24.53it/s]\n", - "zipp-3.6.0 | 17 KB | : 100% 1.0/1 [00:00<00:00, 23.17it/s]\n", - "attrs-21.2.0 | 46 KB | : 100% 1.0/1 [00:00<00:00, 24.91it/s]\n", - "importlib_metadata-4 | 12 KB | : 100% 1.0/1 [00:00<00:00, 25.52it/s]\n", - "py-1.10.0 | 76 KB | : 100% 1.0/1 [00:00<00:00, 20.35it/s]\n", - "Preparing transaction: / \b\b- \b\bdone\n", - "Verifying transaction: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n", - "Executing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n" - ] - } - ], + "outputs": [], "source": [ - "!conda install -c openeye openeye-toolkits --yes" + "!mamba install -c openeye openeye-toolkits --yes" ] }, { @@ -545,15 +1055,7 @@ "id": "Qt-gYSE8JsXz", "outputId": "28b1f26b-8782-418d-c6e9-5cfe4674a67e" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Was your OpenEye license correctly installed (True/False)? True\n" - ] - } - ], + "outputs": [], "source": [ "license_filename = '/content/drive/MyDrive/oe_license.txt'\n", "\n", @@ -572,6 +1074,55 @@ " print(\"Error: Your OpenEye license is not correctly installed.\")\n", " raise Exception(\"Error: Your OpenEye license is not correctly installed.\")" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## File handling\n", + "\n", + "If you are working on course content that uses supporting files provided by the class (e.g. a fortran library) you will need to make sure that:\n", + "1) This content is uploaded to your Google Drive\n", + "2) Your Google Drive is mounted to Google Colab\n", + "3) You navigate to the directory containing that content\n", + "\n", + "Here is an example of how I would do that if I've uploaded the whole course GitHub repository to Google Drive and want to use content in the energy minimization assignment directory (note I will need to give various permissions for Colab to access my Google account, and I must have already put the relevant files there):" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'google.colab'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[1], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# Mount Google Drive. NOTE: Do NOT move this before install of miniforge and other packages above or you may encounter path issues.\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mgoogle\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcolab\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m drive\n\u001b[1;32m 3\u001b[0m drive\u001b[38;5;241m.\u001b[39mmount(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m/content/drive\u001b[39m\u001b[38;5;124m'\u001b[39m,force_remount \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 5\u001b[0m \u001b[38;5;66;03m# This uses a \"magic\" command involving the percent symbol to execute command-line prompts\u001b[39;00m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'google.colab'" + ] + } + ], + "source": [ + "# Mount Google Drive. NOTE: Do NOT move this before install of miniforge and other packages above or you may encounter path issues.\n", + "from google.colab import drive\n", + "drive.mount('/content/drive',force_remount = True)\n", + "\n", + "# This uses a \"magic\" command involving the percent symbol to execute command-line prompts\n", + "%cd /content/drive/MyDrive/drug-computing/uci-pharmsci/assignments/energy_minimization\n", + "%ls\n", + "# Here I used the ls command just to make sure I see the content I should in that directory (e.g. that I uploaded correctly etc.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -595,9 +1146,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.12.8" } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/uci-pharmsci/Getting_Started_condacolab.ipynb b/uci-pharmsci/Getting_Started_condacolab.ipynb deleted file mode 100644 index 43a82c0..0000000 --- a/uci-pharmsci/Getting_Started_condacolab.ipynb +++ /dev/null @@ -1,5482 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MobleyLab/drug-computing/blob/master/uci-pharmsci/Getting_Started_condacolab.ipynb)" - ], - "metadata": { - "id": "QHMl5iOcxiQm" - } - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7uWUhEZk_BKQ" - }, - "source": [ - "# Getting Started with Condacolab\n", - "This notebook describes the steps to set up condacolab, which is another method of using the conda package manager on google colab notebooks.\n", - "\n", - "Here is a link to the package [repository](https://github.com/conda-incubator/condacolab/)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "R4srDnu0_Utk", - "outputId": "c676025b-b834-46e2-bbce-50f3b082f42f" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✨🍰✨ Everything looks OK!\n" - ] - } - ], - "source": [ - "!pip install -q condacolab\n", - "import condacolab\n", - "condacolab.install()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "W69bjdHkAe2n" - }, - "source": [ - "Make sure that the kernel restarts so that you can run the cell below. It should be ensured that the above code block should be run first before running any other cells since it restarts the kernel and resets the variables." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "A30gwGyfAjhd", - "outputId": "89a57efc-f21d-4c64-dae7-3f914b995995" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "✨🍰✨ Everything looks OK!\n" - ] - } - ], - "source": [ - "import condacolab\n", - "condacolab.check()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "1oyPEmeYA7YJ" - }, - "source": [ - "# Package Installation\n", - "The section below will describe the necessary steps to install packages required for the course. Please make sure to run them in the appropriate order.\n", - "\n", - "Note: **We will be using mamba here instead of conda since it is faster. Mamba is more or less a drop-in replacement for conda, so if you are using mamba in lectures/assignments you should switch the `conda` command to the `mamba` command everywhere it occurs.**\n", - "\n", - "In this section we will install libgfortran and openeye toolkits. **Depending on the lecture and/or assignment, you may or may not need either or both of these**. \n", - "\n", - "`libgfortran` is needed mainly for the energy minimization, MD and MC assignments and the MD and MC lectures, where we compile fortran code. If you are not working with `f2py3`, you will not need libgfortran.\n", - "\n", - "The OpenEye toolkits are used more frequently, for general cheminformatics work. Still, they are only used in some notebooks, so it's only necessary to install them if the assignment or lecture contains `import openeye` or similar." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wj4m2U8uBXUm" - }, - "source": [ - "## Installation of Fortran\n", - "This involves the installation of 3 packages, gcc (fortran compiler for conda), libgfortran (fortran library for conda) and f2py (which allows us to convert fortran code to python outputs)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "PoMiN1kMBWEn", - "outputId": "89999ec6-9c04-4a4f-b6ec-97cf23876c37" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " __ __ __ __\n", - " / \\ / \\ / \\ / \\\n", - " / \\/ \\/ \\/ \\\n", - "███████████████/ /██/ /██/ /██/ /████████████████████████\n", - " / / \\ / \\ / \\ / \\ \\____\n", - " / / \\_/ \\_/ \\_/ \\ o \\__,\n", - " / _/ \\_____/ `\n", - " |/\n", - " ███╗ ███╗ █████╗ ███╗ ███╗██████╗ █████╗\n", - " ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗\n", - " ██╔████╔██║███████║██╔████╔██║██████╔╝███████║\n", - " ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║\n", - " ██║ ╚═╝ ██║██║ ██║██║ ╚═╝ ██║██████╔╝██║ ██║\n", - " ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚═════╝ ╚═╝ ╚═╝\n", - "\n", - " mamba (0.8.0) supported by @QuantStack\n", - "\n", - " GitHub: https://github.com/mamba-org/mamba\n", - " Twitter: https://twitter.com/QuantStack\n", - "\n", - "█████████████████████████████████████████████████████████████\n", - "\n", - "\n", - "Looking for: ['gcc']\n", - "\n", - "conda-forge/linux-64 [] (00m:00s) \n", - "conda-forge/linux-64 [] (00m:00s) 112 KB / ?? (369.12 KB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 112 KB / ?? (369.12 KB/s)\n", - "conda-forge/noarch [] (00m:00s) \n", - "conda-forge/linux-64 [] (00m:00s) 112 KB / ?? (369.12 KB/s)\n", - "conda-forge/noarch [] (00m:00s) 91 KB / ?? (300.37 KB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 112 KB / ?? (369.12 KB/s)\n", - "conda-forge/noarch [] (00m:00s) 91 KB / ?? (300.37 KB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) \n", - "conda-forge/linux-64 [] (00m:00s) 112 KB / ?? (369.12 KB/s)\n", - "conda-forge/noarch [] (00m:00s) 91 KB / ?? (300.37 KB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 116 KB / ?? (382.45 KB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 112 KB / ?? (369.12 KB/s)\n", - "conda-forge/noarch [] (00m:00s) 91 KB / ?? (300.37 KB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 116 KB / ?? (382.45 KB/s)\n", - "pkgs/main/noarch [] (00m:00s) \n", - "conda-forge/linux-64 [] (00m:00s) 112 KB / ?? (369.12 KB/s)\n", - "conda-forge/noarch [] (00m:00s) 91 KB / ?? 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(382.45 KB/s)\n", - "pkgs/main/noarch [] (00m:00s) Finalizing...\n", - "pkgs/r/linux-64 [] (00m:00s) 128 KB / ?? (421.08 KB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 112 KB / ?? (369.12 KB/s)\n", - "conda-forge/noarch [] (00m:00s) 91 KB / ?? (300.37 KB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 116 KB / ?? (382.45 KB/s)\n", - "pkgs/main/noarch [] (00m:00s) Done\n", - "pkgs/r/linux-64 [] (00m:00s) 128 KB / ?? (421.08 KB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 112 KB / ?? (369.12 KB/s)\n", - "conda-forge/noarch [] (00m:00s) 91 KB / ?? (300.37 KB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 116 KB / ?? (382.45 KB/s)\n", - "pkgs/main/noarch [] (00m:00s) Done\n", - "pkgs/r/linux-64 [] (00m:00s) 128 KB / ?? (421.08 KB/s)\n", - "pkgs/main/noarch [] (00m:00s) Done\n", - "conda-forge/linux-64 [] (00m:00s) 112 KB / ?? (369.12 KB/s)\n", - "conda-forge/noarch [] (00m:00s) 91 KB / ?? (300.37 KB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 116 KB / ?? (382.45 KB/s)\n", - "pkgs/r/linux-64 [] (00m:00s) 128 KB / ?? (421.08 KB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 112 KB / ?? (369.12 KB/s)\n", - "conda-forge/noarch [] (00m:00s) 91 KB / ?? (300.37 KB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 116 KB / ?? (382.45 KB/s)\n", - "pkgs/r/linux-64 [] (00m:00s) 128 KB / ?? (421.08 KB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 652 KB / ?? (1.40 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 91 KB / ?? (300.37 KB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 116 KB / ?? (382.45 KB/s)\n", - "pkgs/r/linux-64 [] (00m:00s) 128 KB / ?? (421.08 KB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 652 KB / ?? (1.40 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 91 KB / ?? (300.37 KB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 116 KB / ?? (382.45 KB/s)\n", - "pkgs/r/linux-64 [] (00m:00s) 128 KB / ?? (421.08 KB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 652 KB / ?? (1.40 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 745 KB / ?? (1.60 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 116 KB / ?? (382.45 KB/s)\n", - "pkgs/r/linux-64 [] (00m:00s) 128 KB / ?? (421.08 KB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 652 KB / ?? (1.40 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 745 KB / ?? (1.60 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 116 KB / ?? (382.45 KB/s)\n", - "pkgs/r/linux-64 [] (00m:00s) 128 KB / ?? (421.08 KB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 652 KB / ?? (1.40 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 745 KB / ?? (1.60 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 868 KB / ?? (1.86 MB/s)\n", - "pkgs/r/linux-64 [] (00m:00s) 128 KB / ?? (421.08 KB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 652 KB / ?? (1.40 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 745 KB / ?? (1.60 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 868 KB / ?? (1.86 MB/s)\n", - "pkgs/r/linux-64 [] (00m:00s) 128 KB / ?? (421.08 KB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 652 KB / ?? (1.40 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 745 KB / ?? (1.60 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 868 KB / ?? (1.86 MB/s)\n", - "pkgs/r/linux-64 [] (00m:00s) 980 KB / ?? (2.10 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 652 KB / ?? (1.40 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 745 KB / ?? (1.60 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 868 KB / ?? (1.86 MB/s)\n", - "pkgs/r/linux-64 [] (00m:00s) Finalizing...\n", - "conda-forge/linux-64 [] (00m:00s) 652 KB / ?? (1.40 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 745 KB / ?? (1.60 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 868 KB / ?? (1.86 MB/s)\n", - "pkgs/r/linux-64 [] (00m:00s) Done\n", - "conda-forge/linux-64 [] (00m:00s) 652 KB / ?? (1.40 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 745 KB / ?? (1.60 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 868 KB / ?? (1.86 MB/s)\n", - "pkgs/r/linux-64 [] (00m:00s) Done\n", - "pkgs/r/linux-64 [] (00m:00s) Done\n", - "conda-forge/linux-64 [] (00m:00s) 652 KB / ?? (1.40 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 745 KB / ?? (1.60 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 868 KB / ?? (1.86 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 652 KB / ?? (1.40 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 745 KB / ?? (1.60 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 868 KB / ?? (1.86 MB/s)\n", - "pkgs/r/noarch [] (--:--) Finalizing...\n", - "conda-forge/linux-64 [] (00m:00s) 652 KB / ?? (1.40 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 745 KB / ?? (1.60 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 868 KB / ?? (1.86 MB/s)\n", - "pkgs/r/noarch [] (--:--) Done\n", - "conda-forge/linux-64 [] (00m:00s) 652 KB / ?? (1.40 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 745 KB / ?? (1.60 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 868 KB / ?? (1.86 MB/s)\n", - "pkgs/r/noarch [] (00m:00s) Done\n", - "pkgs/r/noarch [] (00m:00s) Done\n", - "conda-forge/linux-64 [] (00m:00s) 652 KB / ?? (1.40 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 745 KB / ?? (1.60 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 868 KB / ?? (1.86 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 652 KB / ?? (1.40 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 745 KB / ?? (1.60 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 868 KB / ?? (1.86 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 1 MB / ?? (2.13 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 745 KB / ?? (1.60 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 868 KB / ?? (1.86 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 1 MB / ?? (2.13 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 745 KB / ?? (1.60 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 868 KB / ?? (1.86 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 1 MB / ?? (2.13 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 2 MB / ?? (2.78 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 868 KB / ?? (1.86 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 1 MB / ?? (2.13 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 2 MB / ?? (2.78 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 868 KB / ?? (1.86 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 1 MB / ?? (2.13 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 2 MB / ?? (2.78 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 2 MB / ?? (2.79 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 1 MB / ?? (2.13 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 2 MB / ?? (2.78 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 2 MB / ?? (2.79 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 2 MB / ?? (2.65 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 2 MB / ?? (2.78 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 2 MB / ?? (2.79 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 2 MB / ?? (2.65 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 2 MB / ?? (2.78 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 2 MB / ?? (2.79 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 2 MB / ?? (2.65 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 2 MB / ?? (2.78 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 2 MB / ?? (3.16 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 2 MB / ?? (2.65 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 2 MB / ?? (2.78 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 2 MB / ?? (3.16 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 2 MB / ?? (2.65 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 2 MB / ?? (3.19 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 2 MB / ?? (3.16 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 2 MB / ?? (2.65 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 2 MB / ?? (3.19 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 2 MB / ?? (3.16 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 3 MB / ?? (2.90 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 2 MB / ?? (3.19 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 2 MB / ?? (3.16 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 3 MB / ?? (2.90 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 2 MB / ?? (3.19 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 2 MB / ?? (3.16 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 3 MB / ?? (2.90 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 2 MB / ?? (3.19 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 3 MB / ?? (3.42 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 3 MB / ?? (2.90 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 2 MB / ?? (3.19 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 3 MB / ?? (3.42 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 3 MB / ?? (2.90 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 3 MB / ?? (3.45 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 3 MB / ?? (3.42 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 3 MB / ?? (2.90 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 3 MB / ?? (3.45 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 3 MB / ?? (3.42 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 3 MB / ?? (3.07 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 3 MB / ?? (3.45 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 3 MB / ?? (3.42 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 3 MB / ?? (3.07 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 3 MB / ?? (3.45 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 3 MB / ?? (3.42 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 3 MB / ?? (3.07 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 3 MB / ?? (3.45 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 4 MB / ?? (3.63 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 3 MB / ?? (3.07 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 3 MB / ?? (3.45 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 4 MB / ?? (3.63 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 3 MB / ?? (3.07 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 4 MB / ?? (3.56 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) 4 MB / ?? (3.63 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 3 MB / ?? (3.07 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 4 MB / ?? (3.56 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) Finalizing...\n", - "conda-forge/linux-64 [] (00m:00s) 3 MB / ?? (3.07 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 4 MB / ?? (3.56 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) Done\n", - "conda-forge/linux-64 [] (00m:00s) 3 MB / ?? (3.07 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 4 MB / ?? (3.56 MB/s)\n", - "pkgs/main/linux-64 [] (00m:00s) Done\n", - "pkgs/main/linux-64 [] (00m:00s) Done\n", - "conda-forge/linux-64 [] (00m:00s) 3 MB / ?? (3.07 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 4 MB / ?? (3.56 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 3 MB / ?? (3.07 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 4 MB / ?? (3.56 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 4 MB / ?? (3.09 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 4 MB / ?? (3.56 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 4 MB / ?? (3.09 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 4 MB / ?? (3.56 MB/s)\n", - "conda-forge/linux-64 [] (00m:00s) 4 MB / ?? (3.09 MB/s)\n", - "conda-forge/noarch [] (00m:00s) 4 MB / ?? (3.35 MB/s)\n", - "conda-forge/linux-64 [] (00m:01s) 4 MB / ?? (3.09 MB/s)\n", - "conda-forge/noarch [] (00m:01s) 4 MB / ?? (3.35 MB/s)\n", - "conda-forge/linux-64 [] (00m:01s) 4 MB / ?? (3.20 MB/s)\n", - "conda-forge/noarch [] (00m:01s) 4 MB / ?? (3.35 MB/s)\n", - "conda-forge/linux-64 [] (00m:01s) 4 MB / ?? (3.20 MB/s)\n", - "conda-forge/noarch [] (00m:01s) 4 MB / ?? (3.35 MB/s)\n", - "conda-forge/linux-64 [] (00m:01s) 4 MB / ?? (3.20 MB/s)\n", - "conda-forge/noarch [] (00m:01s) 4 MB / ?? (3.14 MB/s)\n", - "conda-forge/linux-64 [] (00m:01s) 4 MB / ?? (3.20 MB/s)\n", - "conda-forge/noarch [] (00m:01s) 4 MB / ?? (3.14 MB/s)\n", - "conda-forge/linux-64 [] (00m:01s) 5 MB / ?? (3.24 MB/s)\n", - "conda-forge/noarch [] (00m:01s) 4 MB / ?? (3.14 MB/s)\n", - "conda-forge/linux-64 [] (00m:01s) 5 MB / ?? (3.24 MB/s)\n", - "conda-forge/noarch [] (00m:01s) 4 MB / ?? (3.14 MB/s)\n", - "conda-forge/linux-64 [] (00m:01s) 5 MB / ?? (3.24 MB/s)\n", - "conda-forge/noarch [] (00m:01s) 5 MB / ?? (3.15 MB/s)\n", - "conda-forge/linux-64 [] (00m:01s) 5 MB / ?? (3.24 MB/s)\n", - "conda-forge/noarch [] (00m:01s) 5 MB / ?? (3.15 MB/s)\n", - "conda-forge/linux-64 [] (00m:01s) 6 MB / ?? (3.32 MB/s)\n", - "conda-forge/noarch [] (00m:01s) 5 MB / ?? (3.15 MB/s)\n", - "conda-forge/linux-64 [] (00m:01s) 6 MB / ?? (3.32 MB/s)\n", - "conda-forge/noarch [] (00m:01s) 5 MB / ?? (3.15 MB/s)\n", - "conda-forge/linux-64 [] (00m:01s) 6 MB / ?? (3.32 MB/s)\n", - "conda-forge/noarch [] (00m:01s) 5 MB / ?? (3.21 MB/s)\n", - "conda-forge/linux-64 [] (00m:01s) 6 MB / ?? (3.32 MB/s)\n", - "conda-forge/noarch [] (00m:01s) 5 MB / ?? (3.21 MB/s)\n", - "conda-forge/linux-64 [] (00m:01s) 6 MB / ?? (3.43 MB/s)\n", - "conda-forge/noarch [] (00m:01s) 5 MB / ?? (3.21 MB/s)\n", - "conda-forge/linux-64 [] (00m:01s) 6 MB / ?? (3.43 MB/s)\n", - "conda-forge/noarch [] (00m:01s) 5 MB / ?? (3.21 MB/s)\n", - "conda-forge/linux-64 [] (00m:01s) 6 MB / ?? (3.43 MB/s)\n", - "conda-forge/noarch [] (00m:01s) 6 MB / ?? (3.32 MB/s)\n", - "conda-forge/linux-64 [] (00m:01s) 6 MB / ?? (3.43 MB/s)\n", - "conda-forge/noarch [] (00m:01s) Finalizing...\n", - "conda-forge/linux-64 [] (00m:01s) 6 MB / ?? (3.43 MB/s)\n", - "conda-forge/noarch [] (00m:01s) Done\n", - "conda-forge/linux-64 [] (00m:01s) 6 MB / ?? (3.43 MB/s)\n", - "conda-forge/noarch [] (00m:01s) Done\n", - "conda-forge/noarch [] (00m:01s) Done\n", - "conda-forge/linux-64 [] (00m:01s) 6 MB / ?? (3.43 MB/s)\n", - "conda-forge/linux-64 [] (00m:01s) 6 MB / ?? (3.43 MB/s)\n", - "conda-forge/linux-64 [] (00m:01s) 7 MB / ?? (3.36 MB/s)\n", - "conda-forge/linux-64 [] (00m:01s) 7 MB / ?? (3.36 MB/s)\n", - "conda-forge/linux-64 [] (00m:01s) 8 MB / ?? (3.64 MB/s)\n", - "conda-forge/linux-64 [] (00m:02s) 8 MB / ?? (3.64 MB/s)\n", - "conda-forge/linux-64 [] (00m:02s) 9 MB / ?? (3.71 MB/s)\n", - "conda-forge/linux-64 [] (00m:02s) 9 MB / ?? (3.71 MB/s)\n", - "conda-forge/linux-64 [] (00m:02s) 9 MB / ?? (3.78 MB/s)\n", - "conda-forge/linux-64 [] (00m:02s) 9 MB / ?? (3.78 MB/s)\n", - "conda-forge/linux-64 [] (00m:02s) 10 MB / ?? (3.82 MB/s)\n", - "conda-forge/linux-64 [] (00m:02s) 10 MB / ?? (3.82 MB/s)\n", - "conda-forge/linux-64 [] (00m:02s) 11 MB / ?? (3.85 MB/s)\n", - "conda-forge/linux-64 [] (00m:02s) 11 MB / ?? (3.85 MB/s)\n", - "conda-forge/linux-64 [] (00m:02s) 11 MB / ?? (3.88 MB/s)\n", - "conda-forge/linux-64 [] (00m:02s) 11 MB / ?? (3.88 MB/s)\n", - "conda-forge/linux-64 [] (00m:02s) 12 MB / ?? (3.94 MB/s)\n", - "conda-forge/linux-64 [] (00m:02s) 12 MB / ?? (3.94 MB/s)\n", - "conda-forge/linux-64 [] (00m:02s) 13 MB / ?? (3.99 MB/s)\n", - "conda-forge/linux-64 [] (00m:03s) 13 MB / ?? (3.99 MB/s)\n", - "conda-forge/linux-64 [] (00m:03s) 14 MB / ?? (3.99 MB/s)\n", - "conda-forge/linux-64 [] (00m:03s) 14 MB / ?? (3.99 MB/s)\n", - "conda-forge/linux-64 [] (00m:03s) 14 MB / ?? (4.02 MB/s)\n", - "conda-forge/linux-64 [] (00m:03s) 14 MB / ?? (4.02 MB/s)\n", - "conda-forge/linux-64 [] (00m:03s) 15 MB / ?? (4.03 MB/s)\n", - "conda-forge/linux-64 [] (00m:03s) 15 MB / ?? (4.03 MB/s)\n", - "conda-forge/linux-64 [] (00m:03s) 16 MB / ?? (4.06 MB/s)\n", - "conda-forge/linux-64 [] (00m:03s) 16 MB / ?? (4.06 MB/s)\n", - "conda-forge/linux-64 [] (00m:03s) 16 MB / ?? (4.06 MB/s)\n", - "conda-forge/linux-64 [] (00m:03s) 16 MB / ?? (4.06 MB/s)\n", - "conda-forge/linux-64 [] (00m:03s) 17 MB / ?? (4.06 MB/s)\n", - "conda-forge/linux-64 [] (00m:04s) 17 MB / ?? (4.06 MB/s)\n", - "conda-forge/linux-64 [] (00m:04s) 18 MB / ?? (4.09 MB/s)\n", - "conda-forge/linux-64 [] (00m:04s) 18 MB / ?? (4.09 MB/s)\n", - "conda-forge/linux-64 [] (00m:04s) 18 MB / ?? (4.11 MB/s)\n", - "conda-forge/linux-64 [] (00m:04s) 18 MB / ?? (4.11 MB/s)\n", - "conda-forge/linux-64 [] (00m:04s) 19 MB / ?? (4.14 MB/s)\n", - "conda-forge/linux-64 [] (00m:04s) Finalizing...\n", - "conda-forge/linux-64 [] (00m:04s) Done\n", - "conda-forge/linux-64 [] (00m:04s) Done\n", - "conda-forge/linux-64 [] (00m:04s) Done\n", - "Transaction\n", - "\n", - " Prefix: /usr/local\n", - "\n", - " Updating specs:\n", - "\n", - " - gcc\n", - "\n", - "\n", - " Package Version Build Channel Size\n", - "──────────────────────────────────────────────────────────────────────────────────────────────\n", - " Install:\n", - "──────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n", - "\u001b[32m binutils_impl_linux-64 \u001b[00m 2.36.1 h193b22a_2 conda-forge/linux-64 10 MB\n", - "\u001b[32m gcc \u001b[00m 11.2.0 h702ea55_3 conda-forge/linux-64 24 KB\n", - "\u001b[32m gcc_impl_linux-64 \u001b[00m 11.2.0 h82a94d6_11 conda-forge/linux-64 52 MB\n", - "\u001b[32m kernel-headers_linux-64\u001b[00m 2.6.32 he073ed8_15 conda-forge/noarch 707 KB\n", - "\u001b[32m libgcc-devel_linux-64 \u001b[00m 11.2.0 h0952999_11 conda-forge/linux-64 3 MB\n", - "\u001b[32m libnsl \u001b[00m 2.0.0 h7f98852_0 conda-forge/linux-64 31 KB\n", - "\u001b[32m libsanitizer \u001b[00m 11.2.0 he4da1e4_11 conda-forge/linux-64 6 MB\n", - "\u001b[32m libzlib \u001b[00m 1.2.11 h36c2ea0_1013 conda-forge/linux-64 59 KB\n", - "\u001b[32m sysroot_linux-64 \u001b[00m 2.12 he073ed8_15 conda-forge/noarch 31 MB\n", - "\n", - " Change:\n", - "──────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n", - "\u001b[31m libnghttp2 \u001b[00m 1.43.0 h812cca2_0 installed \n", - "\u001b[32m libnghttp2 \u001b[00m 1.43.0 ha19adfc_1 conda-forge/linux-64 790 KB\n", - "\u001b[31m python \u001b[00m 3.7.10 hffdb5ce_100_cpython installed \n", - "\u001b[32m python \u001b[00m 3.7.10 hf930737_104_cpython conda-forge/linux-64 57 MB\n", - "\u001b[31m zlib \u001b[00m 1.2.11 h516909a_1010 installed \n", - "\u001b[32m zlib \u001b[00m 1.2.11 h36c2ea0_1013 conda-forge/linux-64 86 KB\n", - "\n", - " Upgrade:\n", - "──────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n", - "\u001b[31m c-ares \u001b[00m 1.17.1 h7f98852_1 installed \n", - "\u001b[32m c-ares \u001b[00m 1.18.1 h7f98852_0 conda-forge/linux-64 113 KB\n", - "\u001b[31m ca-certificates \u001b[00m 2020.12.5 ha878542_0 installed \n", - "\u001b[32m ca-certificates \u001b[00m 2021.10.26 h06a4308_2 pkgs/main/linux-64 115 KB\n", - "\u001b[31m certifi \u001b[00m 2020.12.5 py37h89c1867_1 installed \n", - "\u001b[32m certifi \u001b[00m 2021.10.8 py37h89c1867_1 conda-forge/linux-64 145 KB\n", - "\u001b[31m cffi \u001b[00m 1.14.5 py37hc58025e_0 installed \n", - "\u001b[32m cffi \u001b[00m 1.15.0 py37h036bc23_0 conda-forge/linux-64 225 KB\n", - "\u001b[31m cryptography \u001b[00m 3.4.5 py37h5d9358c_1 installed \n", - "\u001b[32m cryptography \u001b[00m 36.0.0 py37h9ce1e76_0 pkgs/main/linux-64 1 MB\n", - "\u001b[31m krb5 \u001b[00m 1.17.2 h926e7f8_0 installed \n", - "\u001b[32m krb5 \u001b[00m 1.19.2 h48eae69_3 conda-forge/linux-64 1 MB\n", - "\u001b[31m ld_impl_linux-64 \u001b[00m 2.35.1 hea4e1c9_2 installed \n", - "\u001b[32m ld_impl_linux-64 \u001b[00m 2.36.1 hea4e1c9_2 conda-forge/linux-64 667 KB\n", - "\u001b[31m libarchive \u001b[00m 3.5.1 h3f442fb_1 installed \n", - "\u001b[32m libarchive \u001b[00m 3.5.2 hed592e5_1 conda-forge/linux-64 2 MB\n", - "\u001b[31m libcurl \u001b[00m 7.75.0 hc4aaa36_0 installed \n", - "\u001b[32m libcurl \u001b[00m 7.80.0 h494985f_1 conda-forge/linux-64 339 KB\n", - "\u001b[31m libffi \u001b[00m 3.3 h58526e2_2 installed \n", - "\u001b[32m libffi \u001b[00m 3.4.2 h7f98852_5 conda-forge/linux-64 57 KB\n", - "\u001b[31m libgcc-ng \u001b[00m 9.3.0 h2828fa1_18 installed \n", - "\u001b[32m libgcc-ng \u001b[00m 11.2.0 h1d223b6_11 conda-forge/linux-64 887 KB\n", - "\u001b[31m libgomp \u001b[00m 9.3.0 h2828fa1_18 installed \n", - "\u001b[32m libgomp \u001b[00m 11.2.0 h1d223b6_11 conda-forge/linux-64 427 KB\n", - "\u001b[31m libssh2 \u001b[00m 1.9.0 ha56f1ee_6 installed \n", - "\u001b[32m libssh2 \u001b[00m 1.10.0 ha35d2d1_2 conda-forge/linux-64 233 KB\n", - "\u001b[31m libstdcxx-ng \u001b[00m 9.3.0 h6de172a_18 installed \n", - "\u001b[32m libstdcxx-ng \u001b[00m 11.2.0 he4da1e4_11 conda-forge/linux-64 4 MB\n", - "\u001b[31m libxml2 \u001b[00m 2.9.10 h72842e0_3 installed \n", - "\u001b[32m libxml2 \u001b[00m 2.9.12 h72842e0_0 conda-forge/linux-64 772 KB\n", - "\u001b[31m openssl \u001b[00m 1.1.1j h7f98852_0 installed \n", - "\u001b[32m openssl \u001b[00m 3.0.0 h7f98852_2 conda-forge/linux-64 3 MB\n", - "\u001b[31m readline \u001b[00m 8.0 he28a2e2_2 installed \n", - "\u001b[32m readline \u001b[00m 8.1 h46c0cb4_0 conda-forge/linux-64 295 KB\n", - "\u001b[31m sqlite \u001b[00m 3.34.0 h74cdb3f_0 installed \n", - "\u001b[32m sqlite \u001b[00m 3.37.0 h9cd32fc_0 conda-forge/linux-64 1 MB\n", - "\u001b[31m tk \u001b[00m 8.6.10 h21135ba_1 installed \n", - "\u001b[32m tk \u001b[00m 8.6.11 h27826a3_1 conda-forge/linux-64 3 MB\n", - "\u001b[31m zstd \u001b[00m 1.4.9 ha95c52a_0 installed \n", - "\u001b[32m zstd \u001b[00m 1.5.1 ha95c52a_0 conda-forge/linux-64 463 KB\n", - "\n", - " Downgrade:\n", - "──────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n", - "\u001b[31m mamba \u001b[00m 0.8.0 py37h7f483ca_0 installed \n", - "\u001b[32m mamba \u001b[00m 0.1.2 py37h99015e2_0 conda-forge/linux-64 104 KB\n", - "\n", - " Summary:\n", - "\n", - " Install: 9 packages\n", - " Change: 3 packages\n", - " Upgrade: 20 packages\n", - " Downgrade: 1 packages\n", - "\n", - " Total download: 183 MB\n", - "\n", - "──────────────────────────────────────────────────────────────────────────────────────────────\n", - "\n", - "mamba [] (00m:00s) \n", - "mamba [] (00m:00s) 415 B / 104 KB ( 1.27 KB/s)\n", - "mamba [] (00m:00s) 415 B / 104 KB ( 1.27 KB/s)\n", - "libnghttp2 [] (00m:00s) \n", - "mamba [] (00m:00s) 415 B / 104 KB ( 1.27 KB/s)\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "mamba [] (00m:00s) 415 B / 104 KB ( 1.27 KB/s)\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) \n", - "mamba [] (00m:00s) 415 B / 104 KB ( 1.27 KB/s)\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "mamba [] (00m:00s) 415 B / 104 KB ( 1.27 KB/s)\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "zlib [] (00m:00s) \n", - "mamba [] (00m:00s) 415 B / 104 KB ( 1.27 KB/s)\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "zlib [] (00m:00s) 416 B / 86 KB ( 1.27 KB/s)\n", - "mamba [] (00m:00s) 415 B / 104 KB ( 1.27 KB/s)\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "zlib [] (00m:00s) 416 B / 86 KB ( 1.27 KB/s)\n", - "c-ares [] (00m:00s) \n", - "mamba [] (00m:00s) 415 B / 104 KB ( 1.27 KB/s)\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "zlib [] (00m:00s) 416 B / 86 KB ( 1.27 KB/s)\n", - "c-ares [] (00m:00s) 415 B / 113 KB ( 1.27 KB/s)\n", - "mamba [] (00m:00s) 415 B / 104 KB ( 1.27 KB/s)\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "zlib [] (00m:00s) 416 B / 86 KB ( 1.27 KB/s)\n", - "c-ares [] (00m:00s) 415 B / 113 KB ( 1.27 KB/s)\n", - "mamba [] (00m:00s) 415 B / 104 KB ( 1.27 KB/s)\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "zlib [] (00m:00s) Validating...\n", - "c-ares [] (00m:00s) 415 B / 113 KB ( 1.27 KB/s)\n", - "mamba [] (00m:00s) 415 B / 104 KB ( 1.27 KB/s)\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "zlib [] (00m:00s) Validating...\n", - "c-ares [] (00m:00s) 415 B / 113 KB ( 1.27 KB/s)\n", - "mamba [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "zlib [] (00m:00s) Validating...\n", - "c-ares [] (00m:00s) 415 B / 113 KB ( 1.27 KB/s)\n", - "mamba [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "zlib [] (00m:00s) Waiting...\n", - "c-ares [] (00m:00s) 415 B / 113 KB ( 1.27 KB/s)\n", - "mamba [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "zlib [] (00m:00s) Decompressing...\n", - "c-ares [] (00m:00s) 415 B / 113 KB ( 1.27 KB/s)\n", - "mamba [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "zlib [] (00m:00s) Decompressing...\n", - "c-ares [] (00m:00s) 415 B / 113 KB ( 1.27 KB/s)\n", - "mamba [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "zlib [] (00m:00s) Decompressing...\n", - "c-ares [] (00m:00s) 415 B / 113 KB ( 1.27 KB/s)\n", - "mamba [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "zlib [] (00m:00s) Decompressing...\n", - "c-ares [] (00m:00s) Validating...\n", - "mamba [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "zlib [] (00m:00s) Decompressing...\n", - "c-ares [] (00m:00s) Waiting...\n", - "mamba [] (00m:00s) Decompressing...\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "zlib [] (00m:00s) Decompressing...\n", - "c-ares [] (00m:00s) Waiting...\n", - "\u001b[5A\u001b[0KFinished zlib (00m:00s) 86 KB 267 KB/s\n", - "mamba [] (00m:00s) Decompressing...\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "c-ares [] (00m:00s) Waiting...\n", - "mamba [] (00m:00s) Decompressing...\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "c-ares [] (00m:00s) Decompressing...\n", - "\u001b[4A\u001b[0KFinished mamba (00m:00s) 104 KB 320 KB/s\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "c-ares [] (00m:00s) Decompressing...\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "c-ares [] (00m:00s) Decompressing...\n", - "ld_impl_linux-64 [] (00m:00s) \n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "c-ares [] (00m:00s) Decompressing...\n", - "ld_impl_linux-64 [] (00m:00s) Validating...\n", - "\u001b[4A\u001b[0KFinished c-ares (00m:00s) 113 KB 337 KB/s\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Validating...\n", - "libgcc-ng [] (00m:00s) \n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Validating...\n", - "libgcc-ng [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Waiting...\n", - "libgcc-ng [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Validating...\n", - "libcurl [] (00m:00s) \n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Validating...\n", - "libcurl [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) 415 B / 790 KB ( 1.27 KB/s)\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Validating...\n", - "libcurl [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Validating...\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Validating...\n", - "libcurl [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Validating...\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Validating...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:00s) Validating...\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) \n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 411 B / 57 MB ( 1.26 KB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 9 MB / 57 MB ( 18.82 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 9 MB / 57 MB ( 18.82 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 9 MB / 57 MB ( 18.82 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) \n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 9 MB / 57 MB ( 18.82 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 9 MB / 57 MB ( 18.82 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 9 MB / 57 MB ( 18.82 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) \n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 9 MB / 57 MB ( 18.82 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 9 MB / 57 MB ( 18.82 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 9 MB / 57 MB ( 18.82 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) \n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 9 MB / 57 MB ( 18.82 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) 2 MB / 3 MB ( 3.23 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 9 MB / 57 MB ( 18.82 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) 2 MB / 3 MB ( 3.23 MB/s)\n", - "binutils_impl_linux-64 [] (00m:00s) \n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 9 MB / 57 MB ( 18.82 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) 2 MB / 3 MB ( 3.23 MB/s)\n", - "binutils_impl_linux-64 [] (00m:00s) 5 MB / 10 MB ( 7.43 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 9 MB / 57 MB ( 18.82 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) 2 MB / 3 MB ( 3.23 MB/s)\n", - "binutils_impl_linux-64 [] (00m:00s) 5 MB / 10 MB ( 7.43 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 16 MB / 57 MB ( 25.35 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) 2 MB / 3 MB ( 3.23 MB/s)\n", - "binutils_impl_linux-64 [] (00m:00s) 5 MB / 10 MB ( 7.43 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 16 MB / 57 MB ( 25.35 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) 2 MB / 3 MB ( 3.23 MB/s)\n", - "binutils_impl_linux-64 [] (00m:00s) 5 MB / 10 MB ( 7.43 MB/s)\n", - "gcc_impl_linux-64 [] (00m:00s) \n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 16 MB / 57 MB ( 25.35 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) 2 MB / 3 MB ( 3.23 MB/s)\n", - "binutils_impl_linux-64 [] (00m:00s) 5 MB / 10 MB ( 7.43 MB/s)\n", - "gcc_impl_linux-64 [] (00m:00s) 4 MB / 52 MB ( 7.13 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 16 MB / 57 MB ( 25.35 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) 2 MB / 3 MB ( 3.23 MB/s)\n", - "binutils_impl_linux-64 [] (00m:00s) 5 MB / 10 MB ( 7.43 MB/s)\n", - "gcc_impl_linux-64 [] (00m:00s) 4 MB / 52 MB ( 7.13 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 16 MB / 57 MB ( 25.35 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Validating...\n", - "binutils_impl_linux-64 [] (00m:00s) 5 MB / 10 MB ( 7.43 MB/s)\n", - "gcc_impl_linux-64 [] (00m:00s) 4 MB / 52 MB ( 7.13 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 16 MB / 57 MB ( 25.35 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Validating...\n", - "binutils_impl_linux-64 [] (00m:00s) 5 MB / 10 MB ( 7.43 MB/s)\n", - "gcc_impl_linux-64 [] (00m:00s) 4 MB / 52 MB ( 7.13 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) \n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 16 MB / 57 MB ( 25.35 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Validating...\n", - "binutils_impl_linux-64 [] (00m:00s) 5 MB / 10 MB ( 7.43 MB/s)\n", - "gcc_impl_linux-64 [] (00m:00s) 4 MB / 52 MB ( 7.13 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) 2 MB / 3 MB ( 2.36 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 16 MB / 57 MB ( 25.35 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) 5 MB / 10 MB ( 7.43 MB/s)\n", - "gcc_impl_linux-64 [] (00m:00s) 4 MB / 52 MB ( 7.13 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) 2 MB / 3 MB ( 2.36 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 16 MB / 57 MB ( 25.35 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) 5 MB / 10 MB ( 7.43 MB/s)\n", - "gcc_impl_linux-64 [] (00m:00s) 4 MB / 52 MB ( 7.13 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) 2 MB / 3 MB ( 2.36 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 16 MB / 57 MB ( 25.35 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) 5 MB / 10 MB ( 7.43 MB/s)\n", - "gcc_impl_linux-64 [] (00m:00s) 4 MB / 52 MB ( 7.13 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 16 MB / 57 MB ( 25.35 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) 5 MB / 10 MB ( 7.43 MB/s)\n", - "gcc_impl_linux-64 [] (00m:00s) 4 MB / 52 MB ( 7.13 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 16 MB / 57 MB ( 25.35 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 4 MB / 52 MB ( 7.13 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 16 MB / 57 MB ( 25.35 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 4 MB / 52 MB ( 7.13 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 22 MB / 57 MB ( 28.94 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 4 MB / 52 MB ( 7.13 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 22 MB / 57 MB ( 28.94 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 4 MB / 52 MB ( 7.13 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Validating...\n", - "certifi [] (00m:00s) \n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 22 MB / 57 MB ( 28.94 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 4 MB / 52 MB ( 7.13 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Validating...\n", - "certifi [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 22 MB / 57 MB ( 28.94 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 4 MB / 52 MB ( 7.13 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Validating...\n", - "certifi [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 22 MB / 57 MB ( 28.94 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 11 MB / 52 MB ( 13.74 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Validating...\n", - "certifi [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 22 MB / 57 MB ( 28.94 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 11 MB / 52 MB ( 13.74 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 22 MB / 57 MB ( 28.94 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 11 MB / 52 MB ( 13.74 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Validating...\n", - "libsanitizer [] (00m:00s) \n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 22 MB / 57 MB ( 28.94 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 11 MB / 52 MB ( 13.74 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Validating...\n", - "libsanitizer [] (00m:00s) 4 MB / 6 MB ( 4.80 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 22 MB / 57 MB ( 28.94 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 11 MB / 52 MB ( 13.74 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) 4 MB / 6 MB ( 4.80 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 22 MB / 57 MB ( 28.94 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 11 MB / 52 MB ( 13.74 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) 4 MB / 6 MB ( 4.80 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 22 MB / 57 MB ( 28.94 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 11 MB / 52 MB ( 13.74 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 22 MB / 57 MB ( 28.94 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 11 MB / 52 MB ( 13.74 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Validating...\n", - "sysroot_linux-64 [] (00m:00s) \n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 22 MB / 57 MB ( 28.94 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 11 MB / 52 MB ( 13.74 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Validating...\n", - "sysroot_linux-64 [] (00m:00s) 741 KB / 31 MB (837.31 KB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 22 MB / 57 MB ( 28.94 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 11 MB / 52 MB ( 13.74 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Validating...\n", - "sysroot_linux-64 [] (00m:00s) 741 KB / 31 MB (837.31 KB/s)\n", - "libffi [] (00m:00s) \n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 22 MB / 57 MB ( 28.94 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 11 MB / 52 MB ( 13.74 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Validating...\n", - "sysroot_linux-64 [] (00m:00s) 741 KB / 31 MB (837.31 KB/s)\n", - "libffi [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 22 MB / 57 MB ( 28.94 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 11 MB / 52 MB ( 13.74 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Validating...\n", - "sysroot_linux-64 [] (00m:00s) 741 KB / 31 MB (837.31 KB/s)\n", - "libffi [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 27 MB / 57 MB ( 29.34 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 11 MB / 52 MB ( 13.74 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Validating...\n", - "sysroot_linux-64 [] (00m:00s) 741 KB / 31 MB (837.31 KB/s)\n", - "libffi [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 27 MB / 57 MB ( 29.34 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 11 MB / 52 MB ( 13.74 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Validating...\n", - "sysroot_linux-64 [] (00m:00s) 741 KB / 31 MB (837.31 KB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 27 MB / 57 MB ( 29.34 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 11 MB / 52 MB ( 13.74 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Validating...\n", - "sysroot_linux-64 [] (00m:00s) 741 KB / 31 MB (837.31 KB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) \n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 27 MB / 57 MB ( 29.34 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 11 MB / 52 MB ( 13.74 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Validating...\n", - "sysroot_linux-64 [] (00m:00s) 741 KB / 31 MB (837.31 KB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) 67 KB / 1 MB ( 71.35 KB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 27 MB / 57 MB ( 29.34 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 11 MB / 52 MB ( 13.74 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Validating...\n", - "sysroot_linux-64 [] (00m:00s) 741 KB / 31 MB (837.31 KB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) 67 KB / 1 MB ( 71.35 KB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 27 MB / 57 MB ( 29.34 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Waiting...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 15 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Validating...\n", - "sysroot_linux-64 [] (00m:00s) 741 KB / 31 MB (837.31 KB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) 67 KB / 1 MB ( 71.35 KB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 27 MB / 57 MB ( 29.34 MB/s)\n", - "ld_impl_linux-64 [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 15 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Validating...\n", - "sysroot_linux-64 [] (00m:00s) 741 KB / 31 MB (837.31 KB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) 67 KB / 1 MB ( 71.35 KB/s)\n", - "\u001b[17A\u001b[0KFinished ld_impl_linux-64 (00m:00s) 667 KB 2 MB/s\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 27 MB / 57 MB ( 29.34 MB/s)\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 15 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Validating...\n", - "sysroot_linux-64 [] (00m:00s) 741 KB / 31 MB (837.31 KB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) 67 KB / 1 MB ( 71.35 KB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 27 MB / 57 MB ( 29.34 MB/s)\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 15 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Validating...\n", - "sysroot_linux-64 [] (00m:00s) 741 KB / 31 MB (837.31 KB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) 67 KB / 1 MB ( 71.35 KB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 27 MB / 57 MB ( 29.34 MB/s)\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 15 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Validating...\n", - "sysroot_linux-64 [] (00m:00s) 2 MB / 31 MB ( 2.22 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) 67 KB / 1 MB ( 71.35 KB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 27 MB / 57 MB ( 29.34 MB/s)\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 15 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 2 MB / 31 MB ( 2.22 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) 67 KB / 1 MB ( 71.35 KB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 27 MB / 57 MB ( 29.34 MB/s)\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 15 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 2 MB / 31 MB ( 2.22 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) 67 KB / 1 MB ( 71.35 KB/s)\n", - "libstdcxx-ng [] (00m:00s) \n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 27 MB / 57 MB ( 29.34 MB/s)\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 15 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 2 MB / 31 MB ( 2.22 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) 67 KB / 1 MB ( 71.35 KB/s)\n", - "libstdcxx-ng [] (00m:00s) 1 MB / 4 MB ( 1.25 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 27 MB / 57 MB ( 29.34 MB/s)\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 15 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 2 MB / 31 MB ( 2.22 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) 67 KB / 1 MB ( 71.35 KB/s)\n", - "libstdcxx-ng [] (00m:00s) 1 MB / 4 MB ( 1.25 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 29 MB / 57 MB ( 26.87 MB/s)\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Validating...\n", - "gcc_impl_linux-64 [] (00m:00s) 15 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 2 MB / 31 MB ( 2.22 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) 67 KB / 1 MB ( 71.35 KB/s)\n", - "libstdcxx-ng [] (00m:00s) 1 MB / 4 MB ( 1.25 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 29 MB / 57 MB ( 26.87 MB/s)\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 15 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 2 MB / 31 MB ( 2.22 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) 67 KB / 1 MB ( 71.35 KB/s)\n", - "libstdcxx-ng [] (00m:00s) 1 MB / 4 MB ( 1.25 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 29 MB / 57 MB ( 26.87 MB/s)\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 15 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 2 MB / 31 MB ( 2.22 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) 67 KB / 1 MB ( 71.35 KB/s)\n", - "libstdcxx-ng [] (00m:00s) 1 MB / 4 MB ( 1.25 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 29 MB / 57 MB ( 26.87 MB/s)\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 15 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 2 MB / 31 MB ( 2.22 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) 1 MB / 1 MB ( 1.07 MB/s)\n", - "libstdcxx-ng [] (00m:00s) 1 MB / 4 MB ( 1.25 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 29 MB / 57 MB ( 26.87 MB/s)\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 15 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 2 MB / 31 MB ( 2.22 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) 1 MB / 1 MB ( 1.07 MB/s)\n", - "libstdcxx-ng [] (00m:00s) 1 MB / 4 MB ( 1.25 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 29 MB / 57 MB ( 26.87 MB/s)\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 18 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 2 MB / 31 MB ( 2.22 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) 1 MB / 1 MB ( 1.07 MB/s)\n", - "libstdcxx-ng [] (00m:00s) 1 MB / 4 MB ( 1.25 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 29 MB / 57 MB ( 26.87 MB/s)\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 18 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 2 MB / 31 MB ( 2.22 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) 1 MB / 1 MB ( 1.07 MB/s)\n", - "libstdcxx-ng [] (00m:00s) 1 MB / 4 MB ( 1.25 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 29 MB / 57 MB ( 26.87 MB/s)\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 18 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 2 MB / 31 MB ( 2.22 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Validating...\n", - "libstdcxx-ng [] (00m:00s) 1 MB / 4 MB ( 1.25 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 29 MB / 57 MB ( 26.87 MB/s)\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 18 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 2 MB / 31 MB ( 2.22 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) 1 MB / 4 MB ( 1.25 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 29 MB / 57 MB ( 26.87 MB/s)\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 18 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 2 MB / 31 MB ( 2.22 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) 1 MB / 4 MB ( 1.25 MB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 29 MB / 57 MB ( 26.87 MB/s)\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 18 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 2 MB / 31 MB ( 2.22 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 29 MB / 57 MB ( 26.87 MB/s)\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 18 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 2 MB / 31 MB ( 2.22 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 29 MB / 57 MB ( 26.87 MB/s)\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libcurl [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 18 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 7 MB / 31 MB ( 5.60 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 29 MB / 57 MB ( 26.87 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libcurl [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 18 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 7 MB / 31 MB ( 5.60 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Validating...\n", - "\u001b[17A\u001b[0KFinished libcurl (00m:00s) 339 KB 791 KB/s\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 29 MB / 57 MB ( 26.87 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 18 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 7 MB / 31 MB ( 5.60 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 29 MB / 57 MB ( 26.87 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 18 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 7 MB / 31 MB ( 5.60 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 33 MB / 57 MB ( 27.01 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 18 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 7 MB / 31 MB ( 5.60 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 33 MB / 57 MB ( 27.01 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 18 MB / 52 MB ( 16.30 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 7 MB / 31 MB ( 5.60 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 33 MB / 57 MB ( 27.01 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 22 MB / 52 MB ( 17.47 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 7 MB / 31 MB ( 5.60 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 33 MB / 57 MB ( 27.01 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 22 MB / 52 MB ( 17.47 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 7 MB / 31 MB ( 5.60 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Validating...\n", - "sqlite [] (00m:00s) \n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 33 MB / 57 MB ( 27.01 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 22 MB / 52 MB ( 17.47 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 7 MB / 31 MB ( 5.60 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Validating...\n", - "sqlite [] (00m:00s) 100 KB / 1 MB ( 79.72 KB/s)\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 33 MB / 57 MB ( 27.01 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 22 MB / 52 MB ( 17.47 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 7 MB / 31 MB ( 5.60 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Validating...\n", - "sqlite [] (00m:00s) 100 KB / 1 MB ( 79.72 KB/s)\n", - "kernel-headers_linux-64 [] (00m:00s) \n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 33 MB / 57 MB ( 27.01 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 22 MB / 52 MB ( 17.47 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 7 MB / 31 MB ( 5.60 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Validating...\n", - "sqlite [] (00m:00s) 100 KB / 1 MB ( 79.72 KB/s)\n", - "kernel-headers_linux-64 [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 33 MB / 57 MB ( 27.01 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 22 MB / 52 MB ( 17.47 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 7 MB / 31 MB ( 5.60 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) 100 KB / 1 MB ( 79.72 KB/s)\n", - "kernel-headers_linux-64 [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:00s) Waiting...\n", - "python [] (00m:00s) 33 MB / 57 MB ( 27.01 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 22 MB / 52 MB ( 17.47 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 7 MB / 31 MB ( 5.60 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) 100 KB / 1 MB ( 79.72 KB/s)\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 33 MB / 57 MB ( 27.01 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 22 MB / 52 MB ( 17.47 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 7 MB / 31 MB ( 5.60 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) 100 KB / 1 MB ( 79.72 KB/s)\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 33 MB / 57 MB ( 27.01 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 22 MB / 52 MB ( 17.47 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 7 MB / 31 MB ( 5.60 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Validating...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 33 MB / 57 MB ( 27.01 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 22 MB / 52 MB ( 17.47 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 7 MB / 31 MB ( 5.60 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Validating...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) \n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 33 MB / 57 MB ( 27.01 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 22 MB / 52 MB ( 17.47 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 7 MB / 31 MB ( 5.60 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Validating...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 33 MB / 57 MB ( 27.01 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 22 MB / 52 MB ( 17.47 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 7 MB / 31 MB ( 5.60 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Validating...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 33 MB / 57 MB ( 27.01 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 22 MB / 52 MB ( 17.47 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 7 MB / 31 MB ( 5.60 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Validating...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 33 MB / 57 MB ( 27.01 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 22 MB / 52 MB ( 17.47 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 11 MB / 31 MB ( 8.04 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Validating...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 33 MB / 57 MB ( 27.01 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 22 MB / 52 MB ( 17.47 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 11 MB / 31 MB ( 8.04 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Validating...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 38 MB / 57 MB ( 27.81 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 22 MB / 52 MB ( 17.47 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 11 MB / 31 MB ( 8.04 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Validating...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 38 MB / 57 MB ( 27.81 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 22 MB / 52 MB ( 17.47 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 11 MB / 31 MB ( 8.04 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Validating...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) \n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 38 MB / 57 MB ( 27.81 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 22 MB / 52 MB ( 17.47 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 11 MB / 31 MB ( 8.04 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Validating...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 38 MB / 57 MB ( 27.81 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 22 MB / 52 MB ( 17.47 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 11 MB / 31 MB ( 8.04 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 38 MB / 57 MB ( 27.81 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 22 MB / 52 MB ( 17.47 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 11 MB / 31 MB ( 8.04 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 38 MB / 57 MB ( 27.81 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 22 MB / 52 MB ( 17.47 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 11 MB / 31 MB ( 8.04 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 38 MB / 57 MB ( 27.81 MB/s)\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "libssh2 [] (00m:00s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 27 MB / 52 MB ( 19.54 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 11 MB / 31 MB ( 8.04 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 38 MB / 57 MB ( 27.81 MB/s)\n", - "libgcc-ng [] (00m:01s) Decompressing...\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 27 MB / 52 MB ( 19.54 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 11 MB / 31 MB ( 8.04 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) \n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 38 MB / 57 MB ( 27.81 MB/s)\n", - "libgcc-ng [] (00m:01s) Decompressing...\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 27 MB / 52 MB ( 19.54 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 11 MB / 31 MB ( 8.04 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 38 MB / 57 MB ( 27.81 MB/s)\n", - "libgcc-ng [] (00m:01s) Decompressing...\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 27 MB / 52 MB ( 19.54 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 11 MB / 31 MB ( 8.04 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Validating...\n", - "openssl [] (00m:00s) \n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 38 MB / 57 MB ( 27.81 MB/s)\n", - "libgcc-ng [] (00m:01s) Decompressing...\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:00s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 27 MB / 52 MB ( 19.54 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 11 MB / 31 MB ( 8.04 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Validating...\n", - "openssl [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 38 MB / 57 MB ( 27.81 MB/s)\n", - "libgcc-ng [] (00m:01s) Decompressing...\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 27 MB / 52 MB ( 19.54 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 11 MB / 31 MB ( 8.04 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 38 MB / 57 MB ( 27.81 MB/s)\n", - "libgcc-ng [] (00m:01s) Decompressing...\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 27 MB / 52 MB ( 19.54 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 11 MB / 31 MB ( 8.04 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 38 MB / 57 MB ( 27.81 MB/s)\n", - "libgcc-ng [] (00m:01s) Decompressing...\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 27 MB / 52 MB ( 19.54 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 18 MB / 31 MB ( 11.70 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 38 MB / 57 MB ( 27.81 MB/s)\n", - "libgcc-ng [] (00m:01s) Decompressing...\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 27 MB / 52 MB ( 19.54 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 18 MB / 31 MB ( 11.70 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Validating...\n", - "gcc [] (00m:00s) \n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 38 MB / 57 MB ( 27.81 MB/s)\n", - "libgcc-ng [] (00m:01s) Decompressing...\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:00s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 27 MB / 52 MB ( 19.54 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 18 MB / 31 MB ( 11.70 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Validating...\n", - "gcc [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 38 MB / 57 MB ( 27.81 MB/s)\n", - "libgcc-ng [] (00m:01s) Decompressing...\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 27 MB / 52 MB ( 19.54 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 18 MB / 31 MB ( 11.70 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Validating...\n", - "gcc [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 38 MB / 57 MB ( 27.81 MB/s)\n", - "libgcc-ng [] (00m:01s) Decompressing...\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 27 MB / 52 MB ( 19.54 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 18 MB / 31 MB ( 11.70 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 38 MB / 57 MB ( 27.81 MB/s)\n", - "libgcc-ng [] (00m:01s) Decompressing...\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 27 MB / 52 MB ( 19.54 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 18 MB / 31 MB ( 11.70 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Waiting...\n", - "python [] (00m:01s) 45 MB / 57 MB ( 29.50 MB/s)\n", - "libgcc-ng [] (00m:01s) Decompressing...\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 27 MB / 52 MB ( 19.54 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 18 MB / 31 MB ( 11.70 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 45 MB / 57 MB ( 29.50 MB/s)\n", - "libgcc-ng [] (00m:01s) Decompressing...\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 27 MB / 52 MB ( 19.54 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 18 MB / 31 MB ( 11.70 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 45 MB / 57 MB ( 29.50 MB/s)\n", - "libgcc-ng [] (00m:01s) Decompressing...\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 27 MB / 52 MB ( 19.54 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 18 MB / 31 MB ( 11.70 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 45 MB / 57 MB ( 29.50 MB/s)\n", - "libgcc-ng [] (00m:01s) Decompressing...\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 35 MB / 52 MB ( 22.56 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 18 MB / 31 MB ( 11.70 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "\u001b[23A\u001b[0KFinished libgcc-ng (00m:01s) 887 KB 2 MB/s\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 45 MB / 57 MB ( 29.50 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 35 MB / 52 MB ( 22.56 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 18 MB / 31 MB ( 11.70 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 45 MB / 57 MB ( 29.50 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 35 MB / 52 MB ( 22.56 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 18 MB / 31 MB ( 11.70 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) \n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 45 MB / 57 MB ( 29.50 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:00s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 35 MB / 52 MB ( 22.56 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 18 MB / 31 MB ( 11.70 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 45 MB / 57 MB ( 29.50 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 35 MB / 52 MB ( 22.56 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 18 MB / 31 MB ( 11.70 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 45 MB / 57 MB ( 29.50 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 35 MB / 52 MB ( 22.56 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 18 MB / 31 MB ( 11.70 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) \n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 45 MB / 57 MB ( 29.50 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:00s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 35 MB / 52 MB ( 22.56 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 18 MB / 31 MB ( 11.70 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 45 MB / 57 MB ( 29.50 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:00s) 35 MB / 52 MB ( 22.56 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 18 MB / 31 MB ( 11.70 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 45 MB / 57 MB ( 29.50 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) 35 MB / 52 MB ( 22.56 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 18 MB / 31 MB ( 11.70 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 45 MB / 57 MB ( 29.50 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) 35 MB / 52 MB ( 22.56 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 24 MB / 31 MB ( 14.51 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 45 MB / 57 MB ( 29.50 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) 35 MB / 52 MB ( 22.56 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 24 MB / 31 MB ( 14.51 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) \n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 45 MB / 57 MB ( 29.50 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) 35 MB / 52 MB ( 22.56 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 24 MB / 31 MB ( 14.51 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 45 MB / 57 MB ( 29.50 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) 35 MB / 52 MB ( 22.56 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 24 MB / 31 MB ( 14.51 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 51 MB / 57 MB ( 30.27 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) 35 MB / 52 MB ( 22.56 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 24 MB / 31 MB ( 14.51 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 51 MB / 57 MB ( 30.27 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) 35 MB / 52 MB ( 22.56 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 24 MB / 31 MB ( 14.51 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 51 MB / 57 MB ( 30.27 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) 35 MB / 52 MB ( 22.56 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 24 MB / 31 MB ( 14.51 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 51 MB / 57 MB ( 30.27 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) 40 MB / 52 MB ( 23.72 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 24 MB / 31 MB ( 14.51 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 51 MB / 57 MB ( 30.27 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) 40 MB / 52 MB ( 23.72 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 24 MB / 31 MB ( 14.51 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "zstd [] (00m:00s) \n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 51 MB / 57 MB ( 30.27 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) 40 MB / 52 MB ( 23.72 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 24 MB / 31 MB ( 14.51 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "zstd [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 51 MB / 57 MB ( 30.27 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) 40 MB / 52 MB ( 23.72 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 24 MB / 31 MB ( 14.51 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "zstd [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 51 MB / 57 MB ( 30.27 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) 40 MB / 52 MB ( 23.72 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 24 MB / 31 MB ( 14.51 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "zstd [] (00m:00s) Waiting...\n", - "libnsl [] (00m:00s) \n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 51 MB / 57 MB ( 30.27 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) 40 MB / 52 MB ( 23.72 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 24 MB / 31 MB ( 14.51 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "zstd [] (00m:00s) Waiting...\n", - "libnsl [] (00m:00s) Validating...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 51 MB / 57 MB ( 30.27 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) 40 MB / 52 MB ( 23.72 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 24 MB / 31 MB ( 14.51 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "zstd [] (00m:00s) Waiting...\n", - "libnsl [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 51 MB / 57 MB ( 30.27 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) 40 MB / 52 MB ( 23.72 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) 24 MB / 31 MB ( 14.51 MB/s)\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "zstd [] (00m:00s) Waiting...\n", - "libnsl [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 51 MB / 57 MB ( 30.27 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) 40 MB / 52 MB ( 23.72 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:00s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) Validating...\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "zstd [] (00m:00s) Waiting...\n", - "libnsl [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) 51 MB / 57 MB ( 30.27 MB/s)\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) 40 MB / 52 MB ( 23.72 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:01s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) Validating...\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "zstd [] (00m:00s) Waiting...\n", - "libnsl [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) Validating...\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) 40 MB / 52 MB ( 23.72 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:01s) Waiting...\n", - "libsanitizer [] (00m:00s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) Validating...\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "zstd [] (00m:00s) Waiting...\n", - "libnsl [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) Validating...\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) 40 MB / 52 MB ( 23.72 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:01s) Waiting...\n", - "libsanitizer [] (00m:01s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) Validating...\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "zstd [] (00m:00s) Waiting...\n", - "libnsl [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) Validating...\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) 51 MB / 52 MB ( 27.35 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:01s) Waiting...\n", - "libsanitizer [] (00m:01s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) Validating...\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "zstd [] (00m:00s) Waiting...\n", - "libnsl [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) Validating...\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) 51 MB / 52 MB ( 27.35 MB/s)\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:01s) Waiting...\n", - "libsanitizer [] (00m:01s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) Validating...\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "zstd [] (00m:00s) Waiting...\n", - "libnsl [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) Validating...\n", - "libssh2 [] (00m:01s) Waiting...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) Validating...\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:01s) Waiting...\n", - "libsanitizer [] (00m:01s) Waiting...\n", - "sysroot_linux-64 [] (00m:00s) Validating...\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "zstd [] (00m:00s) Waiting...\n", - "libnsl [] (00m:00s) Waiting...\n", - "libnghttp2 [] (00m:01s) Decompressing...\n", - "python [] (00m:01s) Validating...\n", - "libssh2 [] (00m:01s) Decompressing...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) Validating...\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:01s) Waiting...\n", - "libsanitizer [] (00m:01s) Waiting...\n", - "sysroot_linux-64 [] (00m:01s) Validating...\n", - "libffi [] (00m:00s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "zstd [] (00m:00s) Waiting...\n", - "libnsl [] (00m:00s) Waiting...\n", - "\u001b[27A\u001b[0KFinished libnghttp2 (00m:01s) 790 KB 2 MB/s\n", - "python [] (00m:01s) Validating...\n", - "libssh2 [] (00m:01s) Decompressing...\n", - "libxml2 [] (00m:01s) Waiting...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) Validating...\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:01s) Waiting...\n", - "libsanitizer [] (00m:01s) Waiting...\n", - "sysroot_linux-64 [] (00m:01s) Validating...\n", - "libffi [] (00m:01s) Waiting...\n", - "cryptography [] (00m:00s) Waiting...\n", - "libstdcxx-ng [] (00m:00s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "zstd [] (00m:00s) Waiting...\n", - "libnsl [] (00m:00s) Waiting...\n", - "python [] (00m:01s) Validating...\n", - "libssh2 [] (00m:01s) Decompressing...\n", - "libxml2 [] (00m:01s) Decompressing...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) Validating...\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:01s) Waiting...\n", - "libsanitizer [] (00m:01s) Waiting...\n", - "sysroot_linux-64 [] (00m:01s) Validating...\n", - "libffi [] (00m:01s) Waiting...\n", - "cryptography [] (00m:01s) Waiting...\n", - "libstdcxx-ng [] (00m:01s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "zstd [] (00m:00s) Waiting...\n", - "libnsl [] (00m:00s) Waiting...\n", - "\u001b[26A\u001b[0KFinished libssh2 (00m:01s) 233 KB 509 KB/s\n", - "python [] (00m:01s) Validating...\n", - "libxml2 [] (00m:01s) Decompressing...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) Validating...\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:01s) Waiting...\n", - "libsanitizer [] (00m:01s) Waiting...\n", - "sysroot_linux-64 [] (00m:01s) Validating...\n", - "libffi [] (00m:01s) Waiting...\n", - "cryptography [] (00m:01s) Waiting...\n", - "libstdcxx-ng [] (00m:01s) Waiting...\n", - "sqlite [] (00m:00s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:00s) Waiting...\n", - "libzlib [] (00m:00s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "zstd [] (00m:00s) Waiting...\n", - "libnsl [] (00m:00s) Waiting...\n", - "python [] (00m:02s) Validating...\n", - "libxml2 [] (00m:01s) Decompressing...\n", - "readline [] (00m:01s) Waiting...\n", - "tk [] (00m:01s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) Validating...\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:01s) Waiting...\n", - "libsanitizer [] (00m:01s) Waiting...\n", - "sysroot_linux-64 [] (00m:01s) Waiting...\n", - "libffi [] (00m:01s) Waiting...\n", - "cryptography [] (00m:01s) Waiting...\n", - "libstdcxx-ng [] (00m:01s) Waiting...\n", - "sqlite [] (00m:01s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:01s) Waiting...\n", - "libzlib [] (00m:01s) Waiting...\n", - "cffi [] (00m:00s) Waiting...\n", - "libarchive [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "gcc [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "zstd [] (00m:00s) Waiting...\n", - "libnsl [] (00m:00s) Waiting...\n", - "python [] (00m:02s) Validating...\n", - "libxml2 [] (00m:02s) Decompressing...\n", - "readline [] (00m:02s) Decompressing...\n", - "tk [] (00m:02s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) Validating...\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:01s) Waiting...\n", - "libsanitizer [] (00m:01s) Waiting...\n", - "sysroot_linux-64 [] (00m:01s) Waiting...\n", - "libffi [] (00m:01s) Waiting...\n", - "cryptography [] (00m:01s) Waiting...\n", - "libstdcxx-ng [] (00m:01s) Waiting...\n", - "sqlite [] (00m:01s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:01s) Waiting...\n", - "libzlib [] (00m:01s) Waiting...\n", - "cffi [] (00m:01s) Waiting...\n", - "libarchive [] (00m:01s) Waiting...\n", - "openssl [] (00m:01s) Waiting...\n", - "gcc [] (00m:01s) Waiting...\n", - "libgomp [] (00m:01s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "zstd [] (00m:00s) Waiting...\n", - "libnsl [] (00m:00s) Waiting...\n", - "\u001b[25A\u001b[0KFinished libxml2 (00m:02s) 772 KB 2 MB/s\n", - "python [] (00m:02s) Validating...\n", - "readline [] (00m:02s) Decompressing...\n", - "tk [] (00m:02s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:01s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:01s) Validating...\n", - "libgcc-devel_linux-64 [] (00m:01s) Waiting...\n", - "certifi [] (00m:01s) Waiting...\n", - "libsanitizer [] (00m:01s) Waiting...\n", - "sysroot_linux-64 [] (00m:01s) Waiting...\n", - "libffi [] (00m:01s) Waiting...\n", - "cryptography [] (00m:01s) Waiting...\n", - "libstdcxx-ng [] (00m:01s) Waiting...\n", - "sqlite [] (00m:01s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:01s) Waiting...\n", - "libzlib [] (00m:01s) Waiting...\n", - "cffi [] (00m:01s) Waiting...\n", - "libarchive [] (00m:01s) Waiting...\n", - "openssl [] (00m:01s) Waiting...\n", - "gcc [] (00m:01s) Waiting...\n", - "libgomp [] (00m:01s) Waiting...\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "krb5 [] (00m:00s) Waiting...\n", - "zstd [] (00m:00s) Waiting...\n", - "libnsl [] (00m:00s) Waiting...\n", - "python [] (00m:02s) Validating...\n", - "readline [] (00m:02s) Decompressing...\n", - "tk [] (00m:02s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:02s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:02s) Waiting...\n", - "libgcc-devel_linux-64 [] (00m:02s) Waiting...\n", - "certifi [] (00m:01s) Waiting...\n", - "libsanitizer [] (00m:01s) Waiting...\n", - "sysroot_linux-64 [] (00m:01s) Waiting...\n", - "libffi [] (00m:01s) Waiting...\n", - "cryptography [] (00m:01s) Waiting...\n", - "libstdcxx-ng [] (00m:01s) Waiting...\n", - "sqlite [] (00m:01s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:01s) Waiting...\n", - "libzlib [] (00m:01s) Waiting...\n", - "cffi [] (00m:01s) Waiting...\n", - "libarchive [] (00m:01s) Waiting...\n", - "openssl [] (00m:01s) Waiting...\n", - "gcc [] (00m:01s) Waiting...\n", - "libgomp [] (00m:01s) Waiting...\n", - "ca-certificates [] (00m:01s) Waiting...\n", - "krb5 [] (00m:01s) Waiting...\n", - "zstd [] (00m:00s) Waiting...\n", - "libnsl [] (00m:00s) Waiting...\n", - "python [] (00m:02s) Validating...\n", - "readline [] (00m:02s) Decompressing...\n", - "tk [] (00m:02s) Decompressing...\n", - "binutils_impl_linux-64 [] (00m:02s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:02s) Waiting...\n", - "libgcc-devel_linux-64 [] (00m:02s) Waiting...\n", - "certifi [] (00m:01s) Waiting...\n", - "libsanitizer [] (00m:01s) Waiting...\n", - "sysroot_linux-64 [] (00m:01s) Waiting...\n", - "libffi [] (00m:01s) Waiting...\n", - "cryptography [] (00m:01s) Waiting...\n", - "libstdcxx-ng [] (00m:01s) Waiting...\n", - "sqlite [] (00m:01s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:01s) Waiting...\n", - "libzlib [] (00m:01s) Waiting...\n", - "cffi [] (00m:01s) Waiting...\n", - "libarchive [] (00m:01s) Waiting...\n", - "openssl [] (00m:01s) Waiting...\n", - "gcc [] (00m:01s) Waiting...\n", - "libgomp [] (00m:01s) Waiting...\n", - "ca-certificates [] (00m:01s) Waiting...\n", - "krb5 [] (00m:01s) Waiting...\n", - "zstd [] (00m:01s) Waiting...\n", - "libnsl [] (00m:00s) Waiting...\n", - "\u001b[24A\u001b[0KFinished readline (00m:02s) 295 KB 580 KB/s\n", - "python [] (00m:02s) Validating...\n", - "tk [] (00m:02s) Decompressing...\n", - "binutils_impl_linux-64 [] (00m:02s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:02s) Waiting...\n", - "libgcc-devel_linux-64 [] (00m:02s) Waiting...\n", - "certifi [] (00m:01s) Waiting...\n", - "libsanitizer [] (00m:01s) Waiting...\n", - "sysroot_linux-64 [] (00m:01s) Waiting...\n", - "libffi [] (00m:01s) Waiting...\n", - "cryptography [] (00m:01s) Waiting...\n", - "libstdcxx-ng [] (00m:01s) Waiting...\n", - "sqlite [] (00m:01s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:01s) Waiting...\n", - "libzlib [] (00m:01s) Waiting...\n", - "cffi [] (00m:01s) Waiting...\n", - "libarchive [] (00m:01s) Waiting...\n", - "openssl [] (00m:01s) Waiting...\n", - "gcc [] (00m:01s) Waiting...\n", - "libgomp [] (00m:01s) Waiting...\n", - "ca-certificates [] (00m:01s) Waiting...\n", - "krb5 [] (00m:01s) Waiting...\n", - "zstd [] (00m:01s) Waiting...\n", - "libnsl [] (00m:00s) Waiting...\n", - "python [] (00m:02s) Waiting...\n", - "tk [] (00m:02s) Decompressing...\n", - "binutils_impl_linux-64 [] (00m:02s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:02s) Waiting...\n", - "libgcc-devel_linux-64 [] (00m:02s) Waiting...\n", - "certifi [] (00m:01s) Waiting...\n", - "libsanitizer [] (00m:01s) Waiting...\n", - "sysroot_linux-64 [] (00m:01s) Waiting...\n", - "libffi [] (00m:01s) Waiting...\n", - "cryptography [] (00m:01s) Waiting...\n", - "libstdcxx-ng [] (00m:01s) Waiting...\n", - "sqlite [] (00m:01s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:01s) Waiting...\n", - "libzlib [] (00m:01s) Waiting...\n", - "cffi [] (00m:01s) Waiting...\n", - "libarchive [] (00m:01s) Waiting...\n", - "openssl [] (00m:01s) Waiting...\n", - "gcc [] (00m:01s) Waiting...\n", - "libgomp [] (00m:01s) Waiting...\n", - "ca-certificates [] (00m:01s) Waiting...\n", - "krb5 [] (00m:01s) Waiting...\n", - "zstd [] (00m:01s) Waiting...\n", - "libnsl [] (00m:01s) Waiting...\n", - "python [] (00m:03s) Waiting...\n", - "tk [] (00m:02s) Decompressing...\n", - "binutils_impl_linux-64 [] (00m:02s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:02s) Waiting...\n", - "libgcc-devel_linux-64 [] (00m:02s) Decompressing...\n", - "certifi [] (00m:02s) Waiting...\n", - "libsanitizer [] (00m:02s) Waiting...\n", - "sysroot_linux-64 [] (00m:02s) Waiting...\n", - "libffi [] (00m:02s) Waiting...\n", - "cryptography [] (00m:02s) Waiting...\n", - "libstdcxx-ng [] (00m:02s) Waiting...\n", - "sqlite [] (00m:02s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:02s) Waiting...\n", - "libzlib [] (00m:02s) Waiting...\n", - "cffi [] (00m:02s) Waiting...\n", - "libarchive [] (00m:02s) Waiting...\n", - "openssl [] (00m:02s) Waiting...\n", - "gcc [] (00m:01s) Waiting...\n", - "libgomp [] (00m:01s) Waiting...\n", - "ca-certificates [] (00m:01s) Waiting...\n", - "krb5 [] (00m:01s) Waiting...\n", - "zstd [] (00m:01s) Waiting...\n", - "libnsl [] (00m:01s) Waiting...\n", - "\u001b[23A\u001b[0KFinished tk (00m:02s) 3 MB 5 MB/s\n", - "python [] (00m:03s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:02s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:02s) Waiting...\n", - "libgcc-devel_linux-64 [] (00m:02s) Decompressing...\n", - "certifi [] (00m:02s) Waiting...\n", - "libsanitizer [] (00m:02s) Waiting...\n", - "sysroot_linux-64 [] (00m:02s) Waiting...\n", - "libffi [] (00m:02s) Waiting...\n", - "cryptography [] (00m:02s) Waiting...\n", - "libstdcxx-ng [] (00m:02s) Waiting...\n", - "sqlite [] (00m:02s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:02s) Waiting...\n", - "libzlib [] (00m:02s) Waiting...\n", - "cffi [] (00m:02s) Waiting...\n", - "libarchive [] (00m:02s) Waiting...\n", - "openssl [] (00m:02s) Waiting...\n", - "gcc [] (00m:01s) Waiting...\n", - "libgomp [] (00m:01s) Waiting...\n", - "ca-certificates [] (00m:01s) Waiting...\n", - "krb5 [] (00m:01s) Waiting...\n", - "zstd [] (00m:01s) Waiting...\n", - "libnsl [] (00m:01s) Waiting...\n", - "\u001b[22A\u001b[0KFinished libgcc-devel_linux-64 (00m:03s) 3 MB 5 MB/s\n", - "python [] (00m:03s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:03s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:03s) Waiting...\n", - "certifi [] (00m:03s) Decompressing...\n", - "libsanitizer [] (00m:03s) Waiting...\n", - "sysroot_linux-64 [] (00m:03s) Waiting...\n", - "libffi [] (00m:03s) Waiting...\n", - "cryptography [] (00m:03s) Waiting...\n", - "libstdcxx-ng [] (00m:02s) Waiting...\n", - "sqlite [] (00m:02s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:02s) Waiting...\n", - "libzlib [] (00m:02s) Waiting...\n", - "cffi [] (00m:02s) Waiting...\n", - "libarchive [] (00m:02s) Waiting...\n", - "openssl [] (00m:02s) Waiting...\n", - "gcc [] (00m:02s) Waiting...\n", - "libgomp [] (00m:02s) Waiting...\n", - "ca-certificates [] (00m:02s) Waiting...\n", - "krb5 [] (00m:02s) Waiting...\n", - "zstd [] (00m:02s) Waiting...\n", - "libnsl [] (00m:02s) Waiting...\n", - "python [] (00m:03s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:03s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:03s) Waiting...\n", - "certifi [] (00m:03s) Decompressing...\n", - "libsanitizer [] (00m:03s) Waiting...\n", - "sysroot_linux-64 [] (00m:03s) Waiting...\n", - "libffi [] (00m:03s) Waiting...\n", - "cryptography [] (00m:03s) Waiting...\n", - "libstdcxx-ng [] (00m:02s) Waiting...\n", - "sqlite [] (00m:02s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:02s) Waiting...\n", - "libzlib [] (00m:02s) Waiting...\n", - "cffi [] (00m:02s) Waiting...\n", - "libarchive [] (00m:02s) Waiting...\n", - "openssl [] (00m:02s) Waiting...\n", - "gcc [] (00m:02s) Waiting...\n", - "libgomp [] (00m:02s) Waiting...\n", - "ca-certificates [] (00m:02s) Waiting...\n", - "krb5 [] (00m:02s) Waiting...\n", - "zstd [] (00m:02s) Waiting...\n", - "libnsl [] (00m:02s) Waiting...\n", - "\u001b[21A\u001b[0KFinished certifi (00m:03s) 145 KB 187 KB/s\n", - "python [] (00m:03s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:03s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:03s) Waiting...\n", - "libsanitizer [] (00m:03s) Waiting...\n", - "sysroot_linux-64 [] (00m:03s) Waiting...\n", - "libffi [] (00m:03s) Decompressing...\n", - "cryptography [] (00m:03s) Waiting...\n", - "libstdcxx-ng [] (00m:02s) Waiting...\n", - "sqlite [] (00m:02s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:02s) Waiting...\n", - "libzlib [] (00m:02s) Waiting...\n", - "cffi [] (00m:02s) Waiting...\n", - "libarchive [] (00m:02s) Waiting...\n", - "openssl [] (00m:02s) Waiting...\n", - "gcc [] (00m:02s) Waiting...\n", - "libgomp [] (00m:02s) Waiting...\n", - "ca-certificates [] (00m:02s) Waiting...\n", - "krb5 [] (00m:02s) Waiting...\n", - "zstd [] (00m:02s) Waiting...\n", - "libnsl [] (00m:02s) Waiting...\n", - "python [] (00m:03s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:03s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:03s) Waiting...\n", - "libsanitizer [] (00m:03s) Waiting...\n", - "sysroot_linux-64 [] (00m:03s) Waiting...\n", - "libffi [] (00m:03s) Decompressing...\n", - "cryptography [] (00m:03s) Waiting...\n", - "libstdcxx-ng [] (00m:02s) Waiting...\n", - "sqlite [] (00m:02s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:02s) Waiting...\n", - "libzlib [] (00m:02s) Waiting...\n", - "cffi [] (00m:02s) Waiting...\n", - "libarchive [] (00m:02s) Waiting...\n", - "openssl [] (00m:02s) Waiting...\n", - "gcc [] (00m:02s) Waiting...\n", - "libgomp [] (00m:02s) Waiting...\n", - "ca-certificates [] (00m:02s) Waiting...\n", - "krb5 [] (00m:02s) Waiting...\n", - "zstd [] (00m:02s) Waiting...\n", - "libnsl [] (00m:02s) Waiting...\n", - "\u001b[20A\u001b[0KFinished libffi (00m:03s) 57 KB 62 KB/s\n", - "python [] (00m:03s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:03s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:03s) Waiting...\n", - "libsanitizer [] (00m:03s) Decompressing...\n", - "sysroot_linux-64 [] (00m:03s) Waiting...\n", - "cryptography [] (00m:03s) Waiting...\n", - "libstdcxx-ng [] (00m:02s) Waiting...\n", - "sqlite [] (00m:02s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:02s) Waiting...\n", - "libzlib [] (00m:02s) Waiting...\n", - "cffi [] (00m:02s) Waiting...\n", - "libarchive [] (00m:02s) Waiting...\n", - "openssl [] (00m:02s) Waiting...\n", - "gcc [] (00m:02s) Waiting...\n", - "libgomp [] (00m:02s) Waiting...\n", - "ca-certificates [] (00m:02s) Waiting...\n", - "krb5 [] (00m:02s) Waiting...\n", - "zstd [] (00m:02s) Waiting...\n", - "libnsl [] (00m:02s) Waiting...\n", - "python [] (00m:03s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:03s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:03s) Waiting...\n", - "libsanitizer [] (00m:03s) Decompressing...\n", - "sysroot_linux-64 [] (00m:03s) Waiting...\n", - "cryptography [] (00m:03s) Waiting...\n", - "libstdcxx-ng [] (00m:02s) Waiting...\n", - "sqlite [] (00m:02s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:02s) Waiting...\n", - "libzlib [] (00m:02s) Waiting...\n", - "cffi [] (00m:02s) Waiting...\n", - "libarchive [] (00m:02s) Waiting...\n", - "openssl [] (00m:02s) Waiting...\n", - "gcc [] (00m:02s) Waiting...\n", - "libgomp [] (00m:02s) Waiting...\n", - "ca-certificates [] (00m:02s) Waiting...\n", - "krb5 [] (00m:02s) Waiting...\n", - "zstd [] (00m:02s) Waiting...\n", - "libnsl [] (00m:02s) Waiting...\n", - "\u001b[19A\u001b[0KFinished libsanitizer (00m:04s) 6 MB 7 MB/s\n", - "python [] (00m:04s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:04s) Decompressing...\n", - "gcc_impl_linux-64 [] (00m:04s) Waiting...\n", - "sysroot_linux-64 [] (00m:04s) Waiting...\n", - "cryptography [] (00m:04s) Waiting...\n", - "libstdcxx-ng [] (00m:04s) Waiting...\n", - "sqlite [] (00m:03s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:03s) Waiting...\n", - "libzlib [] (00m:03s) Waiting...\n", - "cffi [] (00m:03s) Waiting...\n", - "libarchive [] (00m:03s) Waiting...\n", - "openssl [] (00m:03s) Waiting...\n", - "gcc [] (00m:03s) Waiting...\n", - "libgomp [] (00m:03s) Waiting...\n", - "ca-certificates [] (00m:03s) Waiting...\n", - "krb5 [] (00m:03s) Waiting...\n", - "zstd [] (00m:03s) Waiting...\n", - "libnsl [] (00m:03s) Waiting...\n", - "python [] (00m:04s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:04s) Decompressing...\n", - "gcc_impl_linux-64 [] (00m:04s) Waiting...\n", - "sysroot_linux-64 [] (00m:04s) Waiting...\n", - "cryptography [] (00m:04s) Waiting...\n", - "libstdcxx-ng [] (00m:04s) Waiting...\n", - "sqlite [] (00m:03s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:03s) Waiting...\n", - "libzlib [] (00m:03s) Waiting...\n", - "cffi [] (00m:03s) Waiting...\n", - "libarchive [] (00m:03s) Waiting...\n", - "openssl [] (00m:03s) Waiting...\n", - "gcc [] (00m:03s) Waiting...\n", - "libgomp [] (00m:03s) Waiting...\n", - "ca-certificates [] (00m:03s) Waiting...\n", - "krb5 [] (00m:03s) Waiting...\n", - "zstd [] (00m:03s) Waiting...\n", - "libnsl [] (00m:03s) Waiting...\n", - "python [] (00m:07s) Waiting...\n", - "binutils_impl_linux-64 [] (00m:06s) Decompressing...\n", - "gcc_impl_linux-64 [] (00m:06s) Waiting...\n", - "sysroot_linux-64 [] (00m:06s) Waiting...\n", - "cryptography [] (00m:06s) Decompressing...\n", - "libstdcxx-ng [] (00m:06s) Waiting...\n", - "sqlite [] (00m:06s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:06s) Waiting...\n", - "libzlib [] (00m:06s) Waiting...\n", - "cffi [] (00m:06s) Waiting...\n", - "libarchive [] (00m:05s) Waiting...\n", - "openssl [] (00m:05s) Waiting...\n", - "gcc [] (00m:05s) Waiting...\n", - "libgomp [] (00m:05s) Waiting...\n", - "ca-certificates [] (00m:05s) Waiting...\n", - "krb5 [] (00m:05s) Waiting...\n", - "zstd [] (00m:05s) Waiting...\n", - "libnsl [] (00m:05s) Waiting...\n", - "\u001b[18A\u001b[0KFinished binutils_impl_linux-64 (00m:06s) 10 MB 14 MB/s\n", - "python [] (00m:07s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:06s) Waiting...\n", - "sysroot_linux-64 [] (00m:06s) Waiting...\n", - "cryptography [] (00m:06s) Decompressing...\n", - "libstdcxx-ng [] (00m:06s) Waiting...\n", - "sqlite [] (00m:06s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:06s) Waiting...\n", - "libzlib [] (00m:06s) Waiting...\n", - "cffi [] (00m:06s) Waiting...\n", - "libarchive [] (00m:05s) Waiting...\n", - "openssl [] (00m:05s) Waiting...\n", - "gcc [] (00m:05s) Waiting...\n", - "libgomp [] (00m:05s) Waiting...\n", - "ca-certificates [] (00m:05s) Waiting...\n", - "krb5 [] (00m:05s) Waiting...\n", - "zstd [] (00m:05s) Waiting...\n", - "libnsl [] (00m:05s) Waiting...\n", - "python [] (00m:07s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:06s) Waiting...\n", - "sysroot_linux-64 [] (00m:06s) Waiting...\n", - "cryptography [] (00m:06s) Decompressing...\n", - "libstdcxx-ng [] (00m:06s) Decompressing...\n", - "sqlite [] (00m:06s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:06s) Waiting...\n", - "libzlib [] (00m:06s) Waiting...\n", - "cffi [] (00m:06s) Waiting...\n", - "libarchive [] (00m:05s) Waiting...\n", - "openssl [] (00m:05s) Waiting...\n", - "gcc [] (00m:05s) Waiting...\n", - "libgomp [] (00m:05s) Waiting...\n", - "ca-certificates [] (00m:05s) Waiting...\n", - "krb5 [] (00m:05s) Waiting...\n", - "zstd [] (00m:05s) Waiting...\n", - "libnsl [] (00m:05s) Waiting...\n", - "\u001b[17A\u001b[0KFinished cryptography (00m:06s) 1 MB 1 MB/s\n", - "python [] (00m:07s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:06s) Waiting...\n", - "sysroot_linux-64 [] (00m:06s) Waiting...\n", - "libstdcxx-ng [] (00m:06s) Decompressing...\n", - "sqlite [] (00m:06s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:06s) Waiting...\n", - "libzlib [] (00m:06s) Waiting...\n", - "cffi [] (00m:06s) Waiting...\n", - "libarchive [] (00m:05s) Waiting...\n", - "openssl [] (00m:05s) Waiting...\n", - "gcc [] (00m:05s) Waiting...\n", - "libgomp [] (00m:05s) Waiting...\n", - "ca-certificates [] (00m:05s) Waiting...\n", - "krb5 [] (00m:05s) Waiting...\n", - "zstd [] (00m:05s) Waiting...\n", - "libnsl [] (00m:05s) Waiting...\n", - "\u001b[16A\u001b[0KFinished libstdcxx-ng (00m:07s) 4 MB 4 MB/s\n", - "python [] (00m:07s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:07s) Waiting...\n", - "sysroot_linux-64 [] (00m:07s) Waiting...\n", - "sqlite [] (00m:06s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:06s) Decompressing...\n", - "libzlib [] (00m:06s) Waiting...\n", - "cffi [] (00m:06s) Waiting...\n", - "libarchive [] (00m:06s) Waiting...\n", - "openssl [] (00m:06s) Waiting...\n", - "gcc [] (00m:06s) Waiting...\n", - "libgomp [] (00m:06s) Waiting...\n", - "ca-certificates [] (00m:06s) Waiting...\n", - "krb5 [] (00m:06s) Waiting...\n", - "zstd [] (00m:06s) Waiting...\n", - "libnsl [] (00m:06s) Waiting...\n", - "python [] (00m:07s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:07s) Waiting...\n", - "sysroot_linux-64 [] (00m:07s) Waiting...\n", - "sqlite [] (00m:06s) Waiting...\n", - "kernel-headers_linux-64 [] (00m:06s) Decompressing...\n", - "libzlib [] (00m:06s) Waiting...\n", - "cffi [] (00m:06s) Waiting...\n", - "libarchive [] (00m:06s) Waiting...\n", - "openssl [] (00m:06s) Waiting...\n", - "gcc [] (00m:06s) Waiting...\n", - "libgomp [] (00m:06s) Waiting...\n", - "ca-certificates [] (00m:06s) Waiting...\n", - "krb5 [] (00m:06s) Waiting...\n", - "zstd [] (00m:06s) Waiting...\n", - "libnsl [] (00m:06s) Waiting...\n", - "\u001b[15A\u001b[0KFinished kernel-headers_linux-64 (00m:07s) 707 KB 556 KB/s\n", - "python [] (00m:08s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:07s) Waiting...\n", - "sysroot_linux-64 [] (00m:07s) Waiting...\n", - "sqlite [] (00m:07s) Waiting...\n", - "libzlib [] (00m:07s) Decompressing...\n", - "cffi [] (00m:07s) Waiting...\n", - "libarchive [] (00m:07s) Waiting...\n", - "openssl [] (00m:07s) Waiting...\n", - "gcc [] (00m:06s) Waiting...\n", - "libgomp [] (00m:06s) Waiting...\n", - "ca-certificates [] (00m:06s) Waiting...\n", - "krb5 [] (00m:06s) Waiting...\n", - "zstd [] (00m:06s) Waiting...\n", - "libnsl [] (00m:06s) Waiting...\n", - "python [] (00m:08s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:07s) Waiting...\n", - "sysroot_linux-64 [] (00m:07s) Waiting...\n", - "sqlite [] (00m:07s) Waiting...\n", - "libzlib [] (00m:07s) Decompressing...\n", - "cffi [] (00m:07s) Waiting...\n", - "libarchive [] (00m:07s) Waiting...\n", - "openssl [] (00m:07s) Waiting...\n", - "gcc [] (00m:06s) Waiting...\n", - "libgomp [] (00m:06s) Waiting...\n", - "ca-certificates [] (00m:06s) Waiting...\n", - "krb5 [] (00m:06s) Waiting...\n", - "zstd [] (00m:06s) Waiting...\n", - "libnsl [] (00m:06s) Waiting...\n", - "python [] (00m:08s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:07s) Waiting...\n", - "sysroot_linux-64 [] (00m:07s) Waiting...\n", - "sqlite [] (00m:07s) Decompressing...\n", - "libzlib [] (00m:07s) Decompressing...\n", - "cffi [] (00m:07s) Waiting...\n", - "libarchive [] (00m:07s) Waiting...\n", - "openssl [] (00m:07s) Waiting...\n", - "gcc [] (00m:06s) Waiting...\n", - "libgomp [] (00m:06s) Waiting...\n", - "ca-certificates [] (00m:06s) Waiting...\n", - "krb5 [] (00m:06s) Waiting...\n", - "zstd [] (00m:06s) Waiting...\n", - "libnsl [] (00m:06s) Waiting...\n", - "\u001b[14A\u001b[0KFinished libzlib (00m:07s) 59 KB 44 KB/s\n", - "python [] (00m:08s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:07s) Waiting...\n", - "sysroot_linux-64 [] (00m:07s) Waiting...\n", - "sqlite [] (00m:07s) Decompressing...\n", - "cffi [] (00m:07s) Waiting...\n", - "libarchive [] (00m:07s) Waiting...\n", - "openssl [] (00m:07s) Waiting...\n", - "gcc [] (00m:06s) Waiting...\n", - "libgomp [] (00m:06s) Waiting...\n", - "ca-certificates [] (00m:06s) Waiting...\n", - "krb5 [] (00m:06s) Waiting...\n", - "zstd [] (00m:06s) Waiting...\n", - "libnsl [] (00m:06s) Waiting...\n", - "python [] (00m:08s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:08s) Waiting...\n", - "sysroot_linux-64 [] (00m:07s) Waiting...\n", - "sqlite [] (00m:07s) Decompressing...\n", - "cffi [] (00m:07s) Decompressing...\n", - "libarchive [] (00m:07s) Waiting...\n", - "openssl [] (00m:07s) Waiting...\n", - "gcc [] (00m:07s) Waiting...\n", - "libgomp [] (00m:07s) Waiting...\n", - "ca-certificates [] (00m:07s) Waiting...\n", - "krb5 [] (00m:07s) Waiting...\n", - "zstd [] (00m:07s) Waiting...\n", - "libnsl [] (00m:07s) Waiting...\n", - "\u001b[13A\u001b[0KFinished sqlite (00m:07s) 1 MB 1 MB/s\n", - "python [] (00m:08s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:08s) Waiting...\n", - "sysroot_linux-64 [] (00m:07s) Waiting...\n", - "cffi [] (00m:07s) Decompressing...\n", - "libarchive [] (00m:07s) Waiting...\n", - "openssl [] (00m:07s) Waiting...\n", - "gcc [] (00m:07s) Waiting...\n", - "libgomp [] (00m:07s) Waiting...\n", - "ca-certificates [] (00m:07s) Waiting...\n", - "krb5 [] (00m:07s) Waiting...\n", - "zstd [] (00m:07s) Waiting...\n", - "libnsl [] (00m:07s) Waiting...\n", - "\u001b[12A\u001b[0KFinished cffi (00m:07s) 225 KB 164 KB/s\n", - "python [] (00m:08s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:08s) Waiting...\n", - "sysroot_linux-64 [] (00m:07s) Waiting...\n", - "libarchive [] (00m:07s) Decompressing...\n", - "openssl [] (00m:07s) Waiting...\n", - "gcc [] (00m:07s) Waiting...\n", - "libgomp [] (00m:07s) Waiting...\n", - "ca-certificates [] (00m:07s) Waiting...\n", - "krb5 [] (00m:07s) Waiting...\n", - "zstd [] (00m:07s) Waiting...\n", - "libnsl [] (00m:07s) Waiting...\n", - "python [] (00m:08s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:08s) Waiting...\n", - "sysroot_linux-64 [] (00m:07s) Waiting...\n", - "libarchive [] (00m:07s) Decompressing...\n", - "openssl [] (00m:07s) Waiting...\n", - "gcc [] (00m:07s) Waiting...\n", - "libgomp [] (00m:07s) Waiting...\n", - "ca-certificates [] (00m:07s) Waiting...\n", - "krb5 [] (00m:07s) Waiting...\n", - "zstd [] (00m:07s) Waiting...\n", - "libnsl [] (00m:07s) Waiting...\n", - "python [] (00m:08s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:08s) Waiting...\n", - "sysroot_linux-64 [] (00m:08s) Waiting...\n", - "libarchive [] (00m:07s) Decompressing...\n", - "openssl [] (00m:07s) Waiting...\n", - "gcc [] (00m:07s) Decompressing...\n", - "libgomp [] (00m:07s) Waiting...\n", - "ca-certificates [] (00m:07s) Waiting...\n", - "krb5 [] (00m:07s) Waiting...\n", - "zstd [] (00m:07s) Waiting...\n", - "libnsl [] (00m:07s) Waiting...\n", - "\u001b[11A\u001b[0KFinished libarchive (00m:07s) 2 MB 1 MB/s\n", - "python [] (00m:08s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:08s) Waiting...\n", - "sysroot_linux-64 [] (00m:08s) Waiting...\n", - "openssl [] (00m:07s) Waiting...\n", - "gcc [] (00m:07s) Decompressing...\n", - "libgomp [] (00m:07s) Waiting...\n", - "ca-certificates [] (00m:07s) Waiting...\n", - "krb5 [] (00m:07s) Waiting...\n", - "zstd [] (00m:07s) Waiting...\n", - "libnsl [] (00m:07s) Waiting...\n", - "\u001b[10A\u001b[0KFinished gcc (00m:07s) 24 KB 16 KB/s\n", - "python [] (00m:08s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:08s) Waiting...\n", - "sysroot_linux-64 [] (00m:08s) Waiting...\n", - "openssl [] (00m:07s) Decompressing...\n", - "libgomp [] (00m:07s) Waiting...\n", - "ca-certificates [] (00m:07s) Waiting...\n", - "krb5 [] (00m:07s) Waiting...\n", - "zstd [] (00m:07s) Waiting...\n", - "libnsl [] (00m:07s) Waiting...\n", - "python [] (00m:08s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:08s) Waiting...\n", - "sysroot_linux-64 [] (00m:08s) Waiting...\n", - "openssl [] (00m:07s) Decompressing...\n", - "libgomp [] (00m:07s) Waiting...\n", - "ca-certificates [] (00m:07s) Waiting...\n", - "krb5 [] (00m:07s) Waiting...\n", - "zstd [] (00m:07s) Waiting...\n", - "libnsl [] (00m:07s) Waiting...\n", - "python [] (00m:09s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:09s) Waiting...\n", - "sysroot_linux-64 [] (00m:08s) Waiting...\n", - "openssl [] (00m:08s) Decompressing...\n", - "libgomp [] (00m:08s) Decompressing...\n", - "ca-certificates [] (00m:08s) Waiting...\n", - "krb5 [] (00m:08s) Waiting...\n", - "zstd [] (00m:08s) Waiting...\n", - "libnsl [] (00m:08s) Waiting...\n", - "\u001b[9A\u001b[0KFinished openssl (00m:08s) 3 MB 2 MB/s\n", - "python [] (00m:09s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:09s) Waiting...\n", - "sysroot_linux-64 [] (00m:08s) Waiting...\n", - "libgomp [] (00m:08s) Decompressing...\n", - "ca-certificates [] (00m:08s) Waiting...\n", - "krb5 [] (00m:08s) Waiting...\n", - "zstd [] (00m:08s) Waiting...\n", - "libnsl [] (00m:08s) Waiting...\n", - "python [] (00m:09s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:09s) Waiting...\n", - "sysroot_linux-64 [] (00m:08s) Waiting...\n", - "libgomp [] (00m:08s) Decompressing...\n", - "ca-certificates [] (00m:08s) Decompressing...\n", - "krb5 [] (00m:08s) Waiting...\n", - "zstd [] (00m:08s) Waiting...\n", - "libnsl [] (00m:08s) Waiting...\n", - "\u001b[8A\u001b[0KFinished ca-certificates (00m:08s) 115 KB 72 KB/s\n", - "python [] (00m:09s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:09s) Waiting...\n", - "sysroot_linux-64 [] (00m:08s) Waiting...\n", - "libgomp [] (00m:08s) Decompressing...\n", - "krb5 [] (00m:08s) Decompressing...\n", - "zstd [] (00m:08s) Waiting...\n", - "libnsl [] (00m:08s) Waiting...\n", - "python [] (00m:09s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:09s) Waiting...\n", - "sysroot_linux-64 [] (00m:08s) Waiting...\n", - "libgomp [] (00m:08s) Decompressing...\n", - "krb5 [] (00m:08s) Decompressing...\n", - "zstd [] (00m:08s) Waiting...\n", - "libnsl [] (00m:08s) Waiting...\n", - "\u001b[7A\u001b[0KFinished libgomp (00m:08s) 427 KB 270 KB/s\n", - "python [] (00m:09s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:09s) Waiting...\n", - "sysroot_linux-64 [] (00m:08s) Waiting...\n", - "krb5 [] (00m:08s) Decompressing...\n", - "zstd [] (00m:08s) Waiting...\n", - "libnsl [] (00m:08s) Waiting...\n", - "\u001b[6A\u001b[0KFinished krb5 (00m:08s) 1 MB 891 KB/s\n", - "python [] (00m:09s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:09s) Waiting...\n", - "sysroot_linux-64 [] (00m:09s) Waiting...\n", - "zstd [] (00m:08s) Decompressing...\n", - "libnsl [] (00m:08s) Waiting...\n", - "python [] (00m:09s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:09s) Waiting...\n", - "sysroot_linux-64 [] (00m:09s) Waiting...\n", - "zstd [] (00m:08s) Decompressing...\n", - "libnsl [] (00m:08s) Waiting...\n", - "python [] (00m:09s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:09s) Waiting...\n", - "sysroot_linux-64 [] (00m:09s) Waiting...\n", - "zstd [] (00m:08s) Decompressing...\n", - "libnsl [] (00m:08s) Decompressing...\n", - "\u001b[5A\u001b[0KFinished zstd (00m:08s) 463 KB 273 KB/s\n", - "python [] (00m:09s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:09s) Waiting...\n", - "sysroot_linux-64 [] (00m:09s) Waiting...\n", - "libnsl [] (00m:08s) Decompressing...\n", - "\u001b[4A\u001b[0KFinished libnsl (00m:08s) 31 KB 18 KB/s\n", - "python [] (00m:09s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:09s) Waiting...\n", - "sysroot_linux-64 [] (00m:09s) Waiting...\n", - "python [] (00m:09s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:09s) Waiting...\n", - "sysroot_linux-64 [] (00m:09s) Decompressing...\n", - "python [] (00m:16s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:15s) Decompressing...\n", - "sysroot_linux-64 [] (00m:15s) Decompressing...\n", - "\u001b[3A\u001b[0KFinished sysroot_linux-64 (00m:15s) 31 MB 18 MB/s\n", - "python [] (00m:16s) Waiting...\n", - "gcc_impl_linux-64 [] (00m:15s) Decompressing...\n", - "python [] (00m:27s) Decompressing...\n", - "gcc_impl_linux-64 [] (00m:26s) Decompressing...\n", - "\u001b[2A\u001b[0KFinished gcc_impl_linux-64 (00m:26s) 52 MB 28 MB/s\n", - "python [] (00m:27s) Decompressing...\n", - "\u001b[1A\u001b[0KFinished python (00m:36s) 57 MB 32 MB/s\n", - "Preparing transaction: - \b\b\\ \b\b| \b\b/ \b\bdone\n", - "Verifying transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", - "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n" - ] - } - ], - "source": [ - "!mamba install gcc -c conda-forge" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "iJMQadvlCre5", - "outputId": "38f89a37-8847-4b5b-ba78-12f58b29d881" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " __ __ __ __\n", - " / \\ / \\ / \\ / \\\n", - " / \\/ \\/ \\/ \\\n", - "███████████████/ /██/ /██/ /██/ /████████████████████████\n", - " / / \\ / \\ / \\ / \\ \\____\n", - " / / \\_/ \\_/ \\_/ \\ o \\__,\n", - " / _/ \\_____/ `\n", - " |/\n", - " ███╗ ███╗ █████╗ ███╗ ███╗██████╗ █████╗\n", - " ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗\n", - " ██╔████╔██║███████║██╔████╔██║██████╔╝███████║\n", - " ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║\n", - " ██║ ╚═╝ ██║██║ ██║██║ ╚═╝ ██║██████╔╝██║ ██║\n", - " ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚═════╝ ╚═╝ ╚═╝\n", - "\n", - " Supported by @QuantStack\n", - "\n", - " GitHub: https://github.com/QuantStack/mamba\n", - " Twitter: https://twitter.com/QuantStack\n", - "\n", - "█████████████████████████████████████████████████████████████\n", - "\n", - "Getting conda-forge linux-64\n", - "Getting conda-forge noarch\n", - "\n", - "Looking for: ['gcc', 'libgfortran', 'python 3.7.10']\n", - "\n", - "232724 packages in https://conda.anaconda.org/conda-forge/linux-64\n", - "77750 packages in https://conda.anaconda.org/conda-forge/noarch\n", - "\n", - "## Package Plan ##\n", - "\n", - " environment location: /usr/local\n", - "\n", - " added / updated specs:\n", - " - gcc\n", - " - libgfortran\n", - " - python==3.7.10\n", - "\n", - "\n", - "The following packages will be downloaded:\n", - "\n", - " package | build\n", - " ---------------------------|-----------------\n", - " libgfortran-3.0.0 | 1 281 KB conda-forge\n", - " ------------------------------------------------------------\n", - " Total: 281 KB\n", - "\n", - "The following NEW packages will be INSTALLED:\n", - "\n", - " libgfortran conda-forge/linux-64::libgfortran-3.0.0-1\n", - "\n", - "\n", - "\n", - "Downloading and Extracting Packages\n", - "libgfortran-3.0.0 | 281 KB | : 100% 1.0/1 [00:00<00:00, 3.45it/s] \n", - "Preparing transaction: - \b\bdone\n", - "Verifying transaction: | \b\bdone\n", - "Executing transaction: - \b\bdone\n" - ] - } - ], - "source": [ - "!mamba install gcc libgfortran" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ZQ1PWl65DDWv", - "outputId": "461b6bef-3f50-44fe-f491-45a9c39fb3db" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " __ __ __ __\n", - " / \\ / \\ / \\ / \\\n", - " / \\/ \\/ \\/ \\\n", - "███████████████/ /██/ /██/ /██/ /████████████████████████\n", - " / / \\ / \\ / \\ / \\ \\____\n", - " / / \\_/ \\_/ \\_/ \\ o \\__,\n", - " / _/ \\_____/ `\n", - " |/\n", - " ███╗ ███╗ █████╗ ███╗ ███╗██████╗ █████╗\n", - " ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗\n", - " ██╔████╔██║███████║██╔████╔██║██████╔╝███████║\n", - " ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║\n", - " ██║ ╚═╝ ██║██║ ██║██║ ╚═╝ ██║██████╔╝██║ ██║\n", - " ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚═════╝ ╚═╝ ╚═╝\n", - "\n", - " Supported by @QuantStack\n", - "\n", - " GitHub: https://github.com/QuantStack/mamba\n", - " Twitter: https://twitter.com/QuantStack\n", - "\n", - "█████████████████████████████████████████████████████████████\n", - "\n", - "Getting conda-forge linux-64\n", - "Getting conda-forge noarch\n", - "\n", - "Looking for: ['numpy', 'python 3.7.10']\n", - "\n", - "232724 packages in https://conda.anaconda.org/conda-forge/linux-64\n", - "77750 packages in https://conda.anaconda.org/conda-forge/noarch\n", - "\n", - "## Package Plan ##\n", - "\n", - " environment location: /usr/local\n", - "\n", - " added / updated specs:\n", - " - numpy\n", - " - python==3.7.10\n", - "\n", - "\n", - "The following packages will be downloaded:\n", - "\n", - " package | build\n", - " ---------------------------|-----------------\n", - " libblas-3.9.0 |12_linux64_openblas 12 KB conda-forge\n", - " libcblas-3.9.0 |12_linux64_openblas 12 KB conda-forge\n", - " libgfortran-ng-11.2.0 | h69a702a_11 19 KB conda-forge\n", - " libgfortran5-11.2.0 | h5c6108e_11 1.7 MB conda-forge\n", - " liblapack-3.9.0 |12_linux64_openblas 12 KB conda-forge\n", - " libopenblas-0.3.18 |pthreads_h8fe5266_0 9.6 MB conda-forge\n", - " numpy-1.21.5 | py37hf2998dd_0 6.1 MB conda-forge\n", - " ------------------------------------------------------------\n", - " Total: 17.5 MB\n", - "\n", - "The following NEW packages will be INSTALLED:\n", - "\n", - " libblas conda-forge/linux-64::libblas-3.9.0-12_linux64_openblas\n", - " libcblas conda-forge/linux-64::libcblas-3.9.0-12_linux64_openblas\n", - " libgfortran-ng conda-forge/linux-64::libgfortran-ng-11.2.0-h69a702a_11\n", - " libgfortran5 conda-forge/linux-64::libgfortran5-11.2.0-h5c6108e_11\n", - " liblapack conda-forge/linux-64::liblapack-3.9.0-12_linux64_openblas\n", - " libopenblas conda-forge/linux-64::libopenblas-0.3.18-pthreads_h8fe5266_0\n", - " numpy conda-forge/linux-64::numpy-1.21.5-py37hf2998dd_0\n", - "\n", - "\n", - "\n", - "Downloading and Extracting Packages\n", - "libopenblas-0.3.18 | 9.6 MB | : 100% 1.0/1 [00:02<00:00, 2.47s/it]\n", - "numpy-1.21.5 | 6.1 MB | : 100% 1.0/1 [00:01<00:00, 1.88s/it]\n", - "libblas-3.9.0 | 12 KB | : 100% 1.0/1 [00:00<00:00, 15.63it/s]\n", - "libcblas-3.9.0 | 12 KB | : 100% 1.0/1 [00:00<00:00, 17.63it/s]\n", - "libgfortran-ng-11.2. | 19 KB | : 100% 1.0/1 [00:00<00:00, 22.01it/s]\n", - "liblapack-3.9.0 | 12 KB | : 100% 1.0/1 [00:00<00:00, 17.71it/s]\n", - "libgfortran5-11.2.0 | 1.7 MB | : 100% 1.0/1 [00:00<00:00, 2.11it/s] \n", - "Preparing transaction: - \b\bdone\n", - "Verifying transaction: | \b\b/ \b\bdone\n", - "Executing transaction: \\ \b\b| \b\bdone\n" - ] - } - ], - "source": [ - "!mamba install numpy" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VfhHqvnzD5Ae" - }, - "source": [ - "## Installation of Plotting libraries and MD related packages.\n", - "There are a number of additional packages that need to be installed for running MD. In this section we will install them using mamaba and the appropriate channels." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Ow7Z08eUEVV1", - "outputId": "7acd65b9-53cc-4ddc-c0e6-1884e31dd38b" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " __ __ __ __\n", - " / \\ / \\ / \\ / \\\n", - " / \\/ \\/ \\/ \\\n", - "███████████████/ /██/ /██/ /██/ /████████████████████████\n", - " / / \\ / \\ / \\ / \\ \\____\n", - " / / \\_/ \\_/ \\_/ \\ o \\__,\n", - " / _/ \\_____/ `\n", - " |/\n", - " ███╗ ███╗ █████╗ ███╗ ███╗██████╗ █████╗\n", - " ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗\n", - " ██╔████╔██║███████║██╔████╔██║██████╔╝███████║\n", - " ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║\n", - " ██║ ╚═╝ ██║██║ ██║██║ ╚═╝ ██║██████╔╝██║ ██║\n", - " ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚═════╝ ╚═╝ ╚═╝\n", - "\n", - " Supported by @QuantStack\n", - "\n", - " GitHub: https://github.com/QuantStack/mamba\n", - " Twitter: https://twitter.com/QuantStack\n", - "\n", - "█████████████████████████████████████████████████████████████\n", - "\n", - "Getting omnia linux-64\n", - "Getting omnia noarch\n", - "Getting conda-forge linux-64\n", - "Getting conda-forge noarch\n", - "\n", - "Looking for: ['openmm', 'openforcefield', 'parmed', 'yank', 'openmoltools', 'pdbfixer', 'solvationtoolkit', 'python 3.7.10']\n", - "\n", - "2131 packages in https://conda.anaconda.org/omnia/linux-64\n", - "45 packages in https://conda.anaconda.org/omnia/noarch\n", - "232724 packages in https://conda.anaconda.org/conda-forge/linux-64\n", - "77750 packages in https://conda.anaconda.org/conda-forge/noarch\n", - "\n", - "## Package Plan ##\n", - "\n", - " environment location: /usr/local\n", - "\n", - " added / updated specs:\n", - " - openforcefield\n", - " - openmm\n", - " - openmoltools\n", - " - parmed\n", - " - pdbfixer\n", - " - python==3.7.10\n", - " - solvationtoolkit\n", - " - yank\n", - "\n", - "\n", - "The following packages will be downloaded:\n", - "\n", - " package | build\n", - " ---------------------------|-----------------\n", - " alabaster-0.7.12 | py_0 15 KB conda-forge\n", - " alsa-lib-1.2.3 | h516909a_0 560 KB conda-forge\n", - " ambermini-16.16.0 | 7 21.7 MB omnia\n", - " ambertools-19.11 | py37h141fb67_4 74.3 MB conda-forge\n", - " argcomplete-1.12.3 | pyhd8ed1ab_2 34 KB conda-forge\n", - " argon2-cffi-21.3.0 | pyhd8ed1ab_0 15 KB conda-forge\n", - " argon2-cffi-bindings-21.2.0| py37h5e8e339_1 34 KB conda-forge\n", - " astunparse-1.6.3 | pyhd8ed1ab_0 15 KB conda-forge\n", - " async_generator-1.10 | py_0 18 KB conda-forge\n", - " attrs-21.4.0 | pyhd8ed1ab_0 49 KB conda-forge\n", - " babel-2.9.1 | pyh44b312d_0 6.2 MB conda-forge\n", - " backcall-0.2.0 | pyh9f0ad1d_0 13 KB conda-forge\n", - " backports-1.0 | py_2 4 KB conda-forge\n", - " backports.functools_lru_cache-1.6.4| pyhd8ed1ab_0 9 KB conda-forge\n", - " bleach-4.1.0 | pyhd8ed1ab_0 121 KB conda-forge\n", - " blosc-1.21.0 | h9c3ff4c_0 841 KB conda-forge\n", - " boost-1.72.0 | py37h48f8a5e_1 339 KB conda-forge\n", - " boost-cpp-1.72.0 | h312852a_5 16.3 MB conda-forge\n", - " brotli-1.0.9 | h7f98852_6 18 KB conda-forge\n", - " brotli-bin-1.0.9 | h7f98852_6 19 KB conda-forge\n", - " bson-0.5.9 | py_0 14 KB conda-forge\n", - " cairo-1.16.0 | h6cf1ce9_1008 1.5 MB conda-forge\n", - " cerberus-1.3.4 | pyhd8ed1ab_0 51 KB conda-forge\n", - " cftime-1.5.1.1 | py37hb1e94ed_1 229 KB conda-forge\n", - " clusterutils-0.3.1 | py37_1 17 KB omnia\n", - " colorama-0.4.4 | pyh9f0ad1d_0 18 KB conda-forge\n", - " cudatoolkit-11.5.0 | h36ae40a_9 789.0 MB conda-forge\n", - " curl-7.80.0 | h2574ce0_0 153 KB conda-forge\n", - " cycler-0.11.0 | pyhd8ed1ab_0 10 KB conda-forge\n", - " cython-0.29.26 | py37hcd2ae1e_0 2.2 MB conda-forge\n", - " dataclasses-0.8 | pyhc8e2a94_3 10 KB conda-forge\n", - " dbus-1.13.6 | h5008d03_3 604 KB conda-forge\n", - " debugpy-1.5.1 | py37hcd2ae1e_0 2.0 MB conda-forge\n", - " decorator-5.1.0 | pyhd8ed1ab_0 11 KB conda-forge\n", - " defusedxml-0.7.1 | pyhd8ed1ab_0 23 KB conda-forge\n", - " docopt-0.6.2 | py_1 14 KB conda-forge\n", - " docutils-0.17.1 | py37h89c1867_1 760 KB conda-forge\n", - " entrypoints-0.3 |py37hc8dfbb8_1002 13 KB conda-forge\n", - " expat-2.4.2 | h9c3ff4c_0 182 KB conda-forge\n", - " fftw-3.3.10 |nompi_h77c792f_102 6.4 MB conda-forge\n", - " flit-core-3.6.0 | pyhd8ed1ab_0 42 KB conda-forge\n", - " fontconfig-2.13.1 | hba837de_1005 357 KB conda-forge\n", - " fonttools-4.28.5 | py37h5e8e339_0 1.6 MB conda-forge\n", - " freetype-2.10.4 | h0708190_1 890 KB conda-forge\n", - " gettext-0.19.8.1 | h73d1719_1008 3.6 MB conda-forge\n", - " greenlet-1.1.2 | py37hcd2ae1e_1 90 KB conda-forge\n", - " gst-plugins-base-1.18.5 | hf529b03_2 2.6 MB conda-forge\n", - " gstreamer-1.18.5 | h9f60fe5_2 2.0 MB conda-forge\n", - " hdf4-4.2.15 | h10796ff_3 950 KB conda-forge\n", - " hdf5-1.10.5 |nompi_h5b725eb_1114 3.1 MB conda-forge\n", - " imagesize-1.3.0 | pyhd8ed1ab_0 9 KB conda-forge\n", - " importlib-metadata-4.10.0 | py37h89c1867_0 32 KB conda-forge\n", - " importlib_metadata-4.10.0 | hd8ed1ab_0 4 KB conda-forge\n", - " importlib_resources-5.4.0 | pyhd8ed1ab_0 21 KB conda-forge\n", - " ipykernel-6.6.0 | py37h6531663_0 177 KB conda-forge\n", - " ipython-7.30.1 | py37h89c1867_0 1.1 MB conda-forge\n", - " ipython_genutils-0.2.0 | py_1 21 KB conda-forge\n", - " ipywidgets-7.6.5 | pyhd8ed1ab_0 101 KB conda-forge\n", - " jbig-2.1 | h7f98852_2003 43 KB conda-forge\n", - " jedi-0.18.1 | py37h89c1867_0 997 KB conda-forge\n", - " jinja2-3.0.3 | pyhd8ed1ab_0 99 KB conda-forge\n", - " jpeg-9d | h516909a_0 266 KB conda-forge\n", - " jsonschema-4.3.2 | pyhd8ed1ab_0 56 KB conda-forge\n", - " jupyter-1.0.0 | py37h89c1867_7 6 KB conda-forge\n", - " jupyter_client-7.1.0 | pyhd8ed1ab_0 89 KB conda-forge\n", - " jupyter_console-6.4.0 | pyhd8ed1ab_0 22 KB conda-forge\n", - " jupyter_core-4.9.1 | py37h89c1867_1 80 KB conda-forge\n", - " jupyterlab_pygments-0.1.2 | pyh9f0ad1d_0 8 KB conda-forge\n", - " jupyterlab_widgets-1.0.2 | pyhd8ed1ab_0 130 KB conda-forge\n", - " kiwisolver-1.3.2 | py37h2527ec5_1 78 KB conda-forge\n", - " krb5-1.19.2 | hcc1bbae_3 1.4 MB conda-forge\n", - " latexcodec-2.0.1 | pyh9f0ad1d_0 18 KB conda-forge\n", - " lcms2-2.12 | hddcbb42_0 443 KB conda-forge\n", - " lerc-3.0 | h9c3ff4c_0 216 KB conda-forge\n", - " libarchive-3.5.2 | hccf745f_1 1.6 MB conda-forge\n", - " libbrotlicommon-1.0.9 | h7f98852_6 65 KB conda-forge\n", - " libbrotlidec-1.0.9 | h7f98852_6 33 KB conda-forge\n", - " libbrotlienc-1.0.9 | h7f98852_6 286 KB conda-forge\n", - " libclang-11.1.0 |default_ha53f305_1 19.2 MB conda-forge\n", - " libcurl-7.80.0 | h2574ce0_0 337 KB conda-forge\n", - " libdeflate-1.8 | h7f98852_0 67 KB conda-forge\n", - " libevent-2.1.10 | h9b69904_4 1.1 MB conda-forge\n", - " libgcc-7.2.0 | h69d50b8_2 304 KB conda-forge\n", - " libgfortran-ng-7.5.0 | h14aa051_19 22 KB conda-forge\n", - " libgfortran4-7.5.0 | h14aa051_19 1.3 MB conda-forge\n", - " libglib-2.70.2 | h174f98d_1 3.1 MB conda-forge\n", - " libllvm10-10.0.1 | he513fc3_3 26.4 MB conda-forge\n", - " libllvm11-11.1.0 | hf817b99_2 29.1 MB conda-forge\n", - " libnetcdf-4.7.3 |nompi_h9f9fd6a_101 1.3 MB conda-forge\n", - " libnghttp2-1.43.0 | h812cca2_1 790 KB conda-forge\n", - " libogg-1.3.4 | h7f98852_1 206 KB conda-forge\n", - " libopus-1.3.1 | h7f98852_1 255 KB conda-forge\n", - " libpng-1.6.37 | hed695b0_2 359 KB conda-forge\n", - " libpq-13.5 | hd57d9b9_1 2.8 MB conda-forge\n", - " libsodium-1.0.18 | h516909a_1 366 KB conda-forge\n", - " libssh2-1.10.0 | ha56f1ee_2 233 KB conda-forge\n", - " libtiff-4.3.0 | h6f004c6_2 614 KB conda-forge\n", - " libuuid-2.32.1 | h14c3975_1000 26 KB conda-forge\n", - " libvorbis-1.3.7 | he1b5a44_0 287 KB conda-forge\n", - " libwebp-base-1.2.1 | h7f98852_0 845 KB conda-forge\n", - " libxcb-1.13 | h7f98852_1004 391 KB conda-forge\n", - " libxkbcommon-1.0.3 | he3ba5ed_0 581 KB conda-forge\n", - " llvmlite-0.36.0 | py37h9d7f4d0_0 2.7 MB conda-forge\n", - " markupsafe-2.0.1 | py37h5e8e339_1 22 KB conda-forge\n", - " matplotlib-3.5.1 | py37h89c1867_0 6 KB conda-forge\n", - " matplotlib-base-3.5.1 | py37h1058ff1_0 7.4 MB conda-forge\n", - " matplotlib-inline-0.1.3 | pyhd8ed1ab_0 11 KB conda-forge\n", - " mdtraj-1.9.7 | py37h527cbdb_1 1.8 MB conda-forge\n", - " mistune-0.8.4 |py37h5e8e339_1005 54 KB conda-forge\n", - " mock-4.0.3 | py37h89c1867_2 51 KB conda-forge\n", - " mpi-1.0 | mpich 4 KB conda-forge\n", - " mpich-3.4.3 | h846660c_100 10.4 MB conda-forge\n", - " mpiplus-v0.0.1 |py37hc8dfbb8_1002 23 KB conda-forge\n", - " msgpack-python-1.0.3 | py37h2527ec5_0 91 KB conda-forge\n", - " munkres-1.1.4 | pyh9f0ad1d_0 12 KB conda-forge\n", - " mysql-common-8.0.27 | ha770c72_3 1.8 MB conda-forge\n", - " mysql-libs-8.0.27 | hfa10184_3 1.9 MB conda-forge\n", - " nbclient-0.5.9 | pyhd8ed1ab_0 63 KB conda-forge\n", - " nbconvert-6.3.0 | py37h89c1867_1 535 KB conda-forge\n", - " nbformat-5.1.3 | pyhd8ed1ab_0 47 KB conda-forge\n", - " nest-asyncio-1.5.4 | pyhd8ed1ab_0 9 KB conda-forge\n", - " netcdf-fortran-4.5.2 |nompi_h09cde99_103 1.2 MB conda-forge\n", - " netcdf4-1.5.3 |nompi_py37hd35fb8e_102 546 KB conda-forge\n", - " networkx-2.6.3 | pyhd8ed1ab_1 1.5 MB conda-forge\n", - " nomkl-1.0 | h5ca1d4c_0 4 KB conda-forge\n", - " nose-1.3.7 |py37hc8dfbb8_1004 212 KB conda-forge\n", - " notebook-6.4.6 | pyha770c72_0 6.2 MB conda-forge\n", - " nspr-4.32 | h9c3ff4c_1 233 KB conda-forge\n", - " nss-3.73 | hb5efdd6_0 2.1 MB conda-forge\n", - " numba-0.53.1 | py37hb11d6e1_1 3.7 MB conda-forge\n", - " numexpr-2.8.0 | py37hfe5f03c_100 135 KB conda-forge\n", - " numpydoc-1.1.0 | py_1 42 KB conda-forge\n", - " ocl-icd-2.3.1 | h7f98852_0 119 KB conda-forge\n", - " ocl-icd-system-1.0.0 | 1 4 KB conda-forge\n", - " olefile-0.46 | pyh9f0ad1d_1 32 KB conda-forge\n", - " openforcefield-0.6.0 | py37_1 29.7 MB omnia\n", - " openforcefields-2.0.0rc.2 | py_0 91 KB omnia\n", - " openjpeg-2.4.0 | hb52868f_1 444 KB conda-forge\n", - " openmm-7.7.0 | py37hfbac998_0 11.1 MB conda-forge\n", - " openmmtools-0.20.3 | pyhd8ed1ab_0 8.3 MB conda-forge\n", - " openmoltools-0.8.8 | pyhd8ed1ab_1 4.3 MB conda-forge\n", - " openssl-1.1.1l | h7f98852_0 2.1 MB conda-forge\n", - " packaging-21.3 | pyhd8ed1ab_0 36 KB conda-forge\n", - " packmol-1!18.013 | 0 117 KB omnia\n", - " pandas-1.3.5 | py37he8f5f7f_0 12.7 MB conda-forge\n", - " pandoc-2.16.2 | h7f98852_0 12.6 MB conda-forge\n", - " pandocfilters-1.5.0 | pyhd8ed1ab_0 11 KB conda-forge\n", - " parmed-3.4.3 | py37hcd2ae1e_1 977 KB conda-forge\n", - " parso-0.8.3 | pyhd8ed1ab_0 69 KB conda-forge\n", - " pcre-8.45 | h9c3ff4c_0 253 KB conda-forge\n", - " pdbfixer-1.8.1 | pyh6c4a22f_0 498 KB conda-forge\n", - " perl-5.32.1 | 1_h7f98852_perl5 14.4 MB conda-forge\n", - " pexpect-4.8.0 | py37hc8dfbb8_1 79 KB conda-forge\n", - " pickleshare-0.7.5 |py37hc8dfbb8_1002 13 KB conda-forge\n", - " pillow-8.4.0 | py37h0f21c89_0 706 KB conda-forge\n", - " pixman-0.40.0 | h36c2ea0_0 627 KB conda-forge\n", - " prometheus_client-0.12.0 | pyhd8ed1ab_0 47 KB conda-forge\n", - " prompt-toolkit-3.0.24 | pyha770c72_0 249 KB conda-forge\n", - " prompt_toolkit-3.0.24 | hd8ed1ab_0 5 KB conda-forge\n", - " pthread-stubs-0.4 | h36c2ea0_1001 5 KB conda-forge\n", - " ptyprocess-0.7.0 | pyhd3deb0d_0 16 KB conda-forge\n", - " pybtex-0.24.0 | py37h89c1867_1 310 KB conda-forge\n", - " pybtex-docutils-1.0.1 | py37h89c1867_1 10 KB conda-forge\n", - " pycairo-1.20.1 | py37hfff247e_1 83 KB conda-forge\n", - " pygments-2.11.1 | pyhd8ed1ab_0 796 KB conda-forge\n", - " pymbar-3.0.3 | py37h3010b51_3 113 KB conda-forge\n", - " pyparsing-3.0.6 | pyhd8ed1ab_0 79 KB conda-forge\n", - " pyqt-5.12.3 | py37h89c1867_8 22 KB conda-forge\n", - " pyqt-impl-5.12.3 | py37hac37412_8 5.9 MB conda-forge\n", - " pyqt5-sip-4.19.18 | py37hcd2ae1e_8 310 KB conda-forge\n", - " pyqtchart-5.12 | py37he336c9b_8 257 KB conda-forge\n", - " pyqtwebengine-5.12.1 | py37he336c9b_8 175 KB conda-forge\n", - " pyrsistent-0.18.0 | py37h5e8e339_0 90 KB conda-forge\n", - " pytables-3.6.1 | py37h9f153d1_1 1.5 MB conda-forge\n", - " python-3.7.10 |hb7a2778_104_cpython 57.3 MB conda-forge\n", - " python-dateutil-2.8.2 | pyhd8ed1ab_0 240 KB conda-forge\n", - " pytz-2021.3 | pyhd8ed1ab_0 242 KB conda-forge\n", - " pyyaml-6.0 | py37h5e8e339_3 187 KB conda-forge\n", - " pyzmq-22.3.0 | py37h336d617_1 501 KB conda-forge\n", - " qt-5.12.9 | hda022c4_4 99.5 MB conda-forge\n", - " qtconsole-5.2.2 | pyhd8ed1ab_1 5 KB conda-forge\n", - " qtconsole-base-5.2.2 | pyhd8ed1ab_1 90 KB conda-forge\n", - " qtpy-2.0.0 | pyhd8ed1ab_0 38 KB conda-forge\n", - " rdkit-2020.09.3 | py37h400b6df_0 26.2 MB conda-forge\n", - " reportlab-3.5.68 | py37h69800bb_1 2.4 MB conda-forge\n", - " scipy-1.7.3 | py37hf2a6cf1_0 21.8 MB conda-forge\n", - " send2trash-1.8.0 | pyhd8ed1ab_0 17 KB conda-forge\n", - " smirnoff99frosst-1.1.0 | py37_1 72 KB omnia\n", - " snappy-1.1.8 | he1b5a44_3 32 KB conda-forge\n", - " snowballstemmer-2.2.0 | pyhd8ed1ab_0 57 KB conda-forge\n", - " solvationtoolkit-0.4.3 | py37_0 25 KB omnia\n", - " sphinx-4.3.2 | pyh6c4a22f_0 1.5 MB conda-forge\n", - " sphinxcontrib-applehelp-1.0.2| py_0 28 KB conda-forge\n", - " sphinxcontrib-bibtex-2.4.1 | pyhd8ed1ab_0 28 KB conda-forge\n", - " sphinxcontrib-devhelp-1.0.2| py_0 22 KB conda-forge\n", - " sphinxcontrib-htmlhelp-2.0.0| pyhd8ed1ab_0 31 KB conda-forge\n", - " sphinxcontrib-jsmath-1.0.1 | py_0 7 KB conda-forge\n", - " sphinxcontrib-qthelp-1.0.3 | py_0 25 KB conda-forge\n", - " sphinxcontrib-serializinghtml-1.1.5| pyhd8ed1ab_1 27 KB conda-forge\n", - " sqlalchemy-1.4.29 | py37h5e8e339_0 2.3 MB conda-forge\n", - " terminado-0.12.1 | py37h89c1867_1 28 KB conda-forge\n", - " testpath-0.5.0 | pyhd8ed1ab_0 86 KB conda-forge\n", - " toml-0.10.2 | pyhd8ed1ab_0 18 KB conda-forge\n", - " tornado-6.1 | py37h5e8e339_2 642 KB conda-forge\n", - " traitlets-5.1.1 | pyhd8ed1ab_0 82 KB conda-forge\n", - " typing_extensions-4.0.1 | pyha770c72_0 26 KB conda-forge\n", - " unicodedata2-14.0.0 | py37h5e8e339_0 501 KB conda-forge\n", - " wcwidth-0.2.5 | pyh9f0ad1d_2 33 KB conda-forge\n", - " webencodings-0.5.1 | py_1 12 KB conda-forge\n", - " widgetsnbextension-3.5.2 | py37h89c1867_1 1.3 MB conda-forge\n", - " xmltodict-0.12.0 | py_0 11 KB conda-forge\n", - " xorg-kbproto-1.0.7 | h14c3975_1002 26 KB conda-forge\n", - " xorg-libice-1.0.10 | h516909a_0 57 KB conda-forge\n", - " xorg-libsm-1.2.3 | hd9c2040_1000 26 KB conda-forge\n", - " xorg-libx11-1.7.2 | h7f98852_0 941 KB conda-forge\n", - " xorg-libxau-1.0.9 | h14c3975_0 13 KB conda-forge\n", - " xorg-libxdmcp-1.1.3 | h516909a_0 18 KB conda-forge\n", - " xorg-libxext-1.3.4 | h7f98852_1 54 KB conda-forge\n", - " xorg-libxrender-0.9.10 | h7f98852_1003 32 KB conda-forge\n", - " xorg-libxt-1.2.1 | h7f98852_2 375 KB conda-forge\n", - " xorg-renderproto-0.11.1 | h14c3975_1002 8 KB conda-forge\n", - " xorg-xextproto-7.3.0 | h14c3975_1002 27 KB conda-forge\n", - " xorg-xproto-7.0.31 | h14c3975_1007 72 KB conda-forge\n", - " yank-0.25.2 | py37_2 2.3 MB omnia\n", - " zeromq-4.3.4 | h9c3ff4c_1 351 KB conda-forge\n", - " zipp-3.6.0 | pyhd8ed1ab_0 12 KB conda-forge\n", - " ------------------------------------------------------------\n", - " Total: 1.37 GB\n", - "\n", - "The following NEW packages will be INSTALLED:\n", - "\n", - " alabaster conda-forge/noarch::alabaster-0.7.12-py_0\n", - " alsa-lib conda-forge/linux-64::alsa-lib-1.2.3-h516909a_0\n", - " ambermini omnia/linux-64::ambermini-16.16.0-7\n", - " ambertools conda-forge/linux-64::ambertools-19.11-py37h141fb67_4\n", - " argcomplete conda-forge/noarch::argcomplete-1.12.3-pyhd8ed1ab_2\n", - " argon2-cffi conda-forge/noarch::argon2-cffi-21.3.0-pyhd8ed1ab_0\n", - " argon2-cffi-bindi~ conda-forge/linux-64::argon2-cffi-bindings-21.2.0-py37h5e8e339_1\n", - " astunparse conda-forge/noarch::astunparse-1.6.3-pyhd8ed1ab_0\n", - " async_generator conda-forge/noarch::async_generator-1.10-py_0\n", - " attrs conda-forge/noarch::attrs-21.4.0-pyhd8ed1ab_0\n", - " babel conda-forge/noarch::babel-2.9.1-pyh44b312d_0\n", - " backcall conda-forge/noarch::backcall-0.2.0-pyh9f0ad1d_0\n", - " backports conda-forge/noarch::backports-1.0-py_2\n", - " backports.functoo~ conda-forge/noarch::backports.functools_lru_cache-1.6.4-pyhd8ed1ab_0\n", - " bleach conda-forge/noarch::bleach-4.1.0-pyhd8ed1ab_0\n", - " blosc conda-forge/linux-64::blosc-1.21.0-h9c3ff4c_0\n", - " boost conda-forge/linux-64::boost-1.72.0-py37h48f8a5e_1\n", - " boost-cpp conda-forge/linux-64::boost-cpp-1.72.0-h312852a_5\n", - " brotli conda-forge/linux-64::brotli-1.0.9-h7f98852_6\n", - " brotli-bin conda-forge/linux-64::brotli-bin-1.0.9-h7f98852_6\n", - " bson conda-forge/noarch::bson-0.5.9-py_0\n", - " cairo conda-forge/linux-64::cairo-1.16.0-h6cf1ce9_1008\n", - " cerberus conda-forge/noarch::cerberus-1.3.4-pyhd8ed1ab_0\n", - " cftime conda-forge/linux-64::cftime-1.5.1.1-py37hb1e94ed_1\n", - " clusterutils omnia/linux-64::clusterutils-0.3.1-py37_1\n", - " colorama conda-forge/noarch::colorama-0.4.4-pyh9f0ad1d_0\n", - " cudatoolkit conda-forge/linux-64::cudatoolkit-11.5.0-h36ae40a_9\n", - " curl conda-forge/linux-64::curl-7.80.0-h2574ce0_0\n", - " cycler conda-forge/noarch::cycler-0.11.0-pyhd8ed1ab_0\n", - " cython conda-forge/linux-64::cython-0.29.26-py37hcd2ae1e_0\n", - " dataclasses conda-forge/noarch::dataclasses-0.8-pyhc8e2a94_3\n", - " dbus conda-forge/linux-64::dbus-1.13.6-h5008d03_3\n", - " debugpy conda-forge/linux-64::debugpy-1.5.1-py37hcd2ae1e_0\n", - " decorator conda-forge/noarch::decorator-5.1.0-pyhd8ed1ab_0\n", - " defusedxml conda-forge/noarch::defusedxml-0.7.1-pyhd8ed1ab_0\n", - " docopt conda-forge/noarch::docopt-0.6.2-py_1\n", - " docutils conda-forge/linux-64::docutils-0.17.1-py37h89c1867_1\n", - " entrypoints conda-forge/linux-64::entrypoints-0.3-py37hc8dfbb8_1002\n", - " expat conda-forge/linux-64::expat-2.4.2-h9c3ff4c_0\n", - " fftw conda-forge/linux-64::fftw-3.3.10-nompi_h77c792f_102\n", - " flit-core conda-forge/noarch::flit-core-3.6.0-pyhd8ed1ab_0\n", - " fontconfig conda-forge/linux-64::fontconfig-2.13.1-hba837de_1005\n", - " fonttools conda-forge/linux-64::fonttools-4.28.5-py37h5e8e339_0\n", - " freetype conda-forge/linux-64::freetype-2.10.4-h0708190_1\n", - " gettext conda-forge/linux-64::gettext-0.19.8.1-h73d1719_1008\n", - " greenlet conda-forge/linux-64::greenlet-1.1.2-py37hcd2ae1e_1\n", - " gst-plugins-base conda-forge/linux-64::gst-plugins-base-1.18.5-hf529b03_2\n", - " gstreamer conda-forge/linux-64::gstreamer-1.18.5-h9f60fe5_2\n", - " hdf4 conda-forge/linux-64::hdf4-4.2.15-h10796ff_3\n", - " hdf5 conda-forge/linux-64::hdf5-1.10.5-nompi_h5b725eb_1114\n", - " imagesize conda-forge/noarch::imagesize-1.3.0-pyhd8ed1ab_0\n", - " importlib-metadata conda-forge/linux-64::importlib-metadata-4.10.0-py37h89c1867_0\n", - " importlib_metadata conda-forge/noarch::importlib_metadata-4.10.0-hd8ed1ab_0\n", - " importlib_resourc~ conda-forge/noarch::importlib_resources-5.4.0-pyhd8ed1ab_0\n", - " ipykernel conda-forge/linux-64::ipykernel-6.6.0-py37h6531663_0\n", - " ipython conda-forge/linux-64::ipython-7.30.1-py37h89c1867_0\n", - " ipython_genutils conda-forge/noarch::ipython_genutils-0.2.0-py_1\n", - " ipywidgets conda-forge/noarch::ipywidgets-7.6.5-pyhd8ed1ab_0\n", - " jbig conda-forge/linux-64::jbig-2.1-h7f98852_2003\n", - " jedi conda-forge/linux-64::jedi-0.18.1-py37h89c1867_0\n", - " jinja2 conda-forge/noarch::jinja2-3.0.3-pyhd8ed1ab_0\n", - " jpeg conda-forge/linux-64::jpeg-9d-h516909a_0\n", - " jsonschema conda-forge/noarch::jsonschema-4.3.2-pyhd8ed1ab_0\n", - " jupyter conda-forge/linux-64::jupyter-1.0.0-py37h89c1867_7\n", - " jupyter_client conda-forge/noarch::jupyter_client-7.1.0-pyhd8ed1ab_0\n", - " jupyter_console conda-forge/noarch::jupyter_console-6.4.0-pyhd8ed1ab_0\n", - " jupyter_core conda-forge/linux-64::jupyter_core-4.9.1-py37h89c1867_1\n", - " jupyterlab_pygmen~ conda-forge/noarch::jupyterlab_pygments-0.1.2-pyh9f0ad1d_0\n", - " jupyterlab_widgets conda-forge/noarch::jupyterlab_widgets-1.0.2-pyhd8ed1ab_0\n", - " kiwisolver conda-forge/linux-64::kiwisolver-1.3.2-py37h2527ec5_1\n", - " latexcodec conda-forge/noarch::latexcodec-2.0.1-pyh9f0ad1d_0\n", - " lcms2 conda-forge/linux-64::lcms2-2.12-hddcbb42_0\n", - " lerc conda-forge/linux-64::lerc-3.0-h9c3ff4c_0\n", - " libbrotlicommon conda-forge/linux-64::libbrotlicommon-1.0.9-h7f98852_6\n", - " libbrotlidec conda-forge/linux-64::libbrotlidec-1.0.9-h7f98852_6\n", - " libbrotlienc conda-forge/linux-64::libbrotlienc-1.0.9-h7f98852_6\n", - " libclang conda-forge/linux-64::libclang-11.1.0-default_ha53f305_1\n", - " libdeflate conda-forge/linux-64::libdeflate-1.8-h7f98852_0\n", - " libevent conda-forge/linux-64::libevent-2.1.10-h9b69904_4\n", - " libgcc conda-forge/linux-64::libgcc-7.2.0-h69d50b8_2\n", - " libgfortran4 conda-forge/linux-64::libgfortran4-7.5.0-h14aa051_19\n", - " libglib conda-forge/linux-64::libglib-2.70.2-h174f98d_1\n", - " libllvm10 conda-forge/linux-64::libllvm10-10.0.1-he513fc3_3\n", - " libllvm11 conda-forge/linux-64::libllvm11-11.1.0-hf817b99_2\n", - " libnetcdf conda-forge/linux-64::libnetcdf-4.7.3-nompi_h9f9fd6a_101\n", - " libogg conda-forge/linux-64::libogg-1.3.4-h7f98852_1\n", - " libopus conda-forge/linux-64::libopus-1.3.1-h7f98852_1\n", - " libpng conda-forge/linux-64::libpng-1.6.37-hed695b0_2\n", - " libpq conda-forge/linux-64::libpq-13.5-hd57d9b9_1\n", - " libsodium conda-forge/linux-64::libsodium-1.0.18-h516909a_1\n", - " libtiff conda-forge/linux-64::libtiff-4.3.0-h6f004c6_2\n", - " libuuid conda-forge/linux-64::libuuid-2.32.1-h14c3975_1000\n", - " libvorbis conda-forge/linux-64::libvorbis-1.3.7-he1b5a44_0\n", - " libwebp-base conda-forge/linux-64::libwebp-base-1.2.1-h7f98852_0\n", - " libxcb conda-forge/linux-64::libxcb-1.13-h7f98852_1004\n", - " libxkbcommon conda-forge/linux-64::libxkbcommon-1.0.3-he3ba5ed_0\n", - " llvmlite conda-forge/linux-64::llvmlite-0.36.0-py37h9d7f4d0_0\n", - " markupsafe conda-forge/linux-64::markupsafe-2.0.1-py37h5e8e339_1\n", - " matplotlib conda-forge/linux-64::matplotlib-3.5.1-py37h89c1867_0\n", - " matplotlib-base conda-forge/linux-64::matplotlib-base-3.5.1-py37h1058ff1_0\n", - " matplotlib-inline conda-forge/noarch::matplotlib-inline-0.1.3-pyhd8ed1ab_0\n", - " mdtraj conda-forge/linux-64::mdtraj-1.9.7-py37h527cbdb_1\n", - " mistune conda-forge/linux-64::mistune-0.8.4-py37h5e8e339_1005\n", - " mock conda-forge/linux-64::mock-4.0.3-py37h89c1867_2\n", - " mpi conda-forge/linux-64::mpi-1.0-mpich\n", - " mpich conda-forge/linux-64::mpich-3.4.3-h846660c_100\n", - " mpiplus conda-forge/linux-64::mpiplus-v0.0.1-py37hc8dfbb8_1002\n", - " msgpack-python conda-forge/linux-64::msgpack-python-1.0.3-py37h2527ec5_0\n", - " munkres conda-forge/noarch::munkres-1.1.4-pyh9f0ad1d_0\n", - " mysql-common conda-forge/linux-64::mysql-common-8.0.27-ha770c72_3\n", - " mysql-libs conda-forge/linux-64::mysql-libs-8.0.27-hfa10184_3\n", - " nbclient conda-forge/noarch::nbclient-0.5.9-pyhd8ed1ab_0\n", - " nbconvert conda-forge/linux-64::nbconvert-6.3.0-py37h89c1867_1\n", - " nbformat conda-forge/noarch::nbformat-5.1.3-pyhd8ed1ab_0\n", - " nest-asyncio conda-forge/noarch::nest-asyncio-1.5.4-pyhd8ed1ab_0\n", - " netcdf-fortran conda-forge/linux-64::netcdf-fortran-4.5.2-nompi_h09cde99_103\n", - " netcdf4 conda-forge/linux-64::netcdf4-1.5.3-nompi_py37hd35fb8e_102\n", - " networkx conda-forge/noarch::networkx-2.6.3-pyhd8ed1ab_1\n", - " nomkl conda-forge/noarch::nomkl-1.0-h5ca1d4c_0\n", - " nose conda-forge/linux-64::nose-1.3.7-py37hc8dfbb8_1004\n", - " notebook conda-forge/noarch::notebook-6.4.6-pyha770c72_0\n", - " nspr conda-forge/linux-64::nspr-4.32-h9c3ff4c_1\n", - " nss conda-forge/linux-64::nss-3.73-hb5efdd6_0\n", - " numba conda-forge/linux-64::numba-0.53.1-py37hb11d6e1_1\n", - " numexpr conda-forge/linux-64::numexpr-2.8.0-py37hfe5f03c_100\n", - " numpydoc conda-forge/noarch::numpydoc-1.1.0-py_1\n", - " ocl-icd conda-forge/linux-64::ocl-icd-2.3.1-h7f98852_0\n", - " ocl-icd-system conda-forge/linux-64::ocl-icd-system-1.0.0-1\n", - " olefile conda-forge/noarch::olefile-0.46-pyh9f0ad1d_1\n", - " openforcefield omnia/linux-64::openforcefield-0.6.0-py37_1\n", - " openforcefields omnia/noarch::openforcefields-2.0.0rc.2-py_0\n", - " openjpeg conda-forge/linux-64::openjpeg-2.4.0-hb52868f_1\n", - " openmm conda-forge/linux-64::openmm-7.7.0-py37hfbac998_0\n", - " openmmtools conda-forge/noarch::openmmtools-0.20.3-pyhd8ed1ab_0\n", - " openmoltools conda-forge/noarch::openmoltools-0.8.8-pyhd8ed1ab_1\n", - " packaging conda-forge/noarch::packaging-21.3-pyhd8ed1ab_0\n", - " packmol omnia/linux-64::packmol-1!18.013-0\n", - " pandas conda-forge/linux-64::pandas-1.3.5-py37he8f5f7f_0\n", - " pandoc conda-forge/linux-64::pandoc-2.16.2-h7f98852_0\n", - " pandocfilters conda-forge/noarch::pandocfilters-1.5.0-pyhd8ed1ab_0\n", - " parmed conda-forge/linux-64::parmed-3.4.3-py37hcd2ae1e_1\n", - " parso conda-forge/noarch::parso-0.8.3-pyhd8ed1ab_0\n", - " pcre conda-forge/linux-64::pcre-8.45-h9c3ff4c_0\n", - " pdbfixer conda-forge/noarch::pdbfixer-1.8.1-pyh6c4a22f_0\n", - " perl conda-forge/linux-64::perl-5.32.1-1_h7f98852_perl5\n", - " pexpect conda-forge/linux-64::pexpect-4.8.0-py37hc8dfbb8_1\n", - " pickleshare conda-forge/linux-64::pickleshare-0.7.5-py37hc8dfbb8_1002\n", - " pillow conda-forge/linux-64::pillow-8.4.0-py37h0f21c89_0\n", - " pixman conda-forge/linux-64::pixman-0.40.0-h36c2ea0_0\n", - " prometheus_client conda-forge/noarch::prometheus_client-0.12.0-pyhd8ed1ab_0\n", - " prompt-toolkit conda-forge/noarch::prompt-toolkit-3.0.24-pyha770c72_0\n", - " prompt_toolkit conda-forge/noarch::prompt_toolkit-3.0.24-hd8ed1ab_0\n", - " pthread-stubs conda-forge/linux-64::pthread-stubs-0.4-h36c2ea0_1001\n", - " ptyprocess conda-forge/noarch::ptyprocess-0.7.0-pyhd3deb0d_0\n", - " pybtex conda-forge/linux-64::pybtex-0.24.0-py37h89c1867_1\n", - " pybtex-docutils conda-forge/linux-64::pybtex-docutils-1.0.1-py37h89c1867_1\n", - " pycairo conda-forge/linux-64::pycairo-1.20.1-py37hfff247e_1\n", - " pygments conda-forge/noarch::pygments-2.11.1-pyhd8ed1ab_0\n", - " pymbar conda-forge/linux-64::pymbar-3.0.3-py37h3010b51_3\n", - " pyparsing conda-forge/noarch::pyparsing-3.0.6-pyhd8ed1ab_0\n", - " pyqt conda-forge/linux-64::pyqt-5.12.3-py37h89c1867_8\n", - " pyqt-impl conda-forge/linux-64::pyqt-impl-5.12.3-py37hac37412_8\n", - " pyqt5-sip conda-forge/linux-64::pyqt5-sip-4.19.18-py37hcd2ae1e_8\n", - " pyqtchart conda-forge/linux-64::pyqtchart-5.12-py37he336c9b_8\n", - " pyqtwebengine conda-forge/linux-64::pyqtwebengine-5.12.1-py37he336c9b_8\n", - " pyrsistent conda-forge/linux-64::pyrsistent-0.18.0-py37h5e8e339_0\n", - " pytables conda-forge/linux-64::pytables-3.6.1-py37h9f153d1_1\n", - " python-dateutil conda-forge/noarch::python-dateutil-2.8.2-pyhd8ed1ab_0\n", - " pytz conda-forge/noarch::pytz-2021.3-pyhd8ed1ab_0\n", - " pyyaml conda-forge/linux-64::pyyaml-6.0-py37h5e8e339_3\n", - " pyzmq conda-forge/linux-64::pyzmq-22.3.0-py37h336d617_1\n", - " qt conda-forge/linux-64::qt-5.12.9-hda022c4_4\n", - " qtconsole conda-forge/noarch::qtconsole-5.2.2-pyhd8ed1ab_1\n", - " qtconsole-base conda-forge/noarch::qtconsole-base-5.2.2-pyhd8ed1ab_1\n", - " qtpy conda-forge/noarch::qtpy-2.0.0-pyhd8ed1ab_0\n", - " rdkit conda-forge/linux-64::rdkit-2020.09.3-py37h400b6df_0\n", - " reportlab conda-forge/linux-64::reportlab-3.5.68-py37h69800bb_1\n", - " scipy conda-forge/linux-64::scipy-1.7.3-py37hf2a6cf1_0\n", - " send2trash conda-forge/noarch::send2trash-1.8.0-pyhd8ed1ab_0\n", - " smirnoff99frosst omnia/linux-64::smirnoff99frosst-1.1.0-py37_1\n", - " snappy conda-forge/linux-64::snappy-1.1.8-he1b5a44_3\n", - " snowballstemmer conda-forge/noarch::snowballstemmer-2.2.0-pyhd8ed1ab_0\n", - " solvationtoolkit omnia/linux-64::solvationtoolkit-0.4.3-py37_0\n", - " sphinx conda-forge/noarch::sphinx-4.3.2-pyh6c4a22f_0\n", - " sphinxcontrib-app~ conda-forge/noarch::sphinxcontrib-applehelp-1.0.2-py_0\n", - " sphinxcontrib-bib~ conda-forge/noarch::sphinxcontrib-bibtex-2.4.1-pyhd8ed1ab_0\n", - " sphinxcontrib-dev~ conda-forge/noarch::sphinxcontrib-devhelp-1.0.2-py_0\n", - " sphinxcontrib-htm~ conda-forge/noarch::sphinxcontrib-htmlhelp-2.0.0-pyhd8ed1ab_0\n", - " sphinxcontrib-jsm~ conda-forge/noarch::sphinxcontrib-jsmath-1.0.1-py_0\n", - " sphinxcontrib-qth~ conda-forge/noarch::sphinxcontrib-qthelp-1.0.3-py_0\n", - " sphinxcontrib-ser~ conda-forge/noarch::sphinxcontrib-serializinghtml-1.1.5-pyhd8ed1ab_1\n", - " sqlalchemy conda-forge/linux-64::sqlalchemy-1.4.29-py37h5e8e339_0\n", - " terminado conda-forge/linux-64::terminado-0.12.1-py37h89c1867_1\n", - " testpath conda-forge/noarch::testpath-0.5.0-pyhd8ed1ab_0\n", - " toml conda-forge/noarch::toml-0.10.2-pyhd8ed1ab_0\n", - " tornado conda-forge/linux-64::tornado-6.1-py37h5e8e339_2\n", - " traitlets conda-forge/noarch::traitlets-5.1.1-pyhd8ed1ab_0\n", - " typing_extensions conda-forge/noarch::typing_extensions-4.0.1-pyha770c72_0\n", - " unicodedata2 conda-forge/linux-64::unicodedata2-14.0.0-py37h5e8e339_0\n", - " wcwidth conda-forge/noarch::wcwidth-0.2.5-pyh9f0ad1d_2\n", - " webencodings conda-forge/noarch::webencodings-0.5.1-py_1\n", - " widgetsnbextension conda-forge/linux-64::widgetsnbextension-3.5.2-py37h89c1867_1\n", - " xmltodict conda-forge/noarch::xmltodict-0.12.0-py_0\n", - " xorg-kbproto conda-forge/linux-64::xorg-kbproto-1.0.7-h14c3975_1002\n", - " xorg-libice conda-forge/linux-64::xorg-libice-1.0.10-h516909a_0\n", - " xorg-libsm conda-forge/linux-64::xorg-libsm-1.2.3-hd9c2040_1000\n", - " xorg-libx11 conda-forge/linux-64::xorg-libx11-1.7.2-h7f98852_0\n", - " xorg-libxau conda-forge/linux-64::xorg-libxau-1.0.9-h14c3975_0\n", - " xorg-libxdmcp conda-forge/linux-64::xorg-libxdmcp-1.1.3-h516909a_0\n", - " xorg-libxext conda-forge/linux-64::xorg-libxext-1.3.4-h7f98852_1\n", - " xorg-libxrender conda-forge/linux-64::xorg-libxrender-0.9.10-h7f98852_1003\n", - " xorg-libxt conda-forge/linux-64::xorg-libxt-1.2.1-h7f98852_2\n", - " xorg-renderproto conda-forge/linux-64::xorg-renderproto-0.11.1-h14c3975_1002\n", - " xorg-xextproto conda-forge/linux-64::xorg-xextproto-7.3.0-h14c3975_1002\n", - " xorg-xproto conda-forge/linux-64::xorg-xproto-7.0.31-h14c3975_1007\n", - " yank omnia/linux-64::yank-0.25.2-py37_2\n", - " zeromq conda-forge/linux-64::zeromq-4.3.4-h9c3ff4c_1\n", - " zipp conda-forge/noarch::zipp-3.6.0-pyhd8ed1ab_0\n", - "\n", - "The following packages will be DOWNGRADED:\n", - "\n", - " krb5 1.19.2-h48eae69_3 --> 1.19.2-hcc1bbae_3\n", - " libarchive 3.5.2-hed592e5_1 --> 3.5.2-hccf745f_1\n", - " libcurl 7.80.0-h494985f_1 --> 7.80.0-h2574ce0_0\n", - " libgfortran-ng 11.2.0-h69a702a_11 --> 7.5.0-h14aa051_19\n", - " libnghttp2 1.43.0-ha19adfc_1 --> 1.43.0-h812cca2_1\n", - " libssh2 1.10.0-ha35d2d1_2 --> 1.10.0-ha56f1ee_2\n", - " openssl 3.0.0-h7f98852_2 --> 1.1.1l-h7f98852_0\n", - " python 3.7.10-hf930737_104_cpython --> 3.7.10-hb7a2778_104_cpython\n", - "\n", - "\n", - "\n", - "Downloading and Extracting Packages\n", - "dataclasses-0.8 | 10 KB | : 100% 1.0/1 [00:00<00:00, 4.99it/s]\n", - "libgfortran4-7.5.0 | 1.3 MB | : 100% 1.0/1 [00:00<00:00, 2.23it/s]\n", - "pandocfilters-1.5.0 | 11 KB | : 100% 1.0/1 [00:00<00:00, 25.63it/s]\n", - "pandas-1.3.5 | 12.7 MB | : 100% 1.0/1 [00:04<00:00, 4.36s/it] \n", - "snappy-1.1.8 | 32 KB | : 100% 1.0/1 [00:00<00:00, 16.78it/s]\n", - "debugpy-1.5.1 | 2.0 MB | : 100% 1.0/1 [00:00<00:00, 1.14it/s]\n", - "nose-1.3.7 | 212 KB | : 100% 1.0/1 [00:00<00:00, 2.19it/s]\n", - "openforcefields-2.0. | 91 KB | : 100% 1.0/1 [00:00<00:00, 2.06it/s]\n", - "imagesize-1.3.0 | 9 KB | : 100% 1.0/1 [00:00<00:00, 25.07it/s]\n", - "gettext-0.19.8.1 | 3.6 MB | : 100% 1.0/1 [00:01<00:00, 1.28s/it]\n", - "kiwisolver-1.3.2 | 78 KB | : 100% 1.0/1 [00:00<00:00, 17.89it/s]\n", - "pillow-8.4.0 | 706 KB | : 100% 1.0/1 [00:00<00:00, 4.07it/s]\n", - "libwebp-base-1.2.1 | 845 KB | : 100% 1.0/1 [00:00<00:00, 3.98it/s]\n", - "argcomplete-1.12.3 | 34 KB | : 100% 1.0/1 [00:00<00:00, 15.41it/s]\n", - "libdeflate-1.8 | 67 KB | : 100% 1.0/1 [00:00<00:00, 18.16it/s]\n", - "greenlet-1.1.2 | 90 KB | : 100% 1.0/1 [00:00<00:00, 13.37it/s]\n", - "pytz-2021.3 | 242 KB | : 100% 1.0/1 [00:00<00:00, 5.34it/s]\n", - "openssl-1.1.1l | 2.1 MB | : 100% 1.0/1 [00:00<00:00, 2.05it/s]\n", - "boost-cpp-1.72.0 | 16.3 MB | : 100% 1.0/1 [00:09<00:00, 9.26s/it]\n", - "libssh2-1.10.0 | 233 KB | : 100% 1.0/1 [00:00<00:00, 8.31it/s]\n", - "cairo-1.16.0 | 1.5 MB | : 100% 1.0/1 [00:00<00:00, 1.83it/s]\n", - "ocl-icd-2.3.1 | 119 KB | : 100% 1.0/1 [00:00<00:00, 10.26it/s]\n", - "jpeg-9d | 266 KB | : 100% 1.0/1 [00:00<00:00, 8.56it/s]\n", - "parso-0.8.3 | 69 KB | : 100% 1.0/1 [00:00<00:00, 13.26it/s]\n", - "jedi-0.18.1 | 997 KB | : 100% 1.0/1 [00:00<00:00, 1.38it/s]\n", - "libllvm10-10.0.1 | 26.4 MB | : 100% 1.0/1 [00:07<00:00, 7.04s/it]\n", - "pdbfixer-1.8.1 | 498 KB | : 100% 1.0/1 [00:00<00:00, 5.74it/s]\n", - "mysql-libs-8.0.27 | 1.9 MB | : 100% 1.0/1 [00:00<00:00, 2.36it/s]\n", - "ipython-7.30.1 | 1.1 MB | : 100% 1.0/1 [00:00<00:00, 2.09it/s]\n", - "mysql-common-8.0.27 | 1.8 MB | : 100% 1.0/1 [00:00<00:00, 1.13it/s]\n", - "libgcc-7.2.0 | 304 KB | : 100% 1.0/1 [00:00<00:00, 8.21it/s]\n", - "gstreamer-1.18.5 | 2.0 MB | : 100% 1.0/1 [00:00<00:00, 1.43it/s]\n", - 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"babel-2.9.1 | 6.2 MB | : 100% 1.0/1 [00:01<00:00, 1.88s/it] \n", - "pyqt-5.12.3 | 22 KB | : 100% 1.0/1 [00:00<00:00, 15.06it/s]\n", - "rdkit-2020.09.3 | 26.2 MB | : 100% 1.0/1 [00:06<00:00, 6.22s/it]\n", - "importlib_resources- | 21 KB | : 100% 1.0/1 [00:00<00:00, 19.59it/s]\n", - "pybtex-docutils-1.0. | 10 KB | : 100% 1.0/1 [00:00<00:00, 4.51it/s]\n", - "nbclient-0.5.9 | 63 KB | : 100% 1.0/1 [00:00<00:00, 18.52it/s]\n", - "sphinxcontrib-qthelp | 25 KB | : 100% 1.0/1 [00:00<00:00, 15.69it/s]\n", - "flit-core-3.6.0 | 42 KB | : 100% 1.0/1 [00:00<00:00, 16.58it/s]\n", - "mdtraj-1.9.7 | 1.8 MB | : 100% 1.0/1 [00:00<00:00, 1.97it/s]\n", - "widgetsnbextension-3 | 1.3 MB | : 100% 1.0/1 [00:00<00:00, 2.24it/s]\n", - "netcdf4-1.5.3 | 546 KB | : 100% 1.0/1 [00:00<00:00, 2.24it/s]\n", - "nomkl-1.0 | 4 KB | : 100% 1.0/1 [00:00<00:00, 26.79it/s]\n", - "prometheus_client-0. | 47 KB | : 100% 1.0/1 [00:00<00:00, 18.42it/s]\n", - "fftw-3.3.10 | 6.4 MB | : 100% 1.0/1 [00:01<00:00, 1.71s/it] \n", - "networkx-2.6.3 | 1.5 MB | : 100% 1.0/1 [00:00<00:00, 1.82it/s]\n", - 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"importlib-metadata-4 | 32 KB | : 100% 1.0/1 [00:00<00:00, 19.01it/s]\n", - "freetype-2.10.4 | 890 KB | : 100% 1.0/1 [00:00<00:00, 3.99it/s]\n", - "libbrotlienc-1.0.9 | 286 KB | : 100% 1.0/1 [00:00<00:00, 11.48it/s]\n", - "libsodium-1.0.18 | 366 KB | : 100% 1.0/1 [00:00<00:00, 8.25it/s]\n", - "unicodedata2-14.0.0 | 501 KB | : 100% 1.0/1 [00:00<00:00, 7.44it/s]\n", - "Preparing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", - "Verifying transaction: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", - "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- By downloading and using the CUDA Toolkit conda packages, you accept the terms and conditions of the CUDA End User License Agreement (EULA): https://docs.nvidia.com/cuda/eula/index.html\n", - "\n", - "\b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| Enabling notebook extension jupyter-js-widgets/extension...\n", - "Paths used for configuration of notebook: \n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.d/plotlywidget.json\n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.json\n", - "Paths used for configuration of notebook: \n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.d/plotlywidget.json\n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\n", - " - Validating: \u001b[32mOK\u001b[0m\n", - "Paths used for configuration of notebook: \n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.d/plotlywidget.json\n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.json\n", - "\n", - "\b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n" - ] - } - ], - "source": [ - "!mamba install openmm openforcefield parmed yank openmoltools pdbfixer solvationtoolkit -c omnia" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "8mWaYJh-GIYQ", - "outputId": "f9ff44af-a10e-4a21-da3f-a08e980ff57d" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " __ __ __ __\n", - " / \\ / \\ / \\ / \\\n", - " / \\/ \\/ \\/ \\\n", - "███████████████/ /██/ /██/ /██/ /████████████████████████\n", - " / / \\ / \\ / \\ / \\ \\____\n", - " / / \\_/ \\_/ \\_/ \\ o \\__,\n", - " / _/ \\_____/ `\n", - " |/\n", - " ███╗ ███╗ █████╗ ███╗ ███╗██████╗ █████╗\n", - " ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗\n", - " ██╔████╔██║███████║██╔████╔██║██████╔╝███████║\n", - " ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║\n", - " ██║ ╚═╝ ██║██║ ██║██║ ╚═╝ ██║██████╔╝██║ ██║\n", - " ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚═════╝ ╚═╝ ╚═╝\n", - "\n", - " Supported by @QuantStack\n", - "\n", - " GitHub: https://github.com/QuantStack/mamba\n", - " Twitter: https://twitter.com/QuantStack\n", - "\n", - "█████████████████████████████████████████████████████████████\n", - "\n", - "Getting conda-forge linux-64\n", - "Getting conda-forge noarch\n", - "Getting pkgs/main linux-64\n", - "Getting pkgs/main noarch\n", - "Getting pkgs/r linux-64\n", - "Getting pkgs/r noarch\n", - "\n", - "Looking for: ['nb_conda', 'mpld3', 'scikit-learn', 'seaborn', 'numpy', 'matplotlib', 'bokeh', 'python 3.7.10']\n", - "\n", - "232724 packages in https://conda.anaconda.org/conda-forge/linux-64\n", - "77750 packages in https://conda.anaconda.org/conda-forge/noarch\n", - "23736 packages in https://repo.anaconda.com/pkgs/main/linux-64\n", - "4541 packages in https://repo.anaconda.com/pkgs/main/noarch\n", - "6637 packages in https://repo.anaconda.com/pkgs/r/linux-64\n", - "5025 packages in https://repo.anaconda.com/pkgs/r/noarch\n", - "\n", - "## Package Plan ##\n", - "\n", - " environment location: /usr/local\n", - "\n", - " added / updated specs:\n", - " - bokeh\n", - " - matplotlib\n", - " - mpld3\n", - " - nb_conda\n", - " - numpy\n", - " - python==3.7.10\n", - " - scikit-learn\n", - " - seaborn\n", - "\n", - "\n", - "The following packages will be downloaded:\n", - "\n", - " package | build\n", - " ---------------------------|-----------------\n", - " bokeh-2.4.2 | py37h89c1867_0 13.7 MB conda-forge\n", - " joblib-1.1.0 | pyhd8ed1ab_0 210 KB conda-forge\n", - " mpld3-0.5.7 | pyhd8ed1ab_0 175 KB conda-forge\n", - " nb_conda-2.2.1 | py37hc8dfbb8_4 36 KB conda-forge\n", - " nb_conda_kernels-2.3.1 | py37h89c1867_1 27 KB conda-forge\n", - " scikit-learn-1.0.2 | py37hf9e9bfc_0 7.8 MB conda-forge\n", - " seaborn-0.11.2 | pyhd3eb1b0_0 218 KB\n", - " threadpoolctl-3.0.0 | pyh8a188c0_0 17 KB conda-forge\n", - " ------------------------------------------------------------\n", - " Total: 22.2 MB\n", - "\n", - "The following NEW packages will be INSTALLED:\n", - "\n", - " bokeh conda-forge/linux-64::bokeh-2.4.2-py37h89c1867_0\n", - " joblib conda-forge/noarch::joblib-1.1.0-pyhd8ed1ab_0\n", - " mpld3 conda-forge/noarch::mpld3-0.5.7-pyhd8ed1ab_0\n", - " nb_conda conda-forge/linux-64::nb_conda-2.2.1-py37hc8dfbb8_4\n", - " nb_conda_kernels conda-forge/linux-64::nb_conda_kernels-2.3.1-py37h89c1867_1\n", - " scikit-learn conda-forge/linux-64::scikit-learn-1.0.2-py37hf9e9bfc_0\n", - " seaborn pkgs/main/noarch::seaborn-0.11.2-pyhd3eb1b0_0\n", - " threadpoolctl conda-forge/noarch::threadpoolctl-3.0.0-pyh8a188c0_0\n", - "\n", - "\n", - "\n", - "Downloading and Extracting Packages\n", - "scikit-learn-1.0.2 | 7.8 MB | : 100% 1.0/1 [00:02<00:00, 2.55s/it]\n", - "threadpoolctl-3.0.0 | 17 KB | : 100% 1.0/1 [00:00<00:00, 21.33it/s]\n", - "joblib-1.1.0 | 210 KB | : 100% 1.0/1 [00:00<00:00, 8.36it/s]\n", - "bokeh-2.4.2 | 13.7 MB | : 100% 1.0/1 [00:04<00:00, 4.64s/it]\n", - "nb_conda-2.2.1 | 36 KB | : 100% 1.0/1 [00:00<00:00, 3.14it/s] \n", - "seaborn-0.11.2 | 218 KB | : 100% 1.0/1 [00:00<00:00, 4.12it/s]\n", - "nb_conda_kernels-2.3 | 27 KB | : 100% 1.0/1 [00:00<00:00, 8.40it/s] \n", - "mpld3-0.5.7 | 175 KB | : 100% 1.0/1 [00:00<00:00, 9.40it/s]\n", - "Preparing transaction: - \b\b\\ \b\bdone\n", - "Verifying transaction: / \b\b- \b\b\\ \b\b| \b\bdone\n", - "Executing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- Enabling nb_conda_kernels...\n", - "CONDA_PREFIX: /usr/local\n", - "Status: enabled\n", - "\n", - "\b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- Config option `kernel_spec_manager_class` not recognized by `EnableNBExtensionApp`.\n", - "Enabling notebook extension nb_conda/main...\n", - "Paths used for configuration of notebook: \n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.d/plotlywidget.json\n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.json\n", - "Paths used for configuration of notebook: \n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.d/plotlywidget.json\n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\n", - " - Validating: \u001b[32mOK\u001b[0m\n", - "Paths used for configuration of notebook: \n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.d/plotlywidget.json\n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.json\n", - "Enabling tree extension nb_conda/tree...\n", - "Paths used for configuration of tree: \n", - " \t/usr/local/etc/jupyter/nbconfig/tree.json\n", - "Paths used for configuration of tree: \n", - " \t\n", - " - Validating: \u001b[32mOK\u001b[0m\n", - "Paths used for configuration of tree: \n", - " \t/usr/local/etc/jupyter/nbconfig/tree.json\n", - "Config option `kernel_spec_manager_class` not recognized by `EnableServerExtensionApp`.\n", - "Enabling: nb_conda\n", - "- Writing config: /usr/local/etc/jupyter\n", - " - Validating...\n", - " nb_conda 2.2.1 \u001b[32mOK\u001b[0m\n", - "\n", - "\b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n" - ] - } - ], - "source": [ - "!mamba install nb_conda mpld3 scikit-learn seaborn numpy matplotlib bokeh -c conda-forge" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "G8ZOgWQYGXN4", - "outputId": "f78ccca6-92c3-4281-d653-e7835b67a93d" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " __ __ __ __\n", - " / \\ / \\ / \\ / \\\n", - " / \\/ \\/ \\/ \\\n", - "███████████████/ /██/ /██/ /██/ /████████████████████████\n", - " / / \\ / \\ / \\ / \\ \\____\n", - " / / \\_/ \\_/ \\_/ \\ o \\__,\n", - " / _/ \\_____/ `\n", - " |/\n", - " ███╗ ███╗ █████╗ ███╗ ███╗██████╗ █████╗\n", - " ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗\n", - " ██╔████╔██║███████║██╔████╔██║██████╔╝███████║\n", - " ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║\n", - " ██║ ╚═╝ ██║██║ ██║██║ ╚═╝ ██║██████╔╝██║ ██║\n", - " ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚═════╝ ╚═╝ ╚═╝\n", - "\n", - " Supported by @QuantStack\n", - "\n", - " GitHub: https://github.com/QuantStack/mamba\n", - " Twitter: https://twitter.com/QuantStack\n", - "\n", - "█████████████████████████████████████████████████████████████\n", - "\n", - "Getting bioconda linux-64\n", - "Getting bioconda noarch\n", - "Getting conda-forge linux-64\n", - "Getting conda-forge noarch\n", - "\n", - "Looking for: ['nglview', 'python 3.7.10']\n", - "\n", - "40075 packages in https://conda.anaconda.org/bioconda/linux-64\n", - "33891 packages in https://conda.anaconda.org/bioconda/noarch\n", - "232724 packages in https://conda.anaconda.org/conda-forge/linux-64\n", - "77750 packages in https://conda.anaconda.org/conda-forge/noarch\n", - "\n", - "## Package Plan ##\n", - "\n", - " environment location: /usr/local\n", - "\n", - " added / updated specs:\n", - " - nglview\n", - " - python==3.7.10\n", - "\n", - "\n", - "The following packages will be downloaded:\n", - "\n", - " package | build\n", - " ---------------------------|-----------------\n", - " nglview-3.0.3 | pyh8a188c0_0 6.3 MB conda-forge\n", - " ------------------------------------------------------------\n", - " Total: 6.3 MB\n", - "\n", - "The following NEW packages will be INSTALLED:\n", - "\n", - " nglview conda-forge/noarch::nglview-3.0.3-pyh8a188c0_0\n", - "\n", - "\n", - "\n", - "Downloading and Extracting Packages\n", - "nglview-3.0.3 | 6.3 MB | : 100% 1.0/1 [00:01<00:00, 1.82s/it]\n", - "Preparing transaction: - \b\bdone\n", - "Verifying transaction: | \b\bdone\n", - "Executing transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n" - ] - } - ], - "source": [ - "!mamba install nglview -c bioconda" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "9s9a84nIGhBy" - }, - "source": [ - "## Installation of OpenEye-Toolkits\n", - "These toolkits will be utilized quite often in the course and rely on a license file to be used. Please make sure to have the license file uploaded onto google drive." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "wqu009PiGoLS", - "outputId": "807801f7-6487-42dc-be6c-486954d6b6e1" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - " __ __ __ __\n", - " / \\ / \\ / \\ / \\\n", - " / \\/ \\/ \\/ \\\n", - "███████████████/ /██/ /██/ /██/ /████████████████████████\n", - " / / \\ / \\ / \\ / \\ \\____\n", - " / / \\_/ \\_/ \\_/ \\ o \\__,\n", - " / _/ \\_____/ `\n", - " |/\n", - " ███╗ ███╗ █████╗ ███╗ ███╗██████╗ █████╗\n", - " ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗\n", - " ██╔████╔██║███████║██╔████╔██║██████╔╝███████║\n", - " ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║\n", - " ██║ ╚═╝ ██║██║ ██║██║ ╚═╝ ██║██████╔╝██║ ██║\n", - " ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚═════╝ ╚═╝ ╚═╝\n", - "\n", - " Supported by @QuantStack\n", - "\n", - " GitHub: https://github.com/QuantStack/mamba\n", - " Twitter: https://twitter.com/QuantStack\n", - "\n", - "█████████████████████████████████████████████████████████████\n", - "\n", - "Getting openeye linux-64\n", - "Getting openeye noarch\n", - "Getting conda-forge linux-64\n", - "Getting conda-forge noarch\n", - "\n", - "Looking for: ['openeye-toolkits', 'python 3.7.10']\n", - "\n", - "44 packages in https://conda.anaconda.org/openeye/linux-64\n", - "0 packages in https://conda.anaconda.org/openeye/noarch\n", - "232724 packages in https://conda.anaconda.org/conda-forge/linux-64\n", - "77750 packages in https://conda.anaconda.org/conda-forge/noarch\n", - "\n", - "## Package Plan ##\n", - "\n", - " environment location: /usr/local\n", - "\n", - " added / updated specs:\n", - " - openeye-toolkits\n", - " - python==3.7.10\n", - "\n", - "\n", - "The following packages will be downloaded:\n", - "\n", - " package | build\n", - " ---------------------------|-----------------\n", - " iniconfig-1.1.1 | pyh9f0ad1d_0 8 KB conda-forge\n", - " more-itertools-8.12.0 | pyhd8ed1ab_0 47 KB conda-forge\n", - " openeye-toolkits-2021.2.0 | py37_0 79.6 MB openeye\n", - " pluggy-1.0.0 | py37h89c1867_2 25 KB conda-forge\n", - " py-1.11.0 | pyh6c4a22f_0 74 KB conda-forge\n", - " pytest-6.2.5 | py37h89c1867_1 433 KB conda-forge\n", - " ------------------------------------------------------------\n", - " Total: 80.2 MB\n", - "\n", - "The following NEW packages will be INSTALLED:\n", - "\n", - " iniconfig conda-forge/noarch::iniconfig-1.1.1-pyh9f0ad1d_0\n", - " more-itertools conda-forge/noarch::more-itertools-8.12.0-pyhd8ed1ab_0\n", - " openeye-toolkits openeye/linux-64::openeye-toolkits-2021.2.0-py37_0\n", - " pluggy conda-forge/linux-64::pluggy-1.0.0-py37h89c1867_2\n", - " py conda-forge/noarch::py-1.11.0-pyh6c4a22f_0\n", - " pytest conda-forge/linux-64::pytest-6.2.5-py37h89c1867_1\n", - "\n", - "\n", - "\n", - "Downloading and Extracting Packages\n", - "iniconfig-1.1.1 | 8 KB | : 100% 1.0/1 [00:00<00:00, 6.77it/s]\n", - "pluggy-1.0.0 | 25 KB | : 100% 1.0/1 [00:00<00:00, 11.82it/s]\n", - "more-itertools-8.12. | 47 KB | : 100% 1.0/1 [00:00<00:00, 16.57it/s]\n", - "pytest-6.2.5 | 433 KB | : 100% 1.0/1 [00:00<00:00, 4.80it/s]\n", - "openeye-toolkits-202 | 79.6 MB | : 100% 1.0/1 [00:17<00:00, 17.24s/it]\n", - "py-1.11.0 | 74 KB | : 100% 1.0/1 [00:00<00:00, 15.13it/s]\n", - "Preparing transaction: - \b\bdone\n", - "Verifying transaction: | \b\b/ \b\b- \b\b\\ \b\bdone\n", - "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n" - ] - } - ], - "source": [ - "!mamba install openeye-toolkits -c openeye" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "DBtyRrrWHBq7" - }, - "source": [ - "The code block below checks if your openeye-toolkits are activated. Make sure to place the license file on your google drive." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3jUwtIRYG58b", - "outputId": "bad3e5df-33fd-4bfd-8cd7-e68bede872cd" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mounted at /content/drive\n", - "Was your OpenEye license correctly installed (True/False)? True\n" - ] - } - ], - "source": [ - "from google.colab import drive\n", - "drive.mount('/content/drive',force_remount = True)\n", - "\n", - "license_filename = '/content/drive/MyDrive/oe_license.txt'\n", - "\n", - "import os\n", - "import openeye\n", - "if os.path.isfile(license_filename):\n", - " license_file = open(license_filename, 'r')\n", - " openeye.OEAddLicenseData(license_file.read())\n", - " license_file.close()\n", - "else:\n", - " print(\"Error: Your OpenEye license is not readable; please check your filename and that you have mounted your Google Drive\")\n", - "\n", - "licensed = openeye.oechem.OEChemIsLicensed()\n", - "print(\"Was your OpenEye license correctly installed (True/False)? \" + str(licensed))\n", - "if not licensed:\n", - " print(\"Error: Your OpenEye license is not correctly installed.\")\n", - " raise Exception(\"Error: Your OpenEye license is not correctly installed.\")" - ] - } - ], - "metadata": { - "colab": { - "name": "Getting_Started_condacolab.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.12" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/uci-pharmsci/README.md b/uci-pharmsci/README.md index 3de6aae..8de53aa 100644 --- a/uci-pharmsci/README.md +++ b/uci-pharmsci/README.md @@ -25,7 +25,7 @@ Please begin by reading the beginning portion of [`getting-started.md`](getting- #### Cheatsheets and dotfiles -Under the **uci-pharmasci/docs/** directory, we have included some useful command cheat sheets for [BASH](https://github.com/nathanmlim/blues-apps/tree/master/docs/bash_cheatsheet.jpg) and [vi](https://github.com/nathanmlim/blues-apps/tree/master/docs/vi_cheatsheet.pdf) (on OS X, zsh is becoming default instead, but in general it is similar.). +Under the **uci-pharmasci/docs/** directory, we have included some useful command cheat sheets for [BASH](https://github.com/MobleyLab/drug-computing/blob/master/uci-pharmsci/docs/bash_cheatsheet.jpg) and [vi](https://github.com/MobleyLab/drug-computing/blob/master/uci-pharmsci/docs/vi_cheatsheet.pdf) (on OS X, zsh is becoming default instead, but in general it is similar.). You may also want to check out the [CodeAcademy Github](https://www.codecademy.com/learn/learn-git) tutorial. diff --git a/uci-pharmsci/assigned_materials.md b/uci-pharmsci/assigned_materials.md index f03010c..3ba72c6 100644 --- a/uci-pharmsci/assigned_materials.md +++ b/uci-pharmsci/assigned_materials.md @@ -42,8 +42,11 @@ To prepare for Lecture 1, on Python, Linux, text editors, and GitHub, please: - Skim through the PDF of the MD (part II)/MC slides in the `MC` directory, noting any questions for discussion in class - Review the MC sandbox Jupyter notebook in the same directory -## Before Lecture 7 ([Solvation](lectures/solvation)): -- Read the Jupyter notebook in the `solvation` directory under lectures. +## Before Lecture 7 ([ChEMBL database work](https://github.com/volkamerlab/teachopencadd/tree/master/teachopencadd/talktorials/T001_query_chembl) +- For this lecture we draw on the [TeachOpenCADD](https://github.com/volkamerlab/teachopencadd) repo +- Clone or download the GitHub repo, or at least the content of the `T001_query_chembl` directory from the `talktorials` directory +- Skim the content in the `talktorial.ipynb` notebook +- Install chembl_webresource_client, which is conda-installable ## Before Lecture 8 ([Docking, scoring and pose prediction](lectures/docking_scoring_pose)): - Read the `docking.ipynb` Jupyter notebook in the relevant directory and `old_slides.pdf` there, from the slide numbered 11 (bottom left corner) onwards. diff --git a/uci-pharmsci/assignments/3D_structure_shape/3D_structure_shape_assignment.ipynb b/uci-pharmsci/assignments/3D_structure_shape/3D_structure_shape_assignment.ipynb index af7efc4..444cb9c 100644 --- a/uci-pharmsci/assignments/3D_structure_shape/3D_structure_shape_assignment.ipynb +++ b/uci-pharmsci/assignments/3D_structure_shape/3D_structure_shape_assignment.ipynb @@ -1,568 +1,564 @@ { - "cells": [ - { - "cell_type": "markdown", - "source": [ - "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MobleyLab/drug-computing/blob/master/uci-pharmsci/assignments/3D_structure_shape/3D_structure_shape_assignment.ipynb)" - ], - "metadata": { - "id": "-cfqgSTNynHP" - } - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GH7gVE7WyP0a" - }, - "source": [ - "# 3D Structure/Shape Assignment\n", - "\n", - "## Overview\n", - "\n", - "This assignment involves two major parts. It first introduces the OEChem toolkits by having you perform some simple tasks with molecules in two and three dimensions, including generating 3D structures of molecules (much of which you will have already seen in the 3D structure/shape lecture and Jupyter notebook). You will also convert between common file formats and do simple shape overlays as well as visualization of your molecules. Some software installation will be required.\n", - "\n", - "In the second major part of this assignment, you will use shape overlays to try and recognize HIV integrase inhibitors from a small library of potential HIV integrase inhibitors.\n", - "\n", - "## More detailed overview\n", - "\n", - "This assignment works through common tasks for working with small molecules. Specifically, you want to model a molecule, and you need to build 2D and 3D structures of the molecule, save to a particular file format for visualization, and compare it to other molecules. Here, you get to do these for the first time. Many different tool sets do these tasks, but here I’ll introduce you to one particular tool set (from OpenEye software) that works particularly well with Python and is free to academics. You have already seen and used this set of tools in class in the 3D structure/shape Jupyter notebook. This assignment will simply build on that notebook. Especially, Step 0 through Step 3 replicate tasks done in that notebook.\n", - "\n", - "\n", - "After completing these initial tasks, you will then take a small library of potential HIV integrase inhibitors, and use a shape-based search to try and identify active compounds on the basis of shape similarity to known inhibitors." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "nvKQIyv-KtW-" - }, - "outputs": [], - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "okcvdgqHyP0c" - }, - "source": [ - "# Getting started/warm-up\n", - "\n", - "Before getting started, make a copy of this Jupyter notebook to work in and name it something else, such as `Mobley_3D_structure_shape.ipynb` if your last name is Mobley.\n", - "\n", - "***If you are running this on Google Colab, please add the installation blocks from the [getting started notebook](https://github.com/MobleyLab/drug-computing/blob/master/uci-pharmsci/Getting_Started.ipynb) or [condacolab](https://github.com/aakankschit/drug-computing/blob/master/uci-pharmsci/Getting_Started_condacolab.ipynb) here and then execute the code below***\n", - "\n", - "## Step 0: Install PyMol for visualization\n", - "\n", - "We've seen `nglview` already for interactive visualization, but it is not as useful for visualizing things which are neither (a) MD simulations, nor (b) single structures. Specifically, here we will generate multiple structures/conformations, so we need a bit better visualization.\n", - "\n", - "In this exercise, we’ll begin looking at and working with molecules in 3D, which means we need a viewer. PyMol is a popular general purpose one we will use extensively in this class. You should obtain the free academic version of PyMol online, but if you have any trouble with that please contact me. Alternate viewers like VMD or Chimera could also be used, but here I'll explain PyMol's usage.\n", - "\n", - "## Step 1: Build 2D structures of some molecules\n", - "\n", - "The first step is to build some molecules to work with. You might need to do this because your boss or research adviser asks you to model certain molecules, or you might come across some 2D structures of molecules in a research paper you would like to model. In any case, for whatever reason, you have a list of molecules to model, and you need to start by building their structures.\n", - "\n", - "Let’s start by building 2D structures. If you already have commercial or favorite software for this, like ChemDraw, feel free to use that. If not, I recommend Marvin, which is free, as noted in the 3D structure/shape lecture. You can use it [within your browser](https://marvinjs-demo.chemaxon.com/latest/); you may need to install a free Java plugin to do so) or [download it](https://www.chemaxon.com/products/marvin/marvinsketch/) and install it on your computer (http://www.chemaxon.com/download/marvin/for-end-users/). I normally use the desktop version. \n", - "\n", - "In Marvin, draw some structures of molecules (if you have some molecules you’re interested in because of another class or research, you should do them, otherwise just pick some) and save them to your computer (from the file->save as menu) as MDL SDF files, one at a time. \n", - "\n", - "Drawing them should produce something like this:\n", - "![image](https://raw.githubusercontent.com/MobleyLab/drug-computing/master/uci-pharmsci/lectures/3D_structure_shape/Marvin_molecule.png)\n", - "\n", - "Use the buttons at the bottom to create pre-formed rings; the tools at right can be used to select specific bond types or stereochemistry, and the buttons at right can be used to introduce elements other than carbon.\n", - "\n", - "You should select at least 5-10 molecules to build, of your choice. Try to include some which are relatively similar to one another (i.e. differing only by a functional group, for example) and some which are not.\n", - "\tOnce your molecules are built, I suggest loading them (or at least one) in PyMol (open PyMol, then go to File->Open, then select your file) and having a quick look. You should see something like the below, though obviously details will depend on your platform (OS X, Linux, Windows) and what you’ve named your molecules. I suggest clicking the “show” button (S, as indicated by the red arrow) and choosing the “sticks” representation so you can see your molecule more clearly. Try clicking it and dragging to rotate -- notice how the molecule is completely planar (two dimensional) right now, even though it shouldn’t be. If you would like, you can choose Display -> Show Valences to show bond types. Experiment with using S to show other representations (such as surface) if you like, and use H to hide representations you’re done with.\n", - " \n", - "![pymol](https://github.com/MobleyLab/drug-computing/blob/master/uci-pharmsci/assignments/3D_structure_shape/pymol.png?raw=1)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VReZXLoCyP0d" - }, - "source": [ - "## Step 2: Building 3D structures of molecules\n", - "\n", - "In the next step, you will want to build 3D structures of your molecules. I have provided some example Python code for this (and for other tasks) in the [`3D_Structure_shape.ipynb`](https://github.com/MobleyLab/drug-computing/blob/master/uci-pharmsci/lectures/3D_structure_shape/3D_Structure_Shape.ipynb) notebook we used in the relevant lecture. \n", - "\n", - "### First, do this for just one molecule\n", - "\n", - "**Put some code in the box just below to read in one of the molecules you drew and saved from the SDF file, generate a 3D conformation, and write it out to a mol2 file**, drawing on the examples from the lecture." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "id": "M8ij6GIkyP0e" - }, - "outputs": [], - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "hNFQ_DNgyP0e" - }, - "source": [ - "### But we're going to want to do this for many molecules, so write some functions\n", - "\n", - "Here let's write functions to read molecules, write molecules, and generate conformations; we'll call the last one `expandConformations` because it could be used on a molecule which already has one conformation in order to generate many conformations, as well.\n", - "\n", - "We'll make these functions so you can easily reuse them on many molecules.\n", - "\n", - "I've written the documentation strings for you in the code below; your job is to fill in the inner workings and then make sure they work\n", - "\n", - "#### Write your functions here" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "id": "PlVLdvb0yP0e" - }, - "outputs": [], - "source": [ - "def readMolecule( inputfile ):\n", - " \"\"\"Take specified input molecule file with an OEChem supported molecular file format. Read a molecule from the file and return it as an OEChem OEMol molecule.\"\"\"\n", - "\n", - " #Insert your code (as seen in the example) here to read the input file 'inputfile'\n", - "\n", - " return oemol #Replace oemol with the name of your molecule object\n", - "\n", - "\n", - "def writeMolecule( oemol, outputfile):\n", - " \"\"\"Write a provided oemol to specified output file using an OEChem supported file format, as determined by the file extension (such as .mol2 or .sdf).\n", - "Arguments:\n", - "- oemol: OEMol molecule to write\n", - "- outputfile: File to write to.\n", - "Returns: Nothing\"\"\"\n", - "\n", - " #Insert your code (as seen in the example) here to write an output file\n", - "\n", - "def expandConformations( oemol, maxconfs = 100, strictStereo = True ):\n", - " \"\"\"Take a provided OEMol molecule, and use OpenEye's Omega to generate multiple conformations of the molecule, which are then returned in a new OEMol molecule.\n", - "Arguments:\n", - "- oemol: Provided molecule to generate conformations for\n", - "- maxconfs: Optional argument (default 100) specifying the maximum number of conformations to generate. Specify 1 to get just a single conformation.\n", - "- strictStereo: Optional argument specifying whether (True) or not (False) to use strict stereochemistry checking for Omega. Default: True.\n", - "Returns:\n", - "- expanded_oemol: OEMol containing the generated conformations.\"\"\"\n", - "\n", - " #Write your code here (as seen in the example)\n", - " return expanded_oemol" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "v8CSBG-VyP0f" - }, - "source": [ - "#### You probably want to test your functions now\n", - "You should probably use the box below to make sure you can read in one of the molecules you created, generate conformations, and write it back out again. If you've written your functions properly, this code should just work!" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "MeNOf2gqyP0g", - "outputId": "3664b12a-15c6-49a9-936d-3c0499698bf1" - }, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'readMolecule' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0minputfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'test.sdf'\u001b[0m \u001b[0;31m# A test molecule I provided\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreadMolecule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mexpandedmol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpandConformations\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mmol\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mwriteMolecule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'test.mol2'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#Write out to a mol2 file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'readMolecule' is not defined" - ] - } - ], - "source": [ - "inputfile = 'test.sdf' # A test molecule I provided\n", - "\n", - "mol = readMolecule(inputfile)\n", - "expandedmol = expandConformations( mol ) \n", - "writeMolecule('test.mol2') #Write out to a mol2 file" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "OSAaPq5gyP0g" - }, - "source": [ - "Now you should probably check with PyMol that `test.mol2` has a molecule with a 3D conformer in it, or add visualization in this notebook using `nglview` to check." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "09Q1EVHryP0h" - }, - "source": [ - "## Step 3: Shape overlay your molecules\n", - "\n", - "### Get ready\n", - "Next, we want to do a shape overlay of your molecules so we can see how structurally similar they are. This is a common task in drug discovery, as we noted in class, and often can be used in several ways, such as finding out about the likely shape of a binding site based on the molecules which bind there, or finding new molecules which may fit a binding site while being chemically distinct from existing molecules based on their shape. \n", - "\n", - "Here, our goal will be to take your entire set of molecules and overlay them onto a common reference structure. Here's an example which does a shape (+color, where color basically measures chemical similarity) overlay for a pair of molecules; this is rather likewhat you saw in the 3D structure/shape lecture: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "phAZaasmyP0h", - "outputId": "9787e511-50c6-402a-c5ab-763ef47d07e5" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "title: naproxen_13 tanimoto combo = 1.47\n", - "title: naproxen_0 tanimoto combo = 1.47\n", - "title: naproxen_16 tanimoto combo = 1.47\n", - "title: naproxen_12 tanimoto combo = 1.47\n", - "title: naproxen_1 tanimoto combo = 1.47\n", - "title: naproxen_8 tanimoto combo = 1.47\n", - "title: naproxen_4 tanimoto combo = 1.47\n", - "title: naproxen_5 tanimoto combo = 1.47\n", - "title: naproxen_9 tanimoto combo = 1.46\n", - "title: naproxen_17 tanimoto combo = 1.46\n" - ] - } - ], - "source": [ - "from openeye.oechem import *\n", - "from openeye.oeomega import *\n", - "from openeye.oeiupac import *\n", - "from openeye.oeshape import *\n", - "\n", - "omega = OEOmega() #Initialize class\n", - "omega.SetMaxConfs(100) #Here we want to use more conformers if needed\n", - "omega.SetStrictStereo(False) #Set to false to pick random stereoisomer if stereochemistry is not specified\n", - "\n", - "# Make some molecules\n", - "ibuprofen = OEMol()\n", - "naproxen = OEMol()\n", - "OEParseIUPACName(ibuprofen, 'ibuprofen')\n", - "OEParseIUPACName(naproxen, 'naproxen')\n", - "\n", - "#Set up reference molecule\n", - "refmol = ibuprofen\n", - "omega(refmol)\n", - "\n", - "#Fit molecule\n", - "fitmol = naproxen\n", - "omega(fitmol)\n", - "fitmol.SetTitle('naproxen')\n", - "\n", - "# Open output stream for output molecule\n", - "outfs = oemolostream('fitted_output.mol2')\n", - "# How many conformers will we generate for fitting?\n", - "nconfs = 10\n", - "\n", - "# Setup ROCS to provide specified number of conformers per hit\n", - "options = OEROCSOptions()\n", - "options.SetNumBestHits(nconfs)\n", - "options.SetConfsPerHit(nconfs)\n", - "rocs = OEROCS(options)\n", - "rocs.AddMolecule(fitmol) #Add our molecule as the one we are fitting\n", - "\n", - "# Loop over results and output\n", - "for res in rocs.Overlay(refmol):\n", - " outmol = res.GetOverlayConfs() #Use GetOverlayConf to get just the best; GetOverlayConfs for all\n", - " OERemoveColorAtoms(outmol)\n", - " OEAddExplicitHydrogens(outmol)\n", - " OEWriteMolecule(outfs, outmol)\n", - " print(\"title: %s tanimoto combo = %.2f\" % (outmol.GetTitle(), res.GetTanimotoCombo()))\n", - "outfs.close()\n", - "\n", - "# If you wanted, you could also print just the \"color\" similarity:\n", - "#print(res.GetColorTanimoto())\n", - "# Or just the shape similarity:\n", - "#print(res.GetShapeTanimoto())" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "owfnNbolyP0h" - }, - "source": [ - "### Make a function to do this\n", - "\n", - "Again, we'd be better off with a function to do this (and we'll use this function also below in the ligand-based design portion of the assignment, below). So, edit the code below and complete the `fitMoleculeToReference` function. The idea of this function is to take two molecules and compare them based on shape. The code here is a little complicated (which is why I’ve written similar code for you) and the only things you should have to change are the variable names and the indentation. What this ultimately will do is take two molecules, overlay them in a number of different ways, and then return (give back to the user) the actual overlays of the molecules, sorted from best to worst, and the “Tanimoto” scores of the overlays in the same order. Tanimoto scores are scores running from 0 to 1 and essentially measure similarity, where 0 means totally dissimilar and 1 means totally identical. Here, we are actually combining TWO Tanimoto scores (a ‘shape’ Tanimoto score and a ‘color’ (Chemistry) tanimoto score) so our overall Tanimoto score runs from 0 to 2.\n", - "\n", - "Once you think you’ve got your `fitMoleculeToReference` function working, test it on a specific pair of molecules, then use `writeMolecule` (as above) to write out the reference molecule and the fitted molecule (to .mol2 or .sdf format) and view them in PyMol (after downloading to your computer) to ensure it’s working.\n", - "\n", - "When you have the function working, then set up a loop over all of your molecules except your chosen reference molecule, ‘molecule1’, to overlay all of the molecules onto it. Before the loop, set up a list to store the best score for each pair of molecules. Inside the loop, fit each molecule onto the reference and then store the resulting fits to files (being careful not to overwrite any of your existing files). Also store the best score to the list of scores. Also have your program print out the scores. Visualize in PyMol the resulting overlays, and note which ones seem good, which ones seem bad, and which ones seem OK. Note whether these observations seem consistent with the scores (Tanimoto scores) you’re getting or not.\n", - "\n", - "For this portion of the assignment, you should submit your reference molecule’s structure (.mol2 or .sdf) as well as the structure of the best overlay you found, with its score. You should also include a sentence or more explaining what you found in this step in your e-mail." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "id": "e9Avnr34yP0i" - }, - "outputs": [], - "source": [ - "def fitMoleculeToReference( refmol, fitmol, ShapeColor = True):\n", - " \"\"\"Take a multi-conformation molecule to be fitted, and fit it onto a reference molecule using shape overlays.\n", - "Arguments:\n", - "- refmol: Reference molecule to fit onto\n", - "- fitmol: Molecule to fit onto the reference molecule; should be multiple-conformation\n", - "- ShapeColor: Optional argument specifying whether to score overlays based on shape alone (False) or to score based on both shape and color fit (True); in the latter case scores will run from 0 to 2 while in the former they will run from 0 to 1. Default: True.\n", - "Returns:\n", - "- fitted: The fitted conformations, ordered by score from best to worst\n", - "- tanimotos: Tanimoto scores of the fitted conformations.\"\"\"\n", - "\n", - " #Write your code here, as seen in the example\n", - "\n", - " return (fitted, tanimotos)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Eb-WugN9yP0i" - }, - "source": [ - "## Step 4: Ligand-based design\n", - "\n", - "Your next step involves a simple application of this shape overlay technique to a real problem. You will take an existing ligand -- an HIV-1 integrase inhibitor -- from a structure the protein data bank (PDB) and compare this to a set of molecules which were tested experimentally for possible HIV integrase inhibition. The idea is to see whether the molecules which actually are HIV integrase inhibitors are among those which are most shape similar to the known inhibitor. \n", - "\n", - "Here, our exact plan is to take an existing list of 600-700 compounds which were tested for their ability to inhibit HIV-1 integrase. The 3NF8 structure in the PDB gives the crystal structure of one integrase inhibitor from this series bound to HIV-1 integrase. We will use the ligand from that structure as a query, or reference molecule, to see if it easily allows us to recognize other molecules which are likely to bind from our larger set of 600-700 compounds. This somewhat echoes the SAMPL4 HIV integrase challenge reported in [this paper]( http://link.springer.com/article/10.1007/s10822-014-9723-5), though the set of inactive compounds has been expanded somewhat by addition of a portion of the Maybridge fragment library which was also tested experimentally.\n", - "\n", - "You will find several supporting files in this directory to help with this part of the assignment:\n", - "- `actual_w_maybridge.txt`: List of compounds actually tested\n", - "- `3NF8_ligand.pdb`: To simplify your life, I downloaded the 3NF8 structure and extracted the ligand of interest from it to serve as your reference molecule.\n", - "- `final_clean_binders.txt`: “Answers” — a text file which can be parsed (by code provided below) into a list of names of binders, which can be used to check your results.\n", - "Here is what you should do to complete the assignment, starting from the code provided below and the functions you wrote for in Steps 1-3 above:\n", - "\n", - "- Set up a loop over the molecules in the set\n", - "- For every molecule, generate a 3D conformation with your `expandConformations` function, using `strictStereo` set to False (a few molecules have unspecified stereochemistry, and setting this to false here will cause OEChem to pick a random stereoisomer in such cases, which is acceptable in this test)\n", - "- Use your `fitMolToReference` function to overlay your molecule/conformations onto the reference molecule. Do this twice (getting back two different sets of output) — once with ShapeColor = False and once with it True, so we can compare and find out which works better here.\n", - "- Store your scores (I store them to a dictionary keyed by molecule name) in a way which allows you to track which molecules they correspond to. \n", - "- Sort your molecule names by score (for each of the two sets of scores you have). I give an example of how to sort below.\n", - "- Compare your list of molecule names sorted by score with the list of actual binders (see code below) and identify where you find the actual binders. What you want to track at this stage is how many binders you find at or before each *rank* (entry number) in the list of sorted scores. For example, if there are 60 binders distributed across 650 molecules, you might find the first binder at the top of the list, the second binder at entry number 5, and the third binder at entry number 10. For every entry in the sorted list, store the number of binders found — so in this example, your first 10 entries would be `[ 1, 1, 1, 1, 2, 2, 2, 2, 2, 3]`. If this is still confusing, [see here](ranks.md) for a little more about this process.\n", - "- For each list of number of compounds found by rank (do this twice, once for each of your two sets of scores), convert it to a list of fraction of actual binders found by rank, by dividing each entry by the total number of binders in the set. For example, if you have found one binder and there are 60 in total, the fracion actives found is 1/60.\n", - "- Plot the fraction of binders found at each rank, versus the rank, for each of your two scoring schemes. This is an \"enrichment plot\".\n", - "- Also plot what would be expected if you picked compounds to call \"active\" at random. (Hint, in this approach you would expect to find no actives at rank 0, and all of the actives at rank N, with the fraction of actives found increasing linearly in between). This will allow you to determine whether your approach is doing better than random at recognizing likely active compounds." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "dO0jj6cUyP0i" - }, - "source": [ - "### Some code to get you started\n", - "\n", - "This shows how to read in the potential ligands, the reference molecule, and the actual binders." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "id": "6vjxUWMWyP0i" - }, - "outputs": [], - "source": [ - "from openeye.oechem import *\n", - "from openeye.oeiupac import *\n", - "from openeye.oequacpac import *\n", - "import pickle\n", - "\n", - "# Load the text file containing all the potential ligands\n", - "file = open('actual_w_maybridge.txt', 'r')\n", - "text = file.readlines()\n", - "file.close()\n", - "\n", - "#Generate OEMols for all of the potential ligands in our test set; store them to a dictionary by their compound name.\n", - "mol_by_name = {}\n", - "for line in text:\n", - " mol = OEMol()\n", - " tmp = line.split()\n", - " name=tmp[0]\n", - " smiles=tmp[1]\n", - " parsed = OEParseSmiles(mol, smiles)\n", - " if not parsed:\n", - " print(\"Warning, could not parse %s, pausing.\" % name)\n", - " raw_input()\n", - " mol_by_name[name] = mol\n", - " \n", - "#Load reference molecule which we will ultimately overlay onto\n", - "istream = oemolistream('3NF8_ligand.pdb')\n", - "refmol = OEMol()\n", - "OEReadMolecule( istream, refmol)\n", - "istream.close()\n", - "\n", - "#Standardize protonation state and make sure there are explicit hydrogens\n", - "OE3DToInternalStereo(refmol)\n", - "OESetNeutralpHModel(refmol)\n", - "OEAddExplicitHydrogens(refmol)\n", - "\n", - "\n", - "#For the purposes of telling how we did, load a list of the names of actual binders\n", - "#Load actual binders\n", - "file = open('final_clean_binders.txt', 'r')\n", - "text = file.readlines()\n", - "file.close()\n", - "binders = [ line.split()[0].split('_')[0] for line in text]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pjTvTVI0yP0j" - }, - "source": [ - "### Sorting lists" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "QgnpjVn-yP0j" - }, - "source": [ - "I mentioned I would give an example of a sorting a list. If I have scores stored in the dictionary `scores_by_name` and I wanted to make a list of names sorted by scores, I could do it this way: " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "OYGsIvf9yP0j", - "outputId": "520f50d8-668a-4532-9f11-c1dbccc834c3" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['b', 'a', 'c']\n" - ] - } - ], - "source": [ - "# Make up a dummy dictionary to sort\n", - "scores_by_name = {'a':32, 'b':11, 'c':43}\n", - "\n", - "# Make a list we want to sort\n", - "names = list(scores_by_name.keys())\n", - "\n", - "# Define function for sorting\n", - "def f(x):\n", - " return scores_by_name[x]\n", - "\n", - "# Sort, print sorted list\n", - "sorted_names = sorted( names, key=f )\n", - "print(sorted_names)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "zepVJxityP0j" - }, - "source": [ - "In reality you will have molecule names, and be sorting them by Tanimoto score. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "1sId6UnUyP0j" - }, - "source": [ - "### Write your code here\n", - "\n", - "Here, complete your assignment as described above:" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "-cfqgSTNynHP" + }, + "source": [ + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MobleyLab/drug-computing/blob/master/uci-pharmsci/assignments/3D_structure_shape/3D_structure_shape_assignment.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GH7gVE7WyP0a" + }, + "source": [ + "# 3D Structure/Shape Assignment\n", + "\n", + "## Overview\n", + "\n", + "This assignment involves two major parts. It first introduces the OEChem toolkits by having you perform some simple tasks with molecules in two and three dimensions, including generating 3D structures of molecules (much of which you will have already seen in the 3D structure/shape lecture and Jupyter notebook). You will also convert between common file formats and do simple shape overlays as well as visualization of your molecules. Some software installation will be required.\n", + "\n", + "In the second major part of this assignment, you will use shape overlays to try and recognize HIV integrase inhibitors from a small library of potential HIV integrase inhibitors.\n", + "\n", + "## More detailed overview\n", + "\n", + "This assignment works through common tasks for working with small molecules. Specifically, you want to model a molecule, and you need to build 2D and 3D structures of the molecule, save to a particular file format for visualization, and compare it to other molecules. Here, you get to do these for the first time. Many different tool sets do these tasks, but here I’ll introduce you to one particular tool set (from OpenEye software) that works particularly well with Python and is free to academics. You have already seen and used this set of tools in class in the 3D structure/shape Jupyter notebook. This assignment will simply build on that notebook. Especially, Step 0 through Step 3 replicate tasks done in that notebook.\n", + "\n", + "\n", + "After completing these initial tasks, you will then take a small library of potential HIV integrase inhibitors, and use a shape-based search to try and identify active compounds on the basis of shape similarity to known inhibitors." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "nvKQIyv-KtW-" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "okcvdgqHyP0c" + }, + "source": [ + "# Getting started/warm-up\n", + "\n", + "Before getting started, make a copy of this Jupyter notebook to work in and name it something else, such as `Mobley_3D_structure_shape.ipynb` if your last name is Mobley.\n", + "\n", + "***If you are running this on Google Colab, please add the installation blocks from the [getting started notebook](https://github.com/MobleyLab/drug-computing/blob/master/uci-pharmsci/Getting_Started.ipynb) or [condacolab](https://github.com/aakankschit/drug-computing/blob/master/uci-pharmsci/Getting_Started_condacolab.ipynb) here and then execute the code below***\n", + "\n", + "## Step 0: Install PyMol for visualization\n", + "\n", + "We've seen `nglview` already for interactive visualization, but it is not as useful for visualizing things which are neither (a) MD simulations, nor (b) single structures. Specifically, here we will generate multiple structures/conformations, so we need a bit better visualization.\n", + "\n", + "In this exercise, we’ll begin looking at and working with molecules in 3D, which means we need a viewer. PyMol is a popular general purpose one we will use extensively in this class. You should obtain the free academic version of PyMol online, but if you have any trouble with that please contact me. Alternate viewers like VMD or Chimera could also be used, but here I'll explain PyMol's usage.\n", + "\n", + "## Step 1: Build 2D structures of some molecules\n", + "\n", + "The first step is to build some molecules to work with. You might need to do this because your boss or research adviser asks you to model certain molecules, or you might come across some 2D structures of molecules in a research paper you would like to model. In any case, for whatever reason, you have a list of molecules to model, and you need to start by building their structures.\n", + "\n", + "Let’s start by building 2D structures. If you already have commercial or favorite software for this, like ChemDraw, feel free to use that. If not, I recommend Marvin, which is free, as noted in the 3D structure/shape lecture. You can use it [within your browser](https://marvinjs-demo.chemaxon.com/latest/); you may need to install a free Java plugin to do so) or [download it](https://www.chemaxon.com/products/marvin/marvinsketch/) and install it on your computer (http://www.chemaxon.com/download/marvin/for-end-users/). I normally use the desktop version. \n", + "\n", + "In Marvin, draw some structures of molecules (if you have some molecules you’re interested in because of another class or research, you should do them, otherwise just pick some) and save them to your computer (from the file->save as menu) as MDL SDF files, one at a time. \n", + "\n", + "Drawing them should produce something like this:\n", + "![image](https://raw.githubusercontent.com/MobleyLab/drug-computing/master/uci-pharmsci/lectures/3D_structure_shape/Marvin_molecule.png)\n", + "\n", + "Use the buttons at the bottom to create pre-formed rings; the tools at right can be used to select specific bond types or stereochemistry, and the buttons at right can be used to introduce elements other than carbon.\n", + "\n", + "You should select at least 5-10 molecules to build, of your choice. Try to include some which are relatively similar to one another (i.e. differing only by a functional group, for example) and some which are not.\n", + "\tOnce your molecules are built, I suggest loading them (or at least one) in PyMol (open PyMol, then go to File->Open, then select your file) and having a quick look. You should see something like the below, though obviously details will depend on your platform (OS X, Linux, Windows) and what you’ve named your molecules. I suggest clicking the “show” button (S, as indicated by the red arrow) and choosing the “sticks” representation so you can see your molecule more clearly. Try clicking it and dragging to rotate -- notice how the molecule is completely planar (two dimensional) right now, even though it shouldn’t be. If you would like, you can choose Display -> Show Valences to show bond types. Experiment with using S to show other representations (such as surface) if you like, and use H to hide representations you’re done with.\n", + " \n", + "![pymol](https://github.com/MobleyLab/drug-computing/blob/master/uci-pharmsci/assignments/3D_structure_shape/pymol.png?raw=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VReZXLoCyP0d" + }, + "source": [ + "## Step 2: Building 3D structures of molecules\n", + "\n", + "In the next step, you will want to build 3D structures of your molecules. I have provided some example Python code for this (and for other tasks) in the [`3D_Structure_shape.ipynb`](https://github.com/MobleyLab/drug-computing/blob/master/uci-pharmsci/lectures/3D_structure_shape/3D_Structure_Shape.ipynb) notebook we used in the relevant lecture. \n", + "\n", + "### First, do this for just one molecule\n", + "\n", + "**Put some code in the box just below to read in one of the molecules you drew and saved from the SDF file, generate a 3D conformation, and write it out to a mol2 file**, drawing on the examples from the lecture." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "id": "M8ij6GIkyP0e" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hNFQ_DNgyP0e" + }, + "source": [ + "### But we're going to want to do this for many molecules, so write some functions\n", + "\n", + "Here let's write functions to read molecules, write molecules, and generate conformations; we'll call the last one `expandConformations` because it could be used on a molecule which already has one conformation in order to generate many conformations, as well.\n", + "\n", + "We'll make these functions so you can easily reuse them on many molecules.\n", + "\n", + "I've written the documentation strings for you in the code below; your job is to fill in the inner workings and then make sure they work\n", + "\n", + "#### Write your functions here" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "id": "PlVLdvb0yP0e" + }, + "outputs": [], + "source": [ + "def readMolecule( inputfile ):\n", + " \"\"\"Take specified input molecule file with an OEChem supported molecular file format. Read a molecule from the file and return it as an OEChem OEMol molecule.\"\"\"\n", + "\n", + " #Insert your code (as seen in the example) here to read the input file 'inputfile'\n", + "\n", + " return oemol #Replace oemol with the name of your molecule object\n", + "\n", + "\n", + "def writeMolecule( oemol, outputfile):\n", + " \"\"\"Write a provided oemol to specified output file using an OEChem supported file format, as determined by the file extension (such as .mol2 or .sdf).\n", + "Arguments:\n", + "- oemol: OEMol molecule to write\n", + "- outputfile: File to write to.\n", + "Returns: Nothing\"\"\"\n", + "\n", + " #Insert your code (as seen in the example) here to write an output file\n", + "\n", + "def expandConformations( oemol, maxconfs = 100, strictStereo = True ):\n", + " \"\"\"Take a provided OEMol molecule, and use OpenEye's Omega to generate multiple conformations of the molecule, which are then returned in a new OEMol molecule.\n", + "Arguments:\n", + "- oemol: Provided molecule to generate conformations for\n", + "- maxconfs: Optional argument (default 100) specifying the maximum number of conformations to generate. Specify 1 to get just a single conformation.\n", + "- strictStereo: Optional argument specifying whether (True) or not (False) to use strict stereochemistry checking for Omega. Default: True.\n", + "Returns:\n", + "- expanded_oemol: OEMol containing the generated conformations.\"\"\"\n", + "\n", + " #Write your code here (as seen in the example)\n", + " return expanded_oemol" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "v8CSBG-VyP0f" + }, + "source": [ + "#### You probably want to test your functions now\n", + "You should probably use the box below to make sure you can read in one of the molecules you created, generate conformations, and write it back out again. If you've written your functions properly, this code should just work!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "MeNOf2gqyP0g", + "outputId": "3664b12a-15c6-49a9-936d-3c0499698bf1" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true, - "id": "gDREvMFsyP0j" - }, - "outputs": [], - "source": [ - "" - ] - }, + "ename": "NameError", + "evalue": "name 'readMolecule' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0minputfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'test.sdf'\u001b[0m \u001b[0;31m# A test molecule I provided\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mmol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mreadMolecule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputfile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0mexpandedmol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mexpandConformations\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mmol\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mwriteMolecule\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'test.mol2'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m#Write out to a mol2 file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'readMolecule' is not defined" + ] + } + ], + "source": [ + "inputfile = 'test.sdf' # A test molecule I provided\n", + "\n", + "mol = readMolecule(inputfile)\n", + "expandedmol = expandConformations( mol ) \n", + "writeMolecule('test.mol2') #Write out to a mol2 file" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OSAaPq5gyP0g" + }, + "source": [ + "Now you should probably check with PyMol that `test.mol2` has a molecule with a 3D conformer in it, or add visualization in this notebook using `nglview` to check." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "09Q1EVHryP0h" + }, + "source": [ + "## Step 3: Shape overlay your molecules\n", + "\n", + "### Get ready\n", + "Next, we want to do a shape overlay of your molecules so we can see how structurally similar they are. This is a common task in drug discovery, as we noted in class, and often can be used in several ways, such as finding out about the likely shape of a binding site based on the molecules which bind there, or finding new molecules which may fit a binding site while being chemically distinct from existing molecules based on their shape. \n", + "\n", + "Here, our goal will be to take your entire set of molecules and overlay them onto a common reference structure. Here's an example which does a shape (+color, where color basically measures chemical similarity) overlay for a pair of molecules; this is rather likewhat you saw in the 3D structure/shape lecture: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "phAZaasmyP0h", + "outputId": "9787e511-50c6-402a-c5ab-763ef47d07e5" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "PIawEUFMyP0j" - }, - "source": [ - "## What to submit\n", - "\n", - "For this assignment, please submit via the course website:\n", - "- From step 3, structure files for your reference molecule as well as for the best shape overlay onto that structure, with scores. Also submit a sentence or more describing what you found in this section.\n", - "- From step 4, Your resulting graph of “enrichment” of actual ligands from the set, with axes and curves labeled, along with this Jupyter notebook which produced it\n", - "- In a box below, or a separate document, a brief explanation of how you see the performance of the shape overlay approach, in view of the linked paper on the SAMPL4 challenge. Why does it do well, or poorly, in this case? Does ‘color’ scoring in addition to shape help? Why or why not? How does this compare to the “null” models tested in the paper? " - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "title: naproxen_13 tanimoto combo = 1.47\n", + "title: naproxen_0 tanimoto combo = 1.47\n", + "title: naproxen_16 tanimoto combo = 1.47\n", + "title: naproxen_12 tanimoto combo = 1.47\n", + "title: naproxen_1 tanimoto combo = 1.47\n", + "title: naproxen_8 tanimoto combo = 1.47\n", + "title: naproxen_4 tanimoto combo = 1.47\n", + "title: naproxen_5 tanimoto combo = 1.47\n", + "title: naproxen_9 tanimoto combo = 1.46\n", + "title: naproxen_17 tanimoto combo = 1.46\n" + ] } - ], - "metadata": { - "colab": { - "name": "3D_structure_shape_assignment.ipynb", - "provenance": [] - }, - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.12" - }, - "toc": { - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "toc_cell": false, - "toc_position": {}, - "toc_section_display": "block", - "toc_window_display": false + ], + "source": [ + "from openeye.oechem import *\n", + "from openeye.oeomega import *\n", + "from openeye.oeiupac import *\n", + "from openeye.oeshape import *\n", + "\n", + "omega = OEOmega() #Initialize class\n", + "omega.SetMaxConfs(100) #Here we want to use more conformers if needed\n", + "omega.SetStrictStereo(False) #Set to false to pick random stereoisomer if stereochemistry is not specified\n", + "\n", + "# Make some molecules\n", + "ibuprofen = OEMol()\n", + "naproxen = OEMol()\n", + "OEParseIUPACName(ibuprofen, 'ibuprofen')\n", + "OEParseIUPACName(naproxen, 'naproxen')\n", + "\n", + "#Set up reference molecule\n", + "refmol = ibuprofen\n", + "omega(refmol)\n", + "\n", + "#Fit molecule\n", + "fitmol = naproxen\n", + "omega(fitmol)\n", + "fitmol.SetTitle('naproxen')\n", + "\n", + "# Open output stream for output molecule\n", + "outfs = oemolostream('fitted_output.mol2')\n", + "# How many conformers will we generate for fitting?\n", + "nconfs = 10\n", + "\n", + "# Setup ROCS to provide specified number of conformers per hit\n", + "options = OEROCSOptions()\n", + "options.SetNumBestHits(nconfs)\n", + "options.SetConfsPerHit(nconfs)\n", + "rocs = OEROCS(options)\n", + "rocs.AddMolecule(fitmol) #Add our molecule as the one we are fitting\n", + "\n", + "# Loop over results and output\n", + "for res in rocs.Overlay(refmol):\n", + " outmol = res.GetOverlayConfs() #Use GetOverlayConf to get just the best; GetOverlayConfs for all\n", + " OERemoveColorAtoms(outmol)\n", + " OEAddExplicitHydrogens(outmol)\n", + " OEWriteMolecule(outfs, outmol)\n", + " print(\"title: %s tanimoto combo = %.2f\" % (outmol.GetTitle(), res.GetTanimotoCombo()))\n", + "outfs.close()\n", + "\n", + "# If you wanted, you could also print just the \"color\" similarity:\n", + "#print(res.GetColorTanimoto())\n", + "# Or just the shape similarity:\n", + "#print(res.GetShapeTanimoto())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "owfnNbolyP0h" + }, + "source": [ + "### Make a function to do this\n", + "\n", + "Again, we'd be better off with a function to do this (and we'll use this function also below in the ligand-based design portion of the assignment, below). So, edit the code below and complete the `fitMoleculeToReference` function. The idea of this function is to take two molecules and compare them based on shape. The code here is a little complicated (which is why I’ve written similar code for you) and the only things you should have to change are the variable names and the indentation. What this ultimately will do is take two molecules, overlay them in a number of different ways, and then return (give back to the user) the actual overlays of the molecules, sorted from best to worst, and the “Tanimoto” scores of the overlays in the same order. Tanimoto scores are scores running from 0 to 1 and essentially measure similarity, where 0 means totally dissimilar and 1 means totally identical. Here, we are actually combining TWO Tanimoto scores (a ‘shape’ Tanimoto score and a ‘color’ (Chemistry) tanimoto score) so our overall Tanimoto score runs from 0 to 2.\n", + "\n", + "Once you think you’ve got your `fitMoleculeToReference` function working, test it on a specific pair of molecules, then use `writeMolecule` (as above) to write out the reference molecule and the fitted molecule (to .mol2 or .sdf format) and view them in PyMol (after downloading to your computer) to ensure it’s working.\n", + "\n", + "When you have the function working, then set up a loop over all of your molecules except your chosen reference molecule, ‘molecule1’, to overlay all of the molecules onto it. Before the loop, set up a list to store the best score for each pair of molecules. Inside the loop, fit each molecule onto the reference and then store the resulting fits to files (being careful not to overwrite any of your existing files). Also store the best score to the list of scores. Also have your program print out the scores. Previously, we would also visualize the structures (such as with PyMol), but currently OpenEye's Shape toolkit does not output the structures overlaid onto one another (without extra work) so this is not particularly helpful at present and you can skip visualization.\n", + "\n", + "For this portion of the assignment, you should submit your reference molecule’s structure (.mol2 or .sdf) as well as the structure of the best overlay you found, with its score. You should also include a sentence or more explaining what you found in this step in your e-mail." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "id": "e9Avnr34yP0i" + }, + "outputs": [], + "source": [ + "def fitMoleculeToReference( fitmol, refmol, ShapeColor = True):\n", + " \"\"\"Take a multi-conformation molecule to be fitted, and fit it onto a reference molecule using shape overlays.\n", + "Arguments:\n", + "- fitmol: Molecule to fit onto the reference molecule; should be multiple-conformation\n", + "- refmol: Reference molecule to fit onto\n", + "- ShapeColor: Optional argument specifying whether to score overlays based on shape alone (False) or to score based on both shape and color fit (True); in the latter case scores will run from 0 to 2 while in the former they will run from 0 to 1. Default: True.\n", + "Returns:\n", + "- tanimotos: Tanimoto scores of the fitted conformations.\n", + "- fitted: The fitted conformations, ordered by score from best to worst\n", + "\"\"\"\n", + "\n", + " #Write your code here, as seen in the example\n", + "\n", + " return (tanimotos, fitted)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Eb-WugN9yP0i" + }, + "source": [ + "## Step 4: Ligand-based design\n", + "\n", + "Your next step involves a simple application of this shape overlay technique to a real problem. You will take an existing ligand -- an HIV-1 integrase inhibitor -- from a structure the protein data bank (PDB) and compare this to a set of molecules which were tested experimentally for possible HIV integrase inhibition. The idea is to see whether the molecules which actually are HIV integrase inhibitors are among those which are most shape similar to the known inhibitor. \n", + "\n", + "Here, our exact plan is to take an existing list of 600-700 compounds which were tested for their ability to inhibit HIV-1 integrase. The 3NF8 structure in the PDB gives the crystal structure of one integrase inhibitor from this series bound to HIV-1 integrase. We will use the ligand from that structure as a query, or reference molecule, to see if it easily allows us to recognize other molecules which are likely to bind from our larger set of 600-700 compounds. This somewhat echoes the SAMPL4 HIV integrase challenge reported in [this paper]( http://link.springer.com/article/10.1007/s10822-014-9723-5), though the set of inactive compounds has been expanded somewhat by addition of a portion of the Maybridge fragment library which was also tested experimentally.\n", + "\n", + "You will find several supporting files in this directory to help with this part of the assignment:\n", + "- `actual_w_maybridge.txt`: List of compounds actually tested\n", + "- `3NF8_ligand.pdb`: To simplify your life, I downloaded the 3NF8 structure and extracted the ligand of interest from it to serve as your reference molecule.\n", + "- `final_clean_binders.txt`: “Answers” — a text file which can be parsed (by code provided below) into a list of names of binders, which can be used to check your results.\n", + "Here is what you should do to complete the assignment, starting from the code provided below and the functions you wrote for in Steps 1-3 above:\n", + "\n", + "- Set up a loop over the molecules in the set\n", + "- For every molecule, generate a 3D conformation with your `expandConformations` function, using `strictStereo` set to False (a few molecules have unspecified stereochemistry, and setting this to false here will cause OEChem to pick a random stereoisomer in such cases, which is acceptable in this test)\n", + "- Use your `fitMolToReference` function to overlay your molecule/conformations onto the reference molecule. Do this twice (getting back two different sets of output) — once with ShapeColor = False and once with it True, so we can compare and find out which works better here.\n", + "- Store your scores (I store them to a dictionary keyed by molecule name) in a way which allows you to track which molecules they correspond to. \n", + "- Sort your molecule names by score (for each of the two sets of scores you have). I give an example of how to sort below.\n", + "- Compare your list of molecule names sorted by score with the list of actual binders (see code below) and identify where you find the actual binders. What you want to track at this stage is how many binders you find at or before each *rank* (entry number) in the list of sorted scores. For example, if there are 60 binders distributed across 650 molecules, you might find the first binder at the top of the list, the second binder at entry number 5, and the third binder at entry number 10. For every entry in the sorted list, store the number of binders found — so in this example, your first 10 entries would be `[ 1, 1, 1, 1, 2, 2, 2, 2, 2, 3]`. If this is still confusing, [see here](ranks.md) for a little more about this process.\n", + "- For each list of number of compounds found by rank (do this twice, once for each of your two sets of scores), convert it to a list of fraction of actual binders found by rank, by dividing each entry by the total number of binders in the set. For example, if you have found one binder and there are 60 in total, the fracion actives found is 1/60.\n", + "- Plot the fraction of binders found at each rank, versus the rank, for each of your two scoring schemes. This is an \"enrichment plot\".\n", + "- Also plot what would be expected if you picked compounds to call \"active\" at random. (Hint, in this approach you would expect to find no actives at rank 0, and all of the actives at rank N, with the fraction of actives found increasing linearly in between). This will allow you to determine whether your approach is doing better than random at recognizing likely active compounds." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dO0jj6cUyP0i" + }, + "source": [ + "### Some code to get you started\n", + "\n", + "This shows how to read in the potential ligands, the reference molecule, and the actual binders." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "id": "6vjxUWMWyP0i" + }, + "outputs": [], + "source": [ + "from openeye.oechem import *\n", + "from openeye.oeiupac import *\n", + "from openeye.oequacpac import *\n", + "import pickle\n", + "\n", + "# Load the text file containing all the potential ligands\n", + "file = open('actual_w_maybridge.txt', 'r')\n", + "text = file.readlines()\n", + "file.close()\n", + "\n", + "#Generate OEMols for all of the potential ligands in our test set; store them to a dictionary by their compound name.\n", + "mol_by_name = {}\n", + "for line in text:\n", + " mol = OEMol()\n", + " tmp = line.split()\n", + " name=tmp[0]\n", + " smiles=tmp[1]\n", + " parsed = OEParseSmiles(mol, smiles)\n", + " if not parsed:\n", + " print(\"Warning, could not parse %s, pausing.\" % name)\n", + " raw_input()\n", + " mol_by_name[name] = mol\n", + " \n", + "#Load reference molecule which we will ultimately overlay onto\n", + "istream = oemolistream('3NF8_ligand.pdb')\n", + "refmol = OEMol()\n", + "OEReadMolecule( istream, refmol)\n", + "istream.close()\n", + "\n", + "#Standardize protonation state and make sure there are explicit hydrogens\n", + "OE3DToInternalStereo(refmol)\n", + "OESetNeutralpHModel(refmol)\n", + "OEAddExplicitHydrogens(refmol)\n", + "\n", + "\n", + "#For the purposes of telling how we did, load a list of the names of actual binders\n", + "#Load actual binders\n", + "file = open('final_clean_binders.txt', 'r')\n", + "text = file.readlines()\n", + "file.close()\n", + "binders = [ line.split()[0].split('_')[0] for line in text]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pjTvTVI0yP0j" + }, + "source": [ + "### Sorting lists" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QgnpjVn-yP0j" + }, + "source": [ + "I mentioned I would give an example of a sorting a list. If I have scores stored in the dictionary `scores_by_name` and I wanted to make a list of names sorted by scores, I could do it this way: " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "OYGsIvf9yP0j", + "outputId": "520f50d8-668a-4532-9f11-c1dbccc834c3" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['b', 'a', 'c']\n" + ] } + ], + "source": [ + "# Make up a dummy dictionary to sort\n", + "scores_by_name = {'a':32, 'b':11, 'c':43}\n", + "\n", + "# Make a list we want to sort\n", + "names = list(scores_by_name.keys())\n", + "\n", + "# Define function for sorting\n", + "def f(x):\n", + " return scores_by_name[x]\n", + "\n", + "# Sort, print sorted list\n", + "sorted_names = sorted( names, key=f )\n", + "print(sorted_names)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zepVJxityP0j" + }, + "source": [ + "In reality you will have molecule names, and be sorting them by Tanimoto score. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1sId6UnUyP0j" + }, + "source": [ + "### Write your code here\n", + "\n", + "Here, complete your assignment as described above:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true, + "id": "gDREvMFsyP0j" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "PIawEUFMyP0j" + }, + "source": [ + "## What to submit\n", + "\n", + "For this assignment, please submit via the course website:\n", + "- This notebook\n", + "- From step 3, structure files for your reference molecule as well as for the best shape overlay onto that structure, with scores. Also submit a sentence or more describing what you found in this section.\n", + "- From step 4, Your resulting graph of “enrichment” of actual ligands from the set, with axes and curves labeled, along with this Jupyter notebook which produced it\n", + "- In a box below, or a separate document, a brief explanation of how you see the performance of the shape overlay approach, in view of the linked paper on the SAMPL4 challenge. Why does it do well, or poorly, in this case? Does ‘color’ scoring in addition to shape help? Why or why not? How does this compare to the “null” models tested in the paper? " + ] + } + ], + "metadata": { + "colab": { + "name": "3D_structure_shape_assignment.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "toc": { + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "toc_cell": false, + "toc_position": {}, + "toc_section_display": "block", + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/uci-pharmsci/assignments/MD/MD.ipynb b/uci-pharmsci/assignments/MD/MD.ipynb index e393561..3ce4a4f 100644 --- a/uci-pharmsci/assignments/MD/MD.ipynb +++ b/uci-pharmsci/assignments/MD/MD.ipynb @@ -93,7 +93,7 @@ "\n", "The left-hand term is the kinetic energy, and here can be simplified by noting all of the masses in the system are defined to be 1. The right hand term involves the Boltzmann constant, the number of particles in the system, and the instantaneous temperature.\n", "\n", - "So, you can compute the effective temperature of your system, and translate this into a scaling factor which you can use to multiply (scale) all velocities in the system to ensure you get the correct average temperature (see http://www.pages.drexel.edu/~cfa22/msim/node33.html). **Specifically, following the Drexel page (eq. 177), compute a (scalar) constant by which you will multiply all of the velocities to ensure that the effective temperature is at the correct target value after rescaling.** To do this calculation you will need to compute the kinetic energy, which involves the sum above. \n", + "So, you can compute the effective temperature of your system, and translate this into a scaling factor which you can use to multiply (scale) all velocities in the system to ensure you get the correct average temperature (see https://www.compchems.com/thermostats-in-molecular-dynamics/#velocity-rescaling). **Specifically, following the page above, compute a (scalar) constant $\\lambda$ by which you will multiply all of the velocities to ensure that the effective temperature is at the correct target value after rescaling.** To do this calculation you will need to compute the kinetic energy, which involves the sum above. \n", " \n", "Remove translational motion, rescale the velocities, and have your function return the updated velocity array.\n", "Once the above two functions are written, finishing write a simple MD code using the available functions to:\n", @@ -105,7 +105,7 @@ "\n", "Use the settings given above for $N$, $\\rho$, $T$, the timestep, and the cutoff distance. \n", "\n", - "Perform simulations for $M = 2, 4, 6, 8, 12,$ and $16$ and store the total energies versus time out to 2,000 timesteps. (Remember, $M$ controls the number of particles per polymer; you are keeping the same total number of particles in the system and changing the size of the polymers). On a single graph, plot the potential energy versus time for each of these cases (each in a different color). Turn in this graph. \n", + "Perform simulations for $M = 2, 4, 6, 8, 12,$ and $16$ and store the total energies (kinetic plus potential) versus time out to 2,000 timesteps. (Remember, $M$ controls the number of particles per polymer; you are keeping the same total number of particles in the system and changing the size of the polymers). On a single graph, plot the potential energy versus time for each of these cases (each in a different color). Turn in this graph. \n", "\n", "Note also you can visualize, if desired, using the Python module for writing to PDB files which you saw in the Energy Minimization exercise. \n", "\n", @@ -114,12 +114,12 @@ "\n", "Modify your MD code from above to perform a series of steps that will allow you to compute the self-diffusion coefficient as a function of chain length and determine how diffusion of polymers depends on the size of the polymer. To compute the self-diffusion coefficient, you will simply need to monitor the motion of each polymer in time. \n", "\n", - "Here, you will first perform two equilibrations at constant temperature using velocity rescaling . The first will allow the system to reach the desired temperature and forget about its initial configuration (remember, it was started on a lattice). The second will allow you to compute the average total energy of the system. Then, you will fix the total energy at this value and perform a production simulation. \n", + "Here, you will first perform two equilibrations at constant temperature using velocity rescaling . The first will allow the system to reach the desired temperature and forget about its initial configuration (remember, it was started on a lattice). The second will allow you to compute the average total energy (kinetic plus potential) of the system. Then, you will fix the total energy at this value and perform a production simulation. \n", "\n", "Here’s what you should do:\n", "* Following initial preparation (like above), first perform equilibration for NStepsEquil1 using velocity rescaling to target temperature T every RescaleFreq steps, using whatever value of RescaleFreq you used previously (NOT every step!)\n", - "* Perform a second equilibration for `NStepsEquil2` timesteps using velocity rescaling at the same frequency, storing energies while you do this. \n", - "* Compute the average total energy over this second equilibration and rescale the velocities to start the final phase with the correct total energy. In other words, the total energy at the end of equilibration will be slightly above or below the average; you should find the kinetic energy you need to have to get the correct total energy, and rescale the velocities to get this kinetic energy. After this you will be doing no more velocity rescaling. (Hint: You can do this final rescaling easily by computing a scaling factor, and you will probably not be using the rescaling code you use to maintain the temperature during equilibration.)\n", + "* Perform a second equilibration for `NStepsEquil2` timesteps using velocity rescaling at the same frequency, storing energies (kinetic and potential, separately) while you do this. \n", + "* Compute the average total energy (kinetic plus potential) over this second equilibration and rescale the velocities to start the final phase with the correct total energy. In other words, the total energy at the end of equilibration will be slightly above or below the average; you should find the kinetic energy you need to have to get the correct total energy, and rescale the velocities to get this kinetic energy. After this you will be doing no more velocity rescaling. (Hint: You can do this final rescaling easily by computing a scaling factor, and you will probably not be using the rescaling code you use to maintain the temperature during equilibration.)\n", "* Copy the initial positions of the particles into a reference array for computing the mean squared displacement, for example using `Pos0 = Pos.copy()`\n", "* Perform a production run for `NStepsProd` integration steps with constant energy (NVE) rather than velocity rescaling. Periodically record the time and mean squared displacement of the atoms from their initial positions. (You will need to write a small bit of code to compute mean squared displacements, but it shouldn’t take more than a couple of lines; you may send it to me to check if you are concerned about it). The mean squared displacement is given by \n", "\\begin{equation}\n", @@ -157,7 +157,7 @@ "You can send your comments/discussion as an e-mail, and the rest of the items as attachments.\n", "\n", "### What’s provided for you: \n", - "In this case, most of what you need is provided in the importable module `MD_functions.py` (which you can view with your favorite text editor, like `vi` or Atom), except for the functions for initial velocities and velocity rescaling -- in those cases, the shells are present below and you need to write the core (which will be very brief!). **However, you will also need to insert the code for the `ConjugateGradient` function in `MD_functions.py`** from your work you did in the Energy Minimization assignment. If you did not do this, or did not get it correct (or if you are not certain if you did), you will need to e-mail David Mobley for solutions.\n", + "In this case, most of what you need is provided in the importable module `MD_functions.py` (which you can view with your favorite text editor, like `vi` or Atom), except for the functions for initial velocities and velocity rescaling -- in those cases, the shells are present below and you need to write the core (which will be very brief!). **However, you will also need to insert the code for the `ConjugateGradient` function in `MD_functions.py`**. This is similar to (but not identical to) the ConjugateGradient code from the energy minimization assignment -- specifically, now our particles are joined into polymers, meaning that the function needs slightly different arguments to pass to energy and force evaluation routines. \n", "\n", "From the Fortran library `mdlib` (which you will compile as usual via `f2py3 -c -m mdlib mdlib.f90` or similar), the only new function you need is Velocity Verlet. In `MD_functions.py`, the following tools are available (this shows their documentation, not the details of the code, but you should only need to read the documentation in order to be able to use them. NOTE: No modification of these functions is needed; you only need to use them. You will only need to write `InitVelocities` and `RescaleVelocities` as described above, plus provide your previous code for `ConjugateGradient`: \n" ] @@ -358,9 +358,8 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { - "collapsed": true, "id": "uezEQuk55C2F" }, "outputs": [], @@ -374,7 +373,7 @@ "#Set up as in A\n", "#Equilibrate for NStepsEquil1 with velocity rescaling every RescaleFreq steps, discarding energies\n", "#Equilibrate for NStepsEquil2 with velocity rescaling, storing energies\n", - "#Stop and average the energy. Rescale the velocities so the current (end-of-equilibration) energy matches the average\n", + "#Stop and average the toal energy. Rescale the velocities so the current (end-of-equilibration) total energy matches the average total energy.\n", "#Store the particle positions so you can later compute the mean squared displacement\n", "#Run for NStepsProd at constant energy (NVE) recording time and mean squared displacement periodically\n", "\n", @@ -416,7 +415,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.12.8" }, "toc": { "nav_menu": {}, diff --git a/uci-pharmsci/assignments/energy_minimization/energy_minimization_assignment.ipynb b/uci-pharmsci/assignments/energy_minimization/energy_minimization_assignment.ipynb index 99193f6..0094a4f 100644 --- a/uci-pharmsci/assignments/energy_minimization/energy_minimization_assignment.ipynb +++ b/uci-pharmsci/assignments/energy_minimization/energy_minimization_assignment.ipynb @@ -65,16 +65,11 @@ "iPython notebooks such as this one often contain a variety of cells containing code. These are normally intended to be run, which you can when you have an individual cell selected, by hitting the button at the top with a 'play' symbol, or by typing shift-enter. If you do NOT do so on each of the cells defining variables/functions which will be used here, then you will encounter an error about undefined variables when you run the later cells. \n", " \n", "## Your step 1 for the assignment: Start by doing some file bookkeeping:\n", - " * Find `emlib.f90` and optional utility `pos_to_pdb.py` in this directory.\n", - " * In the command prompt navigate to that folder and type 'f2py3 -c -m emlib emlib.f90' which should compile the fortran library for use within python (For more on F2Py, refer to the [f2py documentation](https://numpy.org/doc/stable/f2py)). In OS X, this may require you to install the (free) XCode developer tools (available from the Mac App store) and the command-line developer tools first (the latter via `xcode-select --install`). In Linux it should just work. Windows would be a hurdle.\n", + " * Find `emlib.f90` and optional utility `pos_to_pdb.py` in this directory. **If you are on Google Colab**, you will need to have uploaded this library and/or the directory enclosing it to Google Drive, mount Google Drive, and navigate to the directory containing this library.** See notes below on \"Installing Packages\" as well as background in `Getting_Started.ipynb`.\n", + " * In the command prompt navigate to that folder and type 'f2py -c -m emlib emlib.f90' which should compile the fortran library for use within python (For more on F2Py, refer to the [f2py documentation](https://numpy.org/doc/stable/f2py)). In OS X, this may require you to install the (free) XCode developer tools (available from the Mac App store) and the command-line developer tools first (the latter via `xcode-select --install`). In Linux it should just work. Windows would be a hurdle.\n", " * In your command prompt, start theis Jupyter notebook (in OSX this would be something like 'Jupyter notebook energy_minimization_assignment'), which should open it in your web browser; you're running it already unless you are looking at the HTML version of this file.\n", " \n", - "Template Python code for the assignment is provided below. I suggest making a new notebook which is a copy of this one (perhaps containing your name in the filename) and working from there. \n", - " \n", - " \n", - " ## Next, we prep Python for the work:\n", - " \n", - " First we import the numpy numerical library we're going to need, as well as the compiled Fortran library emlib" + "Template Python code for the assignment is provided below. I suggest making a new notebook which is a copy of this one (perhaps containing your name in the filename) and working from there. \n" ] }, { @@ -85,22 +80,32 @@ "source": [ "## Installing Packages\n", "\n", - "***If you are running this on Google Colab, please add the installation blocks from the [getting started notebook](https://github.com/MobleyLab/drug-computing/blob/master/uci-pharmsci/Getting_Started.ipynb) or [condacolab](https://github.com/aakankschit/drug-computing/blob/master/uci-pharmsci/Getting_Started_condacolab.ipynb) here and then execute the code below***" + "If you are running this notebook locally, you should have installed everything already. If you are running **on Google Colab** you will likely need to install prerequisites. (On occasion, Google Colab has had a fortran compiler installed already, meaning it might not be necessary to install anything, but this is typically not the case.)\n", + "\n", + "If you are running this on Google Colab, you will typically need to add the installation blocks from the [getting started notebook](https://github.com/MobleyLab/drug-computing/blob/master/uci-pharmsci/Getting_Started.ipynb) here **in a new code block** and then execute the code below. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Next, we prep Python for the work:\n", + " \n", + " First we import the numpy numerical library we're going to need, as well as the compiled Fortran library emlib" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { - "collapsed": true, "id": "DiyM0EjL5cdN" }, "outputs": [], "source": [ "import numpy as np\n", "import emlib\n", - "from pos_to_pdb import * \n", - "#This would allow you to export coordinates if you want, later" + "#from pos_to_pdb import * #This would allow you to export coordinates if you want, later" ] }, { @@ -134,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "id": "Cz_zeUNd5cdP" }, @@ -283,7 +288,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "id": "FoKqhioL5cdQ", "outputId": "132969c7-2e96-439f-fe88-9ca13c98281d" @@ -294,10 +299,10 @@ "output_type": "stream", "text": [ "a=\n", - " [ 0.27969529 0.37836589 0.96785443]\n", + " [0.89598508 0.71295953 0.01997318]\n", "b=\n", - " [[ 0.37068791 0.64081204 0.21422213]\n", - " [ 0.471194 0.28575791 0.54468387]]\n" + " [[0.09347335 0.12438994 0.73166013]\n", + " [0.19155384 0.72085277 0.76497705]]\n" ] } ], @@ -618,9 +623,9 @@ "provenance": [] }, "kernelspec": { - "display_name": "drugcomp", + "display_name": "drugcomp23", "language": "python", - "name": "drugcomp" + "name": "drugcomp23" }, "language_info": { "codemirror_mode": { @@ -632,7 +637,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.16" }, "toc": { "nav_menu": {}, diff --git a/uci-pharmsci/assignments/library_searching/library_searching_asignment.ipynb b/uci-pharmsci/assignments/library_searching/library_searching_asignment.ipynb index 0de21ad..d9d1772 100644 --- a/uci-pharmsci/assignments/library_searching/library_searching_asignment.ipynb +++ b/uci-pharmsci/assignments/library_searching/library_searching_asignment.ipynb @@ -1,690 +1,685 @@ { - "cells": [ - { - "cell_type": "markdown", - "source": [ - "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MobleyLab/drug-computing/blob/master/uci-pharmsci/assignments/library_searching/library_searching_asignment.ipynb)" - ], - "metadata": { - "id": "CUqMDtgw1VH0" - } - }, - { - "cell_type": "markdown", - "metadata": { - "id": "U62m97Om6GKH" - }, - "source": [ - "# Docking and library searching assignment, PharmSci 175/275\n", - "\n", - "## Objective\n", - "In this assignment you will perform one or more sample library searches to examine docking performance on a test set or test sets. This assignment will differ somewhat from the others in that it gives you more of a chance to explore.\n", - "\n", - "## Overview\n", - "As discussed in class, docking methods are one of the fairly widely used computational methods for studying binding, and have seen significant applications in the area of virtual screening of libraries of potential ligands to filter out compounds which are unlikely to be active and hopefully increase the success rate of finding active compounds via experiments. These methods, while fairly fast, are still typically require a few seconds per compound or more, and thus often are unsuited for filtering large libraries of millions of compounds. In such cases, even faster methods such as LINGO searches may be used for pre-filtering.\n", - "\n", - "Here, I am providing you with sample scripts to perform a test LINGO search to filter a library of several million compounds down to a smaller number which have similarity to a known ligand, then to dock these to a target receptor and look at enrichment of active compounds. The test case I have provided is binding of various small molecules to the LEDGF site of HIV-1 integrase, as seen in the 3D structure/shape assignment and as described in the SAMPL4 paper referenced below.\n", - "\n", - "In this exercise, you will work through the example I provide, then perform tests of your own on at least three different data sets to see how well docking and LINGO searches will perform for you. You should also examine different scoring functions (within OpenEye’s docking toolkit). Think of this assignment as a bit of a sandbox - you are given some tools and a chance to play around. It is likely that the ideas you generate will help me make this assignment more successful the next time around, and any feedback is appreciated.\n", - "\n", - "# Your assignment\n", - "\n", - "Here, you will start off by doing a couple example activities so you can see how to use the OpenEye tools to do LINGO searches and docking. Once you’ve done these, then you will have the opportunity to branch out, try other things, and see what will work best on the system(s) you are looking at.\n", - "\n", - "As background for this, I suggest you read, if you have not already done so, the paper on the [SAMPL4 HIV integrase challenge](http://dx.doi.org/10.1007/s10822-014-9723-5), as well as the [DUD paper](http://dx.doi.org/10.1021/jm0608356); the later [DUD-E paper](http://dx.doi.org/10.1021/jm300687e) may also be helpful.\n", - "\n", - "Here, your warm-up work will relate to the HIV-1 integrase system studied in the SAMPL4 challenge (and seen in the 3D structure/shape assignment).\n", - "\n", - "## Warm-up Part 1: Lingo search and other prep\n", - "\n", - "Here, your task is to do a simple test of the Lingo search method by downloading a subset of the ZINC database, hiding some known integrase inhibitors in it, and testing the ability of the LINGO search method to enrich the database for known HIV-1 integrase inhibitors.\n", - "\n", - "### First get some compounds to work with\n", - "\n", - "As a starting point, let's get some compounds to work with. Visit the ZINC15 database (zinc15.docking.org), and click through on \"Tranches\" (subsets) towards the bottom left. This allows you to select all available compounds or subsets by purchaseability, molecular weight, calculated logP, etc. (You can toggle on and off rows and columns by clicking the top or left columns or rows), and filter by reaction likelihood (top menu) or wait time (top menu) and other properties. Select some subset of compounds (600 million is probably too many; let's keep it to a couple million or less) using these options. \n", - "\n", - "Once you've selected a subset (I filtered to get \"clean\" \"in stock\" compounds between 200-400 Daltons in some of the middle logP ranges) click the download buttom at the top right to pop up a \"Download Tranches\" menu. **Don't actually download**. Just copy the listed tranche codes; we can use those here to make your life easier.\n", - "\n", - "Now paste your tranche codes into the box below as a string called `tranches`, like this:" - ] - }, - { - "cell_type": "markdown", - "source": [ - "## Installing Packages\n", - "\n", - "***If you are running this on Google Colab, please add the installation blocks from the [getting started notebook](https://github.com/MobleyLab/drug-computing/blob/master/uci-pharmsci/Getting_Started.ipynb) or [condacolab](https://github.com/aakankschit/drug-computing/blob/master/uci-pharmsci/Getting_Started_condacolab.ipynb) here and then execute the code below***" - ], - "metadata": { - "id": "zKthp7bv6ICn" - } - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "IxEB1QCy6GKK" - }, - "outputs": [], - "source": [ - "# Tranches. Replace these with your selected tranches\n", - "tranches = \"ABAA ABAB ABBA ABBB ABCA ABCB BBAA BBAB BBBA BBBB BBCA BBCB CBAA CBAB CBBA CBBB CBCA CBCB DBAA DBAB DBBA DBBB DBCA DBCB EBAA EBAB EBBA EBBB EBCA EBCB FBAA FBAB FBBA FBBB FBCA FBCB ACAA ACAB ACBA ACBB ACCA ACCB BCAA BCAB BCBA BCBB BCCA BCCB CCAA CCAB CCBA CCBB CCCA CCCB DCAA DCAB DCBA DCBB DCCA DCCB ECAA ECAB ECBA ECBB ECCA ECCB FCAA FCAB FCBA FCBB FCCA FCCB ADAA ADAB ADBA ADBB ADCA ADCB BDAA BDAB BDBA BDBB BDCA BDCB CDAA CDAB CDBA CDBB CDCA CDCB DDAA DDAB DDBA DDBB DDCA DDCB EDAA EDAB EDBA EDBB EDCA EDCB FDAA FDAB FDBA FDBB FDCA FDCB AEAA AEAB AEBA AEBB AECA AECB BEAA BEAB BEBA BEBB BECA BECB CEAA CEAB CEBA CEBB CECA CECB DEAA DEAB DEBA DEBB DECA DECB EEAA EEAB EEBA EEBB EECA EECB FEAA FEAB FEBA FEBB FECA FECB AFAA AFAB AFBA AFBB AFCA AFCB BFAA BFAB BFBA BFBB BFCA BFCB CFAA CFAB CFBA CFBB CFCA CFCB DFAA DFAB DFBA DFBB DFCA DFCB EFAA EFAB EFBA EFBB EFCA EFCB FFAA FFAB FFBA FFBB FFCA FFCB AGAA AGAB AGBA AGBB AGCA AGCB BGAA BGAB BGBA BGBB BGCA BGCB CGAA CGAB CGBA CGBB CGCA CGCB DGAA DGAB DGBA DGBB DGCA DGCB EGAA EGAB EGBA EGBB EGCA EGCB FGAA FGAB FGBA FGBB FGCA FGCB\"" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "viv9OiTo6GKL" - }, - "source": [ - "Now we'll use the code below (using Python's `urllib` module) to download the files from these tranches and link them together into one big text file for further processing (so we don't have to download again if we need them again), and also save them to the list `zinc_compounds`. \n", - "\n", - "The file may be large. (e.g. I downloaded about 4.5 million compounds and ended up with a roughly 260 MB file; this could be reduced using compression but it's a lot of data regardless).\n", - "\n", - "If you wanted to do an even larger number of compounds, you would probably want to process one tranche at a time rather than grouping them all up into one large set like I'm doing here. \n", - "\n", - "**You should just need to run this code**:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "8yvlBw-t6GKL" - }, - "outputs": [], - "source": [ - "# Split up the string of tranches into a list\n", - "tranches_elements = tranches.split()\n", - "\n", - "# Set up an output file and list for storing results\n", - "filename = 'zinc_compounds.smi'\n", - "out_file = open(filename, 'w')\n", - "zinc_compounds = []\n", - "\n", - "# Loop over SMILES string files for desired tranches \n", - "import urllib\n", - "for t_id in tranches_elements:\n", - " prefix = t_id[0:2]\n", - " # Retrieve file-like object for iterating over contents of this one\n", - " contents = urllib.request.urlopen('http://files.docking.org/2D/' + prefix + '/'+ t_id +'.smi')\n", - " # Loop over each line, decode from bytes format to string, write to output. Skip the first line of each file\n", - " # which is a header line\n", - " for line in contents:\n", - " if line.split()[0].decode() != 'smiles':\n", - " # Replace some special characters at the same time\n", - " out_file.write(line.decode().replace('\\n',''))\n", - " zinc_compounds.append(line.decode().replace('\\r\\n',''))\n", - " \n", - "out_file.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "6Wcl-Il56GKM" - }, - "source": [ - "### Now retrieve the SAMPL4 compounds from a file" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "TTG4zs5z6GKM" - }, - "source": [ - "Next, you need to load the SAMPL4 compounds and insert them in your set. These are present in this directory as `SAMPL4_smiles.smi`. Both the ZINC data and the SAMPL4 data are in a format where the SMILES string is the first entry on each line and the compound ID or name is the second entry.\n", - "\n", - "**Insert code below to read in the SAMPL4 compounds (stored in `sampl4_compounds`) and then make a new list called `all_compounds` which contains the ZINC compounds plus the SAMPL4 compounds**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "8gdRVmF26GKM" - }, - "outputs": [], - "source": [ - "# Your code goes here" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "EtY9s-Nz6GKN" - }, - "source": [ - "### Let's also get a known integrase ligand for use as reference\n", - "\n", - "`3NF8_ligand.pdb` in this directory contains a known HIV integrase ligand (from the 3NF8 structure in the PDB) which can be used as a query for trying to find other, similar integrase ligands, as we will do here. Using Python code or a 2D viewer, create a SMILES string for this ligand (recommended option: Use the OpenEye toolkits, read it in as an OEMol, and create an isomeric SMILES string for it) and save it into a plain text file named something like `query.smi`; this will serve as the query molecule for our LINGO search. You can also save it to a variable like `querymolecule_smiles` in this notebook.\n", - "\n", - "**Your code for this goes here (if you use code) and make sure you end up with a `querymolecule_smiles` variable:**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "8dQ9Eosb6GKN" - }, - "outputs": [], - "source": [ - "querymolecule_smiles = 'CC[C@@H](C)NC(=O)c1ccccc1C[N@@H+](CC=C)Cc2ccc3c(c2C(=O)[O-])OC[C@@H](O3)CCC(=O)[O-]'" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "c8Xe0bwq6GKO" - }, - "source": [ - "### Update the code below to process your `all_compounds` list and filter it down to 10k compounds\n", - "\n", - "Next, you'll process your set of compounds from ZINC (plus the known ligands you inserted) and compare to the reference molecule in `querymolecule_smiles`using the code below.\n", - "\n", - "The code below currently takes in a `combined.smi` input file; you'll need to modify it to use your input (an `all_compounds` list)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "hSOd83BD6GKO" - }, - "outputs": [], - "source": [ - "from openeye.oechem import *\n", - "import pickle\n", - "\n", - "#INFILE: Input file containing SMILES strings and titles of the molecules you want to examine\n", - "#infile = 'combined.smi'\n", - "infile = 'zinc_compounds.smi'\n", - "\n", - "#Query molecule: This assumes you already have `querymolecule_smiles` containing the SMILES string for your query molecule\n", - "#Load reference molecule\n", - "qmol = OEGraphMol()\n", - "OEParseSmiles( qmol, querymolecule_smiles )\n", - "\n", - "#Initialize lingo comparison\n", - "lingo = OELingoSim( qmol )\n", - "\n", - "#########################################################################\n", - "#Load other molecules and do the LINGO search to compute similarity scores\n", - "#########################################################################\n", - "\n", - "file = open( infile, 'r') #Open input file for reading\n", - "text = file.readlines() #Read contents and close\n", - "file.close()\n", - "\n", - "#Initialize storage for results\n", - "sims_by_name = {}\n", - "smiles_by_name = {}\n", - "\n", - "#Loop over the text we read in and do similarity comparison\n", - "for (idx,line) in enumerate(text):\n", - "\n", - " #Every 1000 molecules, print an update on progress.\n", - " if idx%1000==0:\n", - " print(\"%s/%s\" % (idx, len(text) ))\n", - "\n", - " #Extract data - split the line up into components\n", - " tmp = line.split()\n", - "\n", - " #Initialize new, empty molecule\n", - " mol = OEGraphMol()\n", - " #Read SMILES into molecule from first entry on line read from file\n", - " OEParseSmiles( mol, tmp[0] )\n", - " \n", - " #DO similarity comparison via lingo\n", - " sim = lingo.Similarity( mol )\n", - "\n", - " #Load and score smiles string and name for future reference.\n", - " smi = tmp[0]\n", - " name = tmp[1]\n", - " sims_by_name[ name ] = sim\n", - " smiles_by_name[name] = smi\n", - "\n", - "#########################################################################\n", - "#Process results, print out info, store results.\n", - "#########################################################################\n", - "\n", - "#Get lists of similarities and compound names.\n", - "similarities = list(sims_by_name.values())\n", - "names = list(sims_by_name.keys())\n", - "print(len(names))\n", - "\n", - "#Do a sort of the names by similarity score, highest to lowest.\n", - "names = sorted(names, key=lambda name: -sims_by_name[name])\n", - "\n", - "#Print out maximum similarity score\n", - "print(\"Max similarity: %.3f\" % max(similarities))\n", - "\n", - "#Save top 10,000 most similar compounds (the full set is probably too large to save again) to a pickle file in case we want to re-load them to do anything else with them.\n", - "saveNum = 10000\n", - "smiles = [ smiles_by_name[name] for name in names[0:saveNum] ]\n", - "file = open('match_names_and_smiles.pickle', 'wb')\n", - "pickle.dump((names, smiles), file)\n", - "file.close()\n", - "\n", - "#Save 1,000 most similar componds (again, full set is too large) to a plain text file, AND print out info on them..\n", - "file = open('match_names_and_smiles.txt', 'w')\n", - "print(\"Molecules, most similar to least similar:\")\n", - "for n in range(saveNum):\n", - " #Store top (saveNum) most similar molecules.\n", - " name = names[n]\n", - " print('%s \\t %s' % (name, sims_by_name[name]))\n", - " file.write('%s\\t %s \\t %s\\n' % ( smiles_by_name[name], names[n], sims_by_name[name]))\n", - "file.close()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "iUHtbuq86GKO" - }, - "source": [ - "### Check how well your Lingo search did at finding the known active compounds\n", - "\n", - "The script above writes the top 10,000 matches to a pickle file and a text file. Check how many of the known active compounds (from `sampl4_compounds`) are in the top 10,000. Specifically, write a python script which:\n", - "- Takes the top 10,000 Lingo matches\n", - "- Checks their names against the actives in `sampl4_compounds`\n", - "- Calcluate the enrichment factor for this 10,000 compounds (the number of actives you actually found, divided by the number you would expect to find if you were guessing randomly and the Lingo search were doing no good (so that active compounds were divided randomly throughout the full set).\n", - "\n", - "You may wish to also use `oenotebook` to depict some of your top compounds and see how they compare to the actual actives. \n", - "\n", - "Ask for help if these tasks seem challenging. \n", - "\n", - "**Your python code for this should go here**:" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4Rbm0HCc6GKP" - }, - "source": [ - "## Warm-up Part 2: Docking\n", - "\n", - "In this section, your starting point is rather similar to that in the section above — but this time, since docking is a lot slower than a LINGO search, let’s just see how simple docking can do at picking out SAMPL4 active compounds from a larger set of nonbinders. This directory contains a file called `Maybridge_nonbinders.smi`, which is non-binders from a set of fragments Tom Peat and his collaborators experimentally screened initially in SAMPL4 (which led to the lead series found in `SAMPL4_smiles.smi`). This directory also contains `3NF8_prepped.pdb`, the receptor, and `3NF8_ligand.pdb`, a reference ligand from that structure.\n", - "\n", - "\n", - "Also get a copy of `dock_example.py` from there, a docking tool based on an example from the OpenEye documentation, and `get_scores_from_sdf.py`, a rough Python script for analyzing the output and making an enrichment plot.\n", - "\n", - "### Read in the relevant data\n", - "\n", - "Read in the `Maybridge_nonbinders.smi` and `SAMPL4_smiles.smi` into a single list of lines similary to what was done above, so you have a list of compounds to dock here (we'll be docking both active and inactive compounds and seeing how well we can do at recognizing the actives). Store these into separate lists, then make a combined list of both that's called `all_docking_compounds`.\n", - "\n", - "**Insert your code for this here**:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "4_GVTH8N6GKP" - }, - "outputs": [], - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "e1pFNBT_6GKP" - }, - "source": [ - "### Now dock using the compounds using the code below.\n", - "\n", - "The code below will use the `Chemgauss4` scoring function from the OpenEye toolkits to dock your compounds to the target. You should try it out; you may also want to revisit it later and consider testing alternate scoring functions [described in the OpenEye documentation](https://docs.eyesopen.com/toolkits/python/dockingtk/docking.html) such as the Hybrid2, PLP, or Chemscore scoring functions. `Hybrid2` is particularly interesting as it is a combination of docking and shape comparison and could perform particularly well here since we have both the receptor AND a known ligand we can use. Comparing performance of these methods would be a useful outcome of this exercise.\n", - "\n", - "This draws on code you saw in the docking sandbox.\n", - "\n", - "#### Prep your receptor for docking" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "sGTbwLKu6GKP" - }, - "outputs": [], - "source": [ - "from openeye import oedocking\n", - "from openeye import oeomega\n", - "from openeye import oechem\n", - "\n", - "# Load the HIV integrase receptor from disk\n", - "imstr = oemolistream('3NF8_prepped.pdb')\n", - "protein = oechem.OEGraphMol()\n", - "oechem.OEReadMolecule(imstr, protein)\n", - "imstr.close()\n", - "\n", - "# Load a reference ligand; we'll use this to indicate where the binding site is (and for `Hybrid`, it serves as a reference ligand)\n", - "ligand = oechem.OEGraphMol()\n", - "imstr = oechem.oemolistream('3NF8_ligand.pdb')\n", - "oechem.OEReadMolecule(imstr, ligand)\n", - "imstr.close()\n", - "\n", - "# Initialize the receptor for docking\n", - "receptor = oechem.OEGraphMol()\n", - "oedocking.OEMakeReceptor(receptor, protein, ligand)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wHqtjfx_6GKP" - }, - "source": [ - "#### Choose your docking scoring function\n", - "\n", - "If you wish to test other scoring functions, this would be where you would change it" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "8DNWnT8Y6GKQ" - }, - "outputs": [], - "source": [ - "#Set the docking method and docking resolution\n", - "# Note: Chemgauss4 is the scoring function for FRED\n", - "dock_method = oedocking.OEDockMethod_Chemgauss4\n", - "dock_resolution = oedocking.OESearchResolution_Default\n", - "sdtag = oedocking.OEDockMethodGetName( dock_method )\n", - "\n", - "#Generate our OEDocking object\n", - "dock = oedocking.OEDock( dock_method, dock_resolution)\n", - "\n", - "#Initialize the OEDocking by providing it the receptor\n", - "if not dock.Initialize(receptor):\n", - " # raise an exception if the receptor cannot be initialized\n", - " raise Exception(\"Unable to initialize Docking with {0}\".format(self.args.receptor))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ScIfNrxO6GKQ" - }, - "source": [ - "#### Define a function for docking" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "Lwbr_M796GKQ" - }, - "outputs": [], - "source": [ - "def dock_molecule( dock: \"OEDock\", sdtag: str, num_poses: int, mcmol ) -> tuple:\n", - " ''' Docks the multiconfomer molecule, with the given number of poses\n", - " Returns a tuple of the docked molecule (dockedMol) and its score\n", - " i.e. ( dockedMol, score )\n", - " '''\n", - " dockedMol = oechem.OEMol()\n", - "\n", - " #Dock the molecule into a given number of poses\n", - " res = dock.DockMultiConformerMolecule(dockedMol, mcmol, num_poses)\n", - " \n", - " if res == oedocking.OEDockingReturnCode_Success:\n", - " \n", - " #Annotate the molecule with the score and SDTag that contains the docking method\n", - " oedocking.OESetSDScore(dockedMol, dock, sdtag)\n", - " dock.AnnotatePose(dockedMol)\n", - " score = dock.ScoreLigand(dockedMol)\n", - " oechem.OESetSDData(dockedMol, sdtag, \"{}\".format(score))\n", - " return dockedMol, score\n", - " \n", - " else:\n", - " # raise an exception if the docking is not successful\n", - " raise Exception(\"Unable to dock ligand {0} to receptor\".format( dockedMol ))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "DSMS8RwO6GKQ" - }, - "source": [ - "#### Run docking\n", - "\n", - "Note that this assumes your ligand SMILES and names are stored in `all_docking_compounds`.\n", - "\n", - "You shouldn't have to change settings here, but you can if you like; e.g. for testing purposes you might wish to dock just a subset of the compounds." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "mDOeg4YD6GKQ" - }, - "outputs": [], - "source": [ - "omega = oeomega.OEOmega()\n", - "omega.SetStrictStereo(False) \n", - "\n", - "# Generate conformers for compounds and dock\n", - "inmols = []\n", - "usednames = []\n", - "for idx,line in enumerate(all_docking_compounds):\n", - " tmp = line.split()\n", - " smi = tmp[0]\n", - " mol = oechem.OEMol()\n", - " name = tmp[1]\n", - " if name=='' or name==None or len(name)<3:\n", - " #Define alternate name based on index\n", - " name = 'mol%s smiles %s' % (idx, smi)\n", - " print(\"No name found on line %s; using alternate name %s...\" % (idx, name))\n", - " if not name in usednames: #Make sure haven't already used this one\n", - " usednames.append(name)\n", - " oechem.OEParseSmiles(mol, smi)\n", - " mol.SetTitle(name)\n", - " builtOK = omega(mol)\n", - " inmols.append(mol)\n", - " else:\n", - " continue\n", - "\n", - "#Define how many docked poses to generate per molecule\n", - "num_poses = 2\n", - "\n", - "\n", - "#Open a filestream for writing the docked molecules\n", - "scores = {}\n", - "with oechem.oemolostream( 'dock-results.sdf') as ofs:\n", - "\n", - " #Loop over 3D molecules from the input filestream\n", - " for mcmol in inmols:\n", - "\n", - " #Call our written docking function\n", - " dockedMol, score = dock_molecule( dock, sdtag, num_poses, mcmol )\n", - " print(\"{} {} score = {:.4f}\".format(sdtag, dockedMol.GetTitle(), score))\n", - "\n", - " #Write docked molecules to output filestream\n", - " oechem.OEWriteMolecule(ofs, dockedMol)\n", - " \n", - " # Store score\n", - " scores[ mcmol.GetTitle()] = score\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gv-tl3kp6GKQ" - }, - "source": [ - "Here, `dock-results.sdf` is a key output file. This is a structure file (sdf) and will contain 3D poses of molecules docked into the receptor, along with scores. You can visualize these by loading them into PyMol or a similar viewer (e.g. Vida, VMD, Chimera, ...) along with the receptor (`3NF8_prepped.pdb`).\n", - "\n", - "### Now let's do an enrichment plot\n", - "\n", - "If we needed to use the scores stored to file, we would start by retrieving them from the SDF file they're stored in. But we already have them in `scores`, which is a dictionary storing scores by molecule title. So here, our first step is to load active compounds and check which ones are present among our docked compounds, at which ranks.\n", - "\n", - "You should be able to just run this code:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "vsiohxdr6GKR" - }, - "outputs": [], - "source": [ - "# Read active compounds\n", - "active_smiles_by_name = {}\n", - "file = open('SAMPL4_smiles.smi', 'r')\n", - "text = file.readlines()\n", - "file.close()\n", - "for line in text:\n", - " tmp = line.split()\n", - " active_smiles_by_name[tmp[1]] = tmp[0]\n", - "\n", - "# Build list of titles sorted by score\n", - "sorted_titles = list(scores.keys())\n", - "sorted_titles.sort( key = lambda title: scores[title] )\n", - "\n", - "# Count how many actives are found at which ranks\n", - "ct = 0\n", - "fnd_actives = []\n", - "for active_name in active_smiles_by_name.keys():\n", - " if active_name in sorted_titles:\n", - " ct += 1\n", - " print(\"Active %s found in docking results at rank %s\" % ( active_name, sorted_titles.index(active_name)))\n", - " fnd_actives.append( active_name )\n", - "\n", - "print(\"Total compounds: %s\" % len(sorted_titles))\n", - "\n", - "#Find number of actives\n", - "n_actives = len(fnd_actives)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "7Fiyur8k6GKR" - }, - "source": [ - "The next step is to actually generate an enrichment plot. This does a really basic one, which it should display inline AND save to a PDF file:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "tNFpyTfd6GKR" - }, - "outputs": [], - "source": [ - "#Do an enrichment plot\n", - "%pylab inline\n", - "numfound = []\n", - "ct=0\n", - "for name in sorted_titles:\n", - " if name in active_smiles_by_name.keys():\n", - " ct+=1\n", - " numfound.append(ct)\n", - "fracfound = [ ct/float(n_actives) for ct in numfound]\n", - "plot( arange(len(sorted_titles)), fracfound, 'bo')\n", - "plot( [0, len(sorted_titles)], [0, 1]) #Random line\n", - "legend(('Docking results', 'Expected for random'))\n", - "xlabel('Rank')\n", - "ylabel('Fraction of actives found')\n", - "show()\n", - "savefig( 'docking-results-enrichment.pdf')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ENguMXEx6GKR" - }, - "source": [ - "## Sandbox: Try out some other things\n", - "\n", - "For the final programming part of this assignment, you have the opportunity to try out some other things *of your choice* which relate to the topic. For example, you might consider linking the two tests above -- if you dock the results of a LINGO search, how well can docking do at further enriching the active compounds? \n", - "\n", - "You also might be interested in getting the DUD-E set's HIV integrase ligands (actives) and decoys (nonbinders) -- this provides a second set of actual integrase binders which are fairly distinct from the series represented by the 3NF8 ligand (see http://dude.docking.org/targets). You could experiment to see whether docking and/or lingo searches can recognize the DUD-E integrase ligands out of the DUD-E integrase decoys, or out of ALL non-binders from DUD-E, or even whether it can recognize DUD-E integrase ligands hidden in a subset of ZINC. Experimenting here would give you a sense of how the performance of these methods depends on the dataset you are using.\n", - "\n", - "You could likewise take any of the other ligand+decoy sets from DUD-E and evaluate docking and/or LINGO performance on those. \n", - "\n", - "You are free to try a variety of things, but **your final submission should include your results on at least three different datasets, and you should look at several different scoring functions. You should provide enrichment plots for every different system and scoring function examined.** Unlike some of our other assignments, relatively minimal coding is required here - mostly you’re just running things I’ve provided for you - so you should take some more time to examine the results. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "gqNCYrty6GKR" - }, - "source": [ - "## Consider some questions\n", - "\n", - "- Why does LINGO do so well here, at least in your initial test above? (Hint: Note who produced the 3NF8 structure by looking it up on the PDB. Is this part of the same series as the SAMPL4 ligands? Do you find that typical ZINC compounds bear a strong resemblance to these, or not?)\n", - "- You might be interested in looking at physical properties (size, number of rotatable bonds, polarity, etc.) of the LINGO matches to see how they compare to the SAMPL4 ligands; if so, feel free to ask if you need any help figuring out how to calculate those properties using the OpenEye tools.\n", - "- Which of the docking scoring functions you tried performs best? Do any of them perform better than random in the second example above?\n", - "- If you try docking on the results of a LINGO search — does it do any further enrichment? How does this depend on the database you dock? If you are docking compounds from ZINC, can you be sure the enrichment (or lack thereof) is real? (Hint: Do you know the compounds from ZINC are inactive?)\n", - "- If you compare results of a single method (i.e. docking) on several different databases or targets, comment on how the contents of the database, or the target you are working on, impacts performance. If possible, speculate as to why.\n", - "\n", - "## Write a brief report below (or separately) for submission\n", - "\n", - "Submit a brief report explaining what you did along with key plots you generated, either in this notebook or as a separate file uploaded to Canvas. Explain what you think your results mean, and answer any of the above questions you are able to. The key idea here is really to do three things:\n", - "- Explain what you did and show me the results\n", - "- Tell me what you learned and what you thought was interesting. This should include commenting on how much enrichment depends on the data set you examine and what scoring function you use. \n", - "- Point out anything which is particularly helpful in understanding strengths and weaknesses of docking and LINGO and how they might work together.\n", - "- Also attach all enrichment plots you are reporting on, including the one you were assigned in Warm-Up Part 2" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "fP50dG2L6GKR" - }, - "source": [ - "" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda env:workshop_test]", - "language": "python", - "name": "conda-env-workshop_test-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.3" - }, - "toc": { - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "toc_cell": false, - "toc_position": {}, - "toc_section_display": "block", - "toc_window_display": false - }, - "colab": { - "name": "library_searching_asignment.ipynb", - "provenance": [] - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "CUqMDtgw1VH0" + }, + "source": [ + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MobleyLab/drug-computing/blob/master/uci-pharmsci/assignments/library_searching/library_searching_asignment.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "U62m97Om6GKH" + }, + "source": [ + "# Docking and library searching assignment, PharmSci 175/275\n", + "\n", + "## Objective\n", + "In this assignment you will perform one or more sample library searches to examine docking performance on a test set or test sets. This assignment will differ somewhat from the others in that it gives you more of a chance to explore.\n", + "\n", + "## Overview\n", + "As discussed in class, docking methods are one of the fairly widely used computational methods for studying binding, and have seen significant applications in the area of virtual screening of libraries of potential ligands to filter out compounds which are unlikely to be active and hopefully increase the success rate of finding active compounds via experiments. These methods, while fairly fast, are still typically require a few seconds per compound or more, and thus often are unsuited for filtering large libraries of millions of compounds. In such cases, even faster methods such as LINGO searches may be used for pre-filtering.\n", + "\n", + "Here, I am providing you with sample scripts to perform a test LINGO search to filter a library of several million compounds down to a smaller number which have similarity to a known ligand, then to dock these to a target receptor and look at enrichment of active compounds. The test case I have provided is binding of various small molecules to the LEDGF site of HIV-1 integrase, as seen in the 3D structure/shape assignment and as described in the SAMPL4 paper referenced below.\n", + "\n", + "In this exercise, you will work through the example I provide, then perform tests of your own on at least three different data sets to see how well docking and LINGO searches will perform for you. You should also examine different scoring functions (within OpenEye’s docking toolkit). Think of this assignment as a bit of a sandbox - you are given some tools and a chance to play around. It is likely that the ideas you generate will help me make this assignment more successful the next time around, and any feedback is appreciated.\n", + "\n", + "# Your assignment\n", + "\n", + "Here, you will start off by doing a couple example activities so you can see how to use the OpenEye tools to do LINGO searches and docking. Once you’ve done these, then you will have the opportunity to branch out, try other things, and see what will work best on the system(s) you are looking at.\n", + "\n", + "As background for this, I suggest you read, if you have not already done so, the paper on the [SAMPL4 HIV integrase challenge](http://dx.doi.org/10.1007/s10822-014-9723-5), as well as the [DUD paper](http://dx.doi.org/10.1021/jm0608356); the later [DUD-E paper](http://dx.doi.org/10.1021/jm300687e) may also be helpful.\n", + "\n", + "Here, your warm-up work will relate to the HIV-1 integrase system studied in the SAMPL4 challenge (and seen in the 3D structure/shape assignment).\n", + "\n", + "## Warm-up Part 1: Lingo search and other prep\n", + "\n", + "Here, your task is to do a simple test of the Lingo search method by downloading a subset of the ZINC database, hiding some known integrase inhibitors in it, and testing the ability of the LINGO search method to enrich the database for known HIV-1 integrase inhibitors.\n", + "\n", + "### First get some compounds to work with\n", + "\n", + "As a starting point, let's get some compounds to work with. Visit the ZINC15 database (zinc15.docking.org), and click through on \"Tranches\" (subsets) towards the bottom left. This allows you to select all available compounds or subsets by purchaseability, molecular weight, calculated logP, etc. (You can toggle on and off rows and columns by clicking the top or left columns or rows), and filter by reaction likelihood (top menu) or wait time (top menu) and other properties. Select some subset of compounds (600 million is probably too many; let's keep it to a couple million or less) using these options. \n", + "\n", + "Once you've selected a subset (I filtered to get \"clean\" \"in stock\" compounds between 200-400 Daltons in some of the middle logP ranges) click the download buttom at the top right to pop up a \"Download Tranches\" menu. **Don't actually download**. Just copy the listed tranche codes; we can use those here to make your life easier.\n", + "\n", + "Now paste your tranche codes into the box below as a string called `tranches`, like this:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zKthp7bv6ICn" + }, + "source": [ + "## Installing Packages\n", + "\n", + "***If you are running this on Google Colab, please add the installation blocks from the [getting started notebook](https://github.com/MobleyLab/drug-computing/blob/master/uci-pharmsci/Getting_Started.ipynb) or [condacolab](https://github.com/aakankschit/drug-computing/blob/master/uci-pharmsci/Getting_Started_condacolab.ipynb) here and then execute the code below***" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IxEB1QCy6GKK" + }, + "outputs": [], + "source": [ + "# Tranches. Replace these with your selected tranches\n", + "tranches = \"ABAA ABAB ABBA ABBB ABCA ABCB BBAA BBAB BBBA BBBB BBCA BBCB CBAA CBAB CBBA CBBB CBCA CBCB DBAA DBAB DBBA DBBB DBCA DBCB EBAA EBAB EBBA EBBB EBCA EBCB FBAA FBAB FBBA FBBB FBCA FBCB ACAA ACAB ACBA ACBB ACCA ACCB BCAA BCAB BCBA BCBB BCCA BCCB CCAA CCAB CCBA CCBB CCCA CCCB DCAA DCAB DCBA DCBB DCCA DCCB ECAA ECAB ECBA ECBB ECCA ECCB FCAA FCAB FCBA FCBB FCCA FCCB ADAA ADAB ADBA ADBB ADCA ADCB BDAA BDAB BDBA BDBB BDCA BDCB CDAA CDAB CDBA CDBB CDCA CDCB DDAA DDAB DDBA DDBB DDCA DDCB EDAA EDAB EDBA EDBB EDCA EDCB FDAA FDAB FDBA FDBB FDCA FDCB AEAA AEAB AEBA AEBB AECA AECB BEAA BEAB BEBA BEBB BECA BECB CEAA CEAB CEBA CEBB CECA CECB DEAA DEAB DEBA DEBB DECA DECB EEAA EEAB EEBA EEBB EECA EECB FEAA FEAB FEBA FEBB FECA FECB AFAA AFAB AFBA AFBB AFCA AFCB BFAA BFAB BFBA BFBB BFCA BFCB CFAA CFAB CFBA CFBB CFCA CFCB DFAA DFAB DFBA DFBB DFCA DFCB EFAA EFAB EFBA EFBB EFCA EFCB FFAA FFAB FFBA FFBB FFCA FFCB AGAA AGAB AGBA AGBB AGCA AGCB BGAA BGAB BGBA BGBB BGCA BGCB CGAA CGAB CGBA CGBB CGCA CGCB DGAA DGAB DGBA DGBB DGCA DGCB EGAA EGAB EGBA EGBB EGCA EGCB FGAA FGAB FGBA FGBB FGCA FGCB\"" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "viv9OiTo6GKL" + }, + "source": [ + "Now we'll use the code below (using Python's `urllib` module) to download the files from these tranches and link them together into one big text file for further processing (so we don't have to download again if we need them again), and also save them to the list `zinc_compounds`. \n", + "\n", + "The file may be large. (e.g. I downloaded about 4.5 million compounds and ended up with a roughly 260 MB file; this could be reduced using compression but it's a lot of data regardless).\n", + "\n", + "If you wanted to do an even larger number of compounds, you would probably want to process one tranche at a time rather than grouping them all up into one large set like I'm doing here. \n", + "\n", + "**You should just need to run this code**:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8yvlBw-t6GKL" + }, + "outputs": [], + "source": [ + "# Split up the string of tranches into a list\n", + "tranches_elements = tranches.split()\n", + "\n", + "# Set up an output file and list for storing results\n", + "filename = 'zinc_compounds.smi'\n", + "out_file = open(filename, 'w')\n", + "zinc_compounds = []\n", + "\n", + "# Loop over SMILES string files for desired tranches \n", + "import urllib\n", + "for t_id in tranches_elements:\n", + " prefix = t_id[0:2]\n", + " # Retrieve file-like object for iterating over contents of this one\n", + " contents = urllib.request.urlopen('http://files.docking.org/2D/' + prefix + '/'+ t_id +'.smi')\n", + " # Loop over each line, decode from bytes format to string, write to output. Skip the first line of each file\n", + " # which is a header line\n", + " for line in contents:\n", + " if line.split()[0].decode() != 'smiles':\n", + " # Replace some special characters at the same time\n", + " out_file.write(line.decode().replace('\\n',''))\n", + " zinc_compounds.append(line.decode().replace('\\r\\n',''))\n", + " \n", + "out_file.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6Wcl-Il56GKM" + }, + "source": [ + "### Now retrieve the SAMPL4 compounds from a file" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "TTG4zs5z6GKM" + }, + "source": [ + "Next, you need to load the SAMPL4 compounds and insert them in your set. These are present in this directory as `SAMPL4_smiles.smi`. Both the ZINC data and the SAMPL4 data are in a format where the SMILES string is the first entry on each line and the compound ID or name is the second entry.\n", + "\n", + "**Insert code below to read in the SAMPL4 compounds (stored in `sampl4_compounds`) and then make a new list called `all_compounds` which contains the ZINC compounds plus the SAMPL4 compounds**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8gdRVmF26GKM" + }, + "outputs": [], + "source": [ + "# Your code goes here" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "EtY9s-Nz6GKN" + }, + "source": [ + "### Let's also get a known integrase ligand for use as reference\n", + "\n", + "`3NF8_ligand.pdb` in this directory contains a known HIV integrase ligand (from the 3NF8 structure in the PDB) which can be used as a query for trying to find other, similar integrase ligands, as we will do here. Using Python code or a 2D viewer, create a SMILES string for this ligand (recommended option: Use the OpenEye toolkits, read it in as an OEMol, and create an isomeric SMILES string for it) and save it into a plain text file named something like `query.smi`; this will serve as the query molecule for our LINGO search. You can also save it to a variable like `querymolecule_smiles` in this notebook.\n", + "\n", + "**Your code for this goes here (if you use code) and make sure you end up with a `querymolecule_smiles` variable:**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8dQ9Eosb6GKN" + }, + "outputs": [], + "source": [ + "querymolecule_smiles = 'CC[C@@H](C)NC(=O)c1ccccc1C[N@@H+](CC=C)Cc2ccc3c(c2C(=O)[O-])OC[C@@H](O3)CCC(=O)[O-]'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c8Xe0bwq6GKO" + }, + "source": [ + "### Update the code below to process your `all_compounds` list and filter it down to 10k compounds\n", + "\n", + "Next, you'll process your set of compounds from ZINC (plus the known ligands you inserted) and compare to the reference molecule in `querymolecule_smiles`using the code below.\n", + "\n", + "The code below currently takes in a `combined.smi` input file; you'll need to modify it to use your input (an `all_compounds` list)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hSOd83BD6GKO" + }, + "outputs": [], + "source": [ + "from openeye.oechem import *\n", + "import pickle\n", + "\n", + "#INFILE: Input file containing SMILES strings and titles of the molecules you want to examine\n", + "#infile = 'combined.smi'\n", + "infile = 'zinc_compounds.smi'\n", + "\n", + "#Query molecule: This assumes you already have `querymolecule_smiles` containing the SMILES string for your query molecule\n", + "#Load reference molecule\n", + "qmol = OEGraphMol()\n", + "OEParseSmiles( qmol, querymolecule_smiles )\n", + "\n", + "#Initialize lingo comparison\n", + "lingo = OELingoSim( qmol )\n", + "\n", + "#########################################################################\n", + "#Load other molecules and do the LINGO search to compute similarity scores\n", + "#########################################################################\n", + "\n", + "file = open( infile, 'r') #Open input file for reading\n", + "text = file.readlines() #Read contents and close\n", + "file.close()\n", + "\n", + "#Initialize storage for results\n", + "sims_by_name = {}\n", + "smiles_by_name = {}\n", + "\n", + "#Loop over the text we read in and do similarity comparison\n", + "for (idx,line) in enumerate(text):\n", + "\n", + " #Every 1000 molecules, print an update on progress.\n", + " if idx%1000==0:\n", + " print(\"%s/%s\" % (idx, len(text) ))\n", + "\n", + " #Extract data - split the line up into components\n", + " tmp = line.split()\n", + "\n", + " #Initialize new, empty molecule\n", + " mol = OEGraphMol()\n", + " #Read SMILES into molecule from first entry on line read from file\n", + " OEParseSmiles( mol, tmp[0] )\n", + " \n", + " #DO similarity comparison via lingo\n", + " sim = lingo.Similarity( mol )\n", + "\n", + " #Load and score smiles string and name for future reference.\n", + " smi = tmp[0]\n", + " name = tmp[1]\n", + " sims_by_name[ name ] = sim\n", + " smiles_by_name[name] = smi\n", + "\n", + "#########################################################################\n", + "#Process results, print out info, store results.\n", + "#########################################################################\n", + "\n", + "#Get lists of similarities and compound names.\n", + "similarities = list(sims_by_name.values())\n", + "names = list(sims_by_name.keys())\n", + "print(len(names))\n", + "\n", + "#Do a sort of the names by similarity score, highest to lowest.\n", + "names = sorted(names, key=lambda name: -sims_by_name[name])\n", + "\n", + "#Print out maximum similarity score\n", + "print(\"Max similarity: %.3f\" % max(similarities))\n", + "\n", + "#Save top 10,000 most similar compounds (the full set is probably too large to save again) to a pickle file in case we want to re-load them to do anything else with them.\n", + "saveNum = 10000\n", + "smiles = [ smiles_by_name[name] for name in names[0:saveNum] ]\n", + "file = open('match_names_and_smiles.pickle', 'wb')\n", + "pickle.dump((names, smiles), file)\n", + "file.close()\n", + "\n", + "#Save 1,000 most similar componds (again, full set is too large) to a plain text file, AND print out info on them..\n", + "file = open('match_names_and_smiles.txt', 'w')\n", + "print(\"Molecules, most similar to least similar:\")\n", + "for n in range(saveNum):\n", + " #Store top (saveNum) most similar molecules.\n", + " name = names[n]\n", + " print('%s \\t %s' % (name, sims_by_name[name]))\n", + " file.write('%s\\t %s \\t %s\\n' % ( smiles_by_name[name], names[n], sims_by_name[name]))\n", + "file.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iUHtbuq86GKO" + }, + "source": [ + "### Check how well your Lingo search did at finding the known active compounds\n", + "\n", + "The script above writes the top 10,000 matches to a pickle file and a text file. Check how many of the known active compounds (from `sampl4_compounds`) are in the top 10,000. Specifically, write a python script which:\n", + "- Takes the top 10,000 Lingo matches\n", + "- Checks their names against the actives in `sampl4_compounds`\n", + "- Calcluate the enrichment factor for this 10,000 compounds (the number of actives you actually found, divided by the number you would expect to find if you were guessing randomly and the Lingo search were doing no good (so that active compounds were divided randomly throughout the full set).\n", + "\n", + "You may wish to also use `oenotebook` to depict some of your top compounds and see how they compare to the actual actives. \n", + "\n", + "Ask for help if these tasks seem challenging. \n", + "\n", + "**Your python code for this should go here**:" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4Rbm0HCc6GKP" + }, + "source": [ + "## Warm-up Part 2: Docking\n", + "\n", + "In this section, your starting point is rather similar to that in the section above — but this time, since docking is a lot slower than a LINGO search, let’s just see how simple docking can do at picking out SAMPL4 active compounds from a larger set of nonbinders. This directory contains a file called `Maybridge_nonbinders.smi`, which is non-binders from a set of fragments Tom Peat and his collaborators experimentally screened initially in SAMPL4 (which led to the lead series found in `SAMPL4_smiles.smi`). This directory also contains `3NF8_prepped.pdb`, the receptor, and `3NF8_ligand.pdb`, a reference ligand from that structure.\n", + "\n", + "### Read in the relevant data\n", + "\n", + "Read in the `Maybridge_nonbinders.smi` and `SAMPL4_smiles.smi` into a single list of lines similary to what was done above, so you have a list of compounds to dock here (we'll be docking both active and inactive compounds and seeing how well we can do at recognizing the actives). Store these into separate lists, then make a combined list of both that's called `all_docking_compounds`.\n", + "\n", + "**Insert your code for this here**:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "4_GVTH8N6GKP" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "e1pFNBT_6GKP" + }, + "source": [ + "### Now dock the compounds using the code below.\n", + "\n", + "The code below will use the `Chemgauss4` scoring function from the OpenEye toolkits to dock your compounds to the target. You should try it out; you may also want to revisit it later and consider testing alternate scoring functions [described in the OpenEye documentation](https://docs.eyesopen.com/toolkits/python/dockingtk/docking.html) such as the Hybrid2, PLP, or Chemscore scoring functions. `Hybrid2` is particularly interesting as it is a combination of docking and shape comparison and could perform particularly well here since we have both the receptor AND a known ligand we can use. Comparing performance of these methods would be a useful outcome of this exercise.\n", + "\n", + "This draws on code you saw in the docking sandbox.\n", + "\n", + "#### Prep your receptor for docking" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "sGTbwLKu6GKP" + }, + "outputs": [], + "source": [ + "from openeye import oedocking\n", + "from openeye import oeomega\n", + "from openeye import oechem\n", + "\n", + "# Load the HIV integrase receptor from disk\n", + "imstr = oemolistream('3NF8_prepped.pdb')\n", + "protein = oechem.OEGraphMol()\n", + "oechem.OEReadMolecule(imstr, protein)\n", + "imstr.close()\n", + "\n", + "# Load a reference ligand; we'll use this to indicate where the binding site is (and for `Hybrid`, it serves as a reference ligand)\n", + "ligand = oechem.OEGraphMol()\n", + "imstr = oechem.oemolistream('3NF8_ligand.pdb')\n", + "oechem.OEReadMolecule(imstr, ligand)\n", + "imstr.close()\n", + "\n", + "# Initialize the receptor for docking\n", + "receptor = oechem.OEGraphMol()\n", + "oedocking.OEMakeReceptor(receptor, protein, ligand)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wHqtjfx_6GKP" + }, + "source": [ + "#### Choose your docking scoring function\n", + "\n", + "If you wish to test other scoring functions, this would be where you would change it" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "8DNWnT8Y6GKQ" + }, + "outputs": [], + "source": [ + "#Set the docking method and docking resolution\n", + "# Note: Chemgauss4 is the scoring function for FRED\n", + "dock_method = oedocking.OEDockMethod_Chemgauss4\n", + "dock_resolution = oedocking.OESearchResolution_Default\n", + "sdtag = oedocking.OEDockMethodGetName( dock_method )\n", + "\n", + "#Generate our OEDocking object\n", + "dock = oedocking.OEDock( dock_method, dock_resolution)\n", + "\n", + "#Initialize the OEDocking by providing it the receptor\n", + "if not dock.Initialize(receptor):\n", + " # raise an exception if the receptor cannot be initialized\n", + " raise Exception(\"Unable to initialize Docking with {0}\".format(self.args.receptor))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ScIfNrxO6GKQ" + }, + "source": [ + "#### Define a function for docking" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Lwbr_M796GKQ" + }, + "outputs": [], + "source": [ + "def dock_molecule( dock: \"OEDock\", sdtag: str, num_poses: int, mcmol ) -> tuple:\n", + " ''' Docks the multiconfomer molecule, with the given number of poses\n", + " Returns a tuple of the docked molecule (dockedMol) and its score\n", + " i.e. ( dockedMol, score )\n", + " '''\n", + " dockedMol = oechem.OEMol()\n", + "\n", + " #Dock the molecule into a given number of poses\n", + " res = dock.DockMultiConformerMolecule(dockedMol, mcmol, num_poses)\n", + " \n", + " if res == oedocking.OEDockingReturnCode_Success:\n", + " \n", + " #Annotate the molecule with the score and SDTag that contains the docking method\n", + " oedocking.OESetSDScore(dockedMol, dock, sdtag)\n", + " dock.AnnotatePose(dockedMol)\n", + " score = dock.ScoreLigand(dockedMol)\n", + " oechem.OESetSDData(dockedMol, sdtag, \"{}\".format(score))\n", + " return dockedMol, score\n", + " \n", + " else:\n", + " # raise an exception if the docking is not successful\n", + " raise Exception(\"Unable to dock ligand {0} to receptor\".format( dockedMol ))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "DSMS8RwO6GKQ" + }, + "source": [ + "#### Run docking\n", + "\n", + "Note that this assumes your ligand SMILES and names are stored in `all_docking_compounds`.\n", + "\n", + "You shouldn't have to change settings here, but you can if you like; e.g. for testing purposes you might wish to dock just a subset of the compounds." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "mDOeg4YD6GKQ" + }, + "outputs": [], + "source": [ + "omega = oeomega.OEOmega()\n", + "omega.SetStrictStereo(False) \n", + "\n", + "# Generate conformers for compounds and dock\n", + "inmols = []\n", + "usednames = []\n", + "for idx,line in enumerate(all_docking_compounds):\n", + " tmp = line.split()\n", + " smi = tmp[0]\n", + " mol = oechem.OEMol()\n", + " name = tmp[1]\n", + " if name=='' or name==None or len(name)<3:\n", + " #Define alternate name based on index\n", + " name = 'mol%s smiles %s' % (idx, smi)\n", + " print(\"No name found on line %s; using alternate name %s...\" % (idx, name))\n", + " if not name in usednames: #Make sure haven't already used this one\n", + " usednames.append(name)\n", + " oechem.OEParseSmiles(mol, smi)\n", + " mol.SetTitle(name)\n", + " builtOK = omega(mol)\n", + " inmols.append(mol)\n", + " else:\n", + " continue\n", + "\n", + "#Define how many docked poses to generate per molecule\n", + "num_poses = 2\n", + "\n", + "\n", + "#Open a filestream for writing the docked molecules\n", + "scores = {}\n", + "with oechem.oemolostream( 'dock-results.sdf') as ofs:\n", + "\n", + " #Loop over 3D molecules from the input filestream\n", + " for mcmol in inmols:\n", + "\n", + " #Call our written docking function\n", + " dockedMol, score = dock_molecule( dock, sdtag, num_poses, mcmol )\n", + " print(\"{} {} score = {:.4f}\".format(sdtag, dockedMol.GetTitle(), score))\n", + "\n", + " #Write docked molecules to output filestream\n", + " oechem.OEWriteMolecule(ofs, dockedMol)\n", + " \n", + " # Store score\n", + " scores[ mcmol.GetTitle()] = score\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gv-tl3kp6GKQ" + }, + "source": [ + "Here, `dock-results.sdf` is a key output file. This is a structure file (sdf) and will contain 3D poses of molecules docked into the receptor, along with scores. You can visualize these by loading them into PyMol or a similar viewer (e.g. Vida, VMD, Chimera, ...) along with the receptor (`3NF8_prepped.pdb`).\n", + "\n", + "### Now let's do an enrichment plot\n", + "\n", + "If we needed to use the scores stored to file, we would start by retrieving them from the SDF file they're stored in. But we already have them in `scores`, which is a dictionary storing scores by molecule title. So here, our first step is to load active compounds and check which ones are present among our docked compounds, at which ranks.\n", + "\n", + "You should be able to just run this code:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vsiohxdr6GKR" + }, + "outputs": [], + "source": [ + "# Read active compounds\n", + "active_smiles_by_name = {}\n", + "file = open('SAMPL4_smiles.smi', 'r')\n", + "text = file.readlines()\n", + "file.close()\n", + "for line in text:\n", + " tmp = line.split()\n", + " active_smiles_by_name[tmp[1]] = tmp[0]\n", + "\n", + "# Build list of titles sorted by score\n", + "sorted_titles = list(scores.keys())\n", + "sorted_titles.sort( key = lambda title: scores[title] )\n", + "\n", + "# Count how many actives are found at which ranks\n", + "ct = 0\n", + "fnd_actives = []\n", + "for active_name in active_smiles_by_name.keys():\n", + " if active_name in sorted_titles:\n", + " ct += 1\n", + " print(\"Active %s found in docking results at rank %s\" % ( active_name, sorted_titles.index(active_name)))\n", + " fnd_actives.append( active_name )\n", + "\n", + "print(\"Total compounds: %s\" % len(sorted_titles))\n", + "\n", + "#Find number of actives\n", + "n_actives = len(fnd_actives)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7Fiyur8k6GKR" + }, + "source": [ + "The next step is to actually generate an enrichment plot. This does a really basic one, which it should display inline AND save to a PDF file:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "tNFpyTfd6GKR" + }, + "outputs": [], + "source": [ + "#Do an enrichment plot\n", + "%pylab inline\n", + "numfound = []\n", + "ct=0\n", + "for name in sorted_titles:\n", + " if name in active_smiles_by_name.keys():\n", + " ct+=1\n", + " numfound.append(ct)\n", + "fracfound = [ ct/float(n_actives) for ct in numfound]\n", + "plot( arange(len(sorted_titles)), fracfound, 'bo')\n", + "plot( [0, len(sorted_titles)], [0, 1]) #Random line\n", + "legend(('Docking results', 'Expected for random'))\n", + "xlabel('Rank')\n", + "ylabel('Fraction of actives found')\n", + "show()\n", + "savefig( 'docking-results-enrichment.pdf')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ENguMXEx6GKR" + }, + "source": [ + "## Sandbox: Try out some other things\n", + "\n", + "For the final programming part of this assignment, you have the opportunity to try out some other things *of your choice* which relate to the topic. For example, you might consider linking the two tests above -- if you dock the results of a LINGO search, how well can docking do at further enriching the active compounds? \n", + "\n", + "You also might be interested in getting the DUD-E set's HIV integrase ligands (actives) and decoys (nonbinders) -- this provides a second set of actual integrase binders which are fairly distinct from the series represented by the 3NF8 ligand (see http://dude.docking.org/targets). You could experiment to see whether docking and/or lingo searches can recognize the DUD-E integrase ligands out of the DUD-E integrase decoys, or out of ALL non-binders from DUD-E, or even whether it can recognize DUD-E integrase ligands hidden in a subset of ZINC. Experimenting here would give you a sense of how the performance of these methods depends on the dataset you are using.\n", + "\n", + "You could likewise take any of the other ligand+decoy sets from DUD-E and evaluate docking and/or LINGO performance on those. \n", + "\n", + "You are free to try a variety of things, but **your final submission should include your results on at least three different datasets, and you should look at several different scoring functions. You should provide enrichment plots for every different system and scoring function examined.** Unlike some of our other assignments, relatively minimal coding is required here - mostly you’re just running things I’ve provided for you - so you should take some more time to examine the results. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gqNCYrty6GKR" + }, + "source": [ + "## Consider some questions\n", + "\n", + "- Why does LINGO do so well here, at least in your initial test above? (Hint: Note who produced the 3NF8 structure by looking it up on the PDB. Is this part of the same series as the SAMPL4 ligands? Do you find that typical ZINC compounds bear a strong resemblance to these, or not?)\n", + "- You might be interested in looking at physical properties (size, number of rotatable bonds, polarity, etc.) of the LINGO matches to see how they compare to the SAMPL4 ligands; if so, feel free to ask if you need any help figuring out how to calculate those properties using the OpenEye tools.\n", + "- Which of the docking scoring functions you tried performs best? Do any of them perform better than random in the second example above?\n", + "- If you try docking on the results of a LINGO search — does it do any further enrichment? How does this depend on the database you dock? If you are docking compounds from ZINC, can you be sure the enrichment (or lack thereof) is real? (Hint: Do you know the compounds from ZINC are inactive?)\n", + "- If you compare results of a single method (i.e. docking) on several different databases or targets, comment on how the contents of the database, or the target you are working on, impacts performance. If possible, speculate as to why.\n", + "\n", + "## Write a brief report below (or separately) for submission\n", + "\n", + "Submit (along with your code) a brief report explaining what you did along with key plots you generated, either in this notebook or as a separate file uploaded to Canvas. Explain what you think your results mean, and answer any of the above questions you are able to. The key idea here is really to do three things:\n", + "- Explain what you did and show me the results\n", + "- Tell me what you learned and what you thought was interesting. This should include commenting on how much enrichment depends on the data set you examine and what scoring function you use. \n", + "- Point out anything which is particularly helpful in understanding strengths and weaknesses of docking and LINGO and how they might work together.\n", + "- Also attach all enrichment plots you are reporting on, including the one you were assigned in Warm-Up Part 2\n", + "\n", + "**Include your code**; your report and code can be in this notebook, or they can be packaged separately." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fP50dG2L6GKR" + }, + "source": [] + } + ], + "metadata": { + "colab": { + "name": "library_searching_asignment.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "drugcomp", + "language": "python", + "name": "drugcomp" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + }, + "toc": { + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "toc_cell": false, + "toc_position": {}, + "toc_section_display": "block", + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/uci-pharmsci/assignments/solubility/physprops_solubility.ipynb b/uci-pharmsci/assignments/solubility/physprops_solubility.ipynb index 0bb8e8c..725efad 100644 --- a/uci-pharmsci/assignments/solubility/physprops_solubility.ipynb +++ b/uci-pharmsci/assignments/solubility/physprops_solubility.ipynb @@ -1,944 +1,944 @@ { - "cells": [ - { - "cell_type": "markdown", - "source": [ - "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MobleyLab/drug-computing/blob/master/uci-pharmsci/assignments/solubility/physprops_solubility.ipynb)" - ], - "metadata": { - "id": "w-XVZi3P1xKh" - } - }, - { - "cell_type": "markdown", - "metadata": { - "id": "cFaGnxIO6dHH" - }, - "source": [ - "# Solubility calculation assignment, PharmSci 175/275\n", - "\n", - "Solubility estimation/prediction is a huge problem in drug discovery. Here, we will attempt to build a simple empirical model for solubility prediction as in a recent literature challenge. We will take a set of ~100 solubility values, and develop a simple model which reproduces those values reasonably well, then test this model on a new set of compounds (a test set). To put it another way, we have a test set and a training set, and want to use the known solubilities from the training set to predict solubilities for the test set. \n", - "\n", - "This builds on the solubility challenge of [Llinàs et al.](https://dx.doi.org/10.1021/ci800058v) and the conclusions/subsequent work of [Hopfinger et al.](https://dx.doi.org/10.1021/ci800436c).\n", - "\n", - "\n", - "## Overview\n", - "\n", - "Solubility calculation is an important problem for drug discovery, partly because it is so important that drugs be soluble. Solubility is an important factor in the design of orally bioavailable drugs, as we have discussed in class. However, no good physical models are available for work in this area yet, so most of the models for solubility estimation are empirical, based on measuring a set of simple molecular properties for molecules and combining these to estimate a solubility in some way, based on calibration to experimental data.\n", - "\n", - "Recently, Llinàs et al., [(J. Chem. Inf. Model 48:1289 (2008))](https://dx.doi.org/10.1021/ci800058v) posed a challenge: Can you predict a set of 32 solubilities on a test set, using a database (training set) of 100 reliable solubility measurements? Follow up work [(Hopfinger et al., J. Chem. Inf. Model 49:1 (2009))](https://dx.doi.org/10.1021/ci800436c) provided the solubility measurements of the test set and assessed performance of a wide variety of solubility estimation techniques in this challenge.\n", - "\n", - "Here, your job is to construct several simple linear models to predict solubilities using the training set of roughly 100 compounds, and then test their performance on the test set, comparing them with one another, with a null model, and with the performance of research groups which participated in the challenge. You should also implement and test a simple variant of the LINGO-based approach of Vidal et al. (J. Chem. Inf. Model 45(2):386-393 (2005)). \n", - "\n", - "A good deal of the technology you will need to use here is provided for you, including example models. Your job in this assignment is simply going to be to adjust the Python code I have provided to build several (five or more) new models for predicting solubilities, plus one based on the approach of Vidal, and compare their performance to select your favorite. \n", - "\n", - "## Some setup notes\n", - "\n", - "In this directory, you should also find a module you can import which will help with some statistics -- `tools.py`. You will also find two directories containing structures of molecules in the different sets -- `llinas_predict`, containing molecules whose solubilities we want to predict, and `llinas_set`, containing molecules in the training set. Additionally, in the `scripts` directory there is `solubilities.pickle` which contains solubility data (not human readable). \n", - "\n", - "I also provide some fairly extensive example code below which you can use as the basis for your assignment. To briefly summmarize the provided code (you can see more detail by reading the comments and code below) it loads the structures of the molecules and their names, computes a reasonably extensive set of descriptros or properties of the different molecules and loads in the actual solubility data. It then proceeds to build two extremely simple models for predicting solubilities based on a simple linear combination/fit of physical properties. You will be able to use this part of the program as a template for building your own solubility models.\n", - "\n", - "## For solubility prediction, we'll use a series of *descriptors*\n", - "\n", - "Descriptors are properties of our molecule which might (or might not) be related to the solubility. For example, we might think that solubility will in general tend to go down as molecular weight goes up, and go up as polarity increases (or go down as polarity decreases) and so on. \n", - "\n", - "Here, let's take a sample molecule and calculate a series of descriptors which we might want to use in constructing a simple solubility model. " - ] - }, - { - "cell_type": "markdown", - "source": [ - "## Installing Packages\n", - "\n", - "***If you are running this on Google Colab, please add the installation blocks from the [getting started notebook](https://github.com/MobleyLab/drug-computing/blob/master/uci-pharmsci/Getting_Started.ipynb) or [condacolab](https://github.com/aakankschit/drug-computing/blob/master/uci-pharmsci/Getting_Started_condacolab.ipynb) here and then execute the code below***" - ], - "metadata": { - "id": "7cjhKOdG6iSb" - } - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "euy7e9bs6dHL", - "outputId": "c22b5d8d-9966-4571-c205-46bc3e992f6d" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Molecular weight: 128.17\n", - "Number of atoms: 18\n", - "Number of heavy atoms: 10\n", - "Number of ring atoms: 10\n", - "Number of halogens: 0\n", - "Number of nitrogens: 0\n", - "Number of oxygens: 0\n", - "Number of rotatable bonds: 0\n", - "Calculated logP: 3.57\n", - "Number of aromatic rings: 2\n", - "Polar surface area: 0.00\n", - "Number of hbond donors: 0\n", - "Number of hbond acceptors: 0\n", - "Number of rings: 1\n", - "Calculated solvation free energy: -4.13\n" - ] - } - ], - "source": [ - "from openeye.oechem import *\n", - "from openeye.oemolprop import *\n", - "from openeye.oeiupac import *\n", - "from openeye.oezap import *\n", - "from openeye.oeomega import *\n", - "import numpy as np\n", - "import scipy.stats\n", - "\n", - "#Initialize an OpenEye molecule\n", - "mol = OEMol()\n", - "\n", - "#let's look at phenol\n", - "OEParseIUPACName( mol, 'naphthalene' )\n", - "\n", - "#Generate conformation\n", - "omega = OEOmega()\n", - "omega(mol)\n", - "\n", - "#Here one of the descriptors we'll use is the calculated solvation free energy, from OpenEye's ZAP electrostatics solver\n", - "#Get zap ready for electrostatics calculations\n", - "zap = OEZap()\n", - "zap.SetInnerDielectric( 1.0 )\n", - "zap.SetGridSpacing(0.5)\n", - "area = OEArea()\n", - "\n", - "#Reduce verbosity\n", - "OEThrow.SetLevel(OEErrorLevel_Warning)\n", - "\n", - "\n", - "#Let's print a bunch of properties\n", - "#Molecular weight\n", - "print( \"Molecular weight: %.2f\" % OECalculateMolecularWeight(mol) )\n", - "#Number of atoms\n", - "print( \"Number of atoms: %s\" % mol.NumAtoms() ) \n", - "#Number of heavy atoms\n", - "print( \"Number of heavy atoms: %s\" % OECount(mol, OEIsHeavy() ) )\n", - "#Number of ring atoms\n", - "print( \"Number of ring atoms: %s\" % OECount(mol, OEAtomIsInRing() ) )\n", - "#Number of halogens\n", - "print( \"Number of halogens: %s\" % OECount( mol, OEIsHalogen() ))\n", - "print (\"Number of nitrogens: %s\" % OECount( mol, OEIsNitrogen() ) )\n", - "print( \"Number of oxygens: %s\" % OECount( mol, OEIsOxygen() ) )\n", - "print( \"Number of rotatable bonds: %s\" % OECount( mol, OEIsRotor() ) )\n", - "\n", - "#Calculated logP - water to octanol partitioning coefficient (which is often something which may correlate somewhat with solubility)\n", - "print( \"Calculated logP: %.2f\" % OEGetXLogP( mol ) )\n", - "\n", - "print( \"Number of aromatic rings: %s\" % OEGetAromaticRingCount( mol ) )\n", - "\n", - " \n", - " \n", - "#Calculate lots of other properties using molprop toolkit as per example in OE MolProp manual\n", - "#Handle the setup of 'filter', which computes lots of properties with the goal of filtering compounds. Here we'll not do any filtering\n", - "#and will use it solely for property calculation\n", - "filt = OEFilter()\n", - "ostr = oeosstream()\n", - "pwnd = False\n", - "filt.SetTable( ostr, pwnd)\n", - "headers = ostr.str().decode().split('\\t')\n", - "ostr.clear()\n", - "filt(mol)\n", - "fields = ostr.str().decode().split('\\t')\n", - "tmpdct = dict( zip(headers, fields) ) #Format the data we need into a dictionary for easy extraction\n", - "\n", - "print(\"Polar surface area: %s\" % tmpdct[ '2d PSA' ] )\n", - "print(\"Number of hbond donors: %s\" % int(tmpdct['hydrogen-bond donors']) )\n", - "print(\"Number of hbond acceptors: %s\" % int(tmpdct['hydrogen-bond acceptors']) )\n", - "print (\"Number of rings: %s\" % int(tmpdct['number of ring systems']) )\n", - "#print(tmpdct.keys())\n", - "\n", - "#Quickly estimate hydration free energy, or a value correlated with that -- from ZAP manual\n", - "#Do ZAP setup for molecule\n", - "OEAssignBondiVdWRadii(mol)\n", - "OEMMFFAtomTypes(mol)\n", - "OEMMFF94PartialCharges(mol)\n", - "zap.SetMolecule( mol )\n", - "solv = zap.CalcSolvationEnergy()\n", - "aval = area.GetArea( mol )\n", - "#Empirically estimate solvation free energy (hydration)\n", - "solvation = 0.59*solv + 0.01*aval #Convert electrostatic part to kcal/mol; use empirically determined kcal/sq angstrom value times surface area term\n", - "print (\"Calculated solvation free energy: %.2f\" % solvation)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5TjHi2Q86dHO" - }, - "source": [ - "## Linear models for solubility: Understanding your task\n", - "\n", - "Here, your first job is to construct some linear models for solubility and attempt to use them to predict solubilities for a test set of molecules. \n", - "Many different models for solubilities would be possible. Here, however, we focus on linear models -- that is, models having the form:\n", - "$y = mx + b$\n", - "\n", - "where $y$ is the solubility, $m$ and $b$ are constants, and $x$ is some descriptor of the molecule. Or with two variables:\n", - "$y = mx + nz + b$\n", - "\n", - "Here we've added a second descrptor, $z$, and another constant, $n$. Still more generally, we could write:\n", - "\n", - "$y = b + \\sum_i m_i x_i$\n", - "\n", - "In this case, we now have a constant, $b$, and a set of other constants, $m_i$, and descriptors, $x_i$; the sum runs over all values of $i$.\n", - "\n", - "What does this all mean? Basically, we are going to assume that we can predict solubilities out of some linear combination of descriptors or molecular properties. For example, (as a null model) I might assume that solubility can be predicted simply based on molecular weight -- perhaps heavier compounds will in general be less (or more) soluble. I might write:\n", - "\n", - "$y = m\\times MW + b$\n", - "\n", - "This has the form $y=mx + b$ but I replaced $x$ with $MW$, the molecular weight. To fit this model, I would then need to find the coefficients $m$ and $b$ to give the best agreement with the actual solublity data.\n", - "\n", - "Here, I would first develop parameters $m$ and $b$ to fit my training set -- that is, I would fit $m$ and $b$ on the training set data, the (roughly 100) compounds provided in the first paper. Then, I would apply the same $m$ and $b$ to the test set data to see how well I can predict the 32 \"new\" compounds. \n", - "\n", - "In this project, you will test the \"null model\" I just described (which turns out actually to be not too bad, here!), as well as another model I built, which has the form\n", - "\n", - "$y = m\\times MW +n\\times F + b$\n", - "where I've added a new descriptor, F, which is the calculated hydration free energy of the compound (calculated with a PB model). So my model predicts that solubility is a constant plus some factor times molecular weight and another factor times the calculated hydration free energy.\n", - "\n", - "Finding the parameters $m$, $n$, and $b$ is a very simple via a least-squares fit. This is done for you within Python. \n", - "Here you will need to develop several of your own linear solubility prediction models (as discussed below) and test their performance.\n", - "\n", - "## Lingo-based solubility models\n", - "\n", - "In class, when we discussed the LINGO similarity approach, I mentioned in passing that this approach had been used to attempt to estimate solubilities based on functional group/LINGO fragment contributions. This was done in work by Vidal et al. (J. Chem. Inf. Model 45(2):386-393 (2005)).\n", - "\n", - "While the approach of Vidal et al. is outside the scope of this assignment, you should quickly implement a related idea (optional for undergraduates). Particularly, you should test what happens if, for each compound in your test set you simply predict the value of the most similar (by LINGO) compound in the training set. This will allow you to quickly test how well you can predict solubilities based on pure molecular similarity to compounds in your training set. Obviously your training set is limited in size, but it’s still a worthwhile test.\n", - "\n", - "## Your assignment: Build and test at least five new solubility models plus (for graduate students) the Lingo-based approach\n", - "\n", - "This section deals with what you are trying to do. A separate section, below, deals with the “how to” aspect. Your goal in this project is to build and test five new solubility models plus the approach based on LINGO similarity. \n", - "\n", - "This section focuses primarily on building linear solubility models; I’ll assume the LINGO similarity idea is simple enough you can implement it yourself. (Though if you like, for extra credit, you can combine it with a linear solubility model to see if it can do better than either approach alone.)\n", - "\n", - "**Building solubility models**: Building a solubility model, here, amounts to selecting a set of descriptors (possible choices are listed below), getting their values, and then doing a least squares fit on the training set (the knowns) to find the parameters.\n", - "\n", - "**Testing solubility models**: Testing a solubility model, here, means taking the parameters that were found for a specific solubility model and applying that model to the test set, predicting solubility values and seeing how well the predicted values compare to experiment. \n", - "\n", - "**Descriptors**: Here, a variety of molecular descriptors are precalculated for you. These are stored below within a dictionary, `compounds`, such that `compounds[molname][descriptorname]` gives you the value of the descriptor named `descriptorname` for compound name `molname`. For example, `compounds['naloxone']['mw']` gives the molecular weight of naloxone. Here are the descriptors available to you below, by their abbreviation (i.e. \"mw\" for molecular weight) with a small amount of information about each:\n", - "- `mw`: Molecular weight\n", - "- `numatoms`: Number of atoms including hydrogens\n", - "- `heavyatoms`: Number of heavy atoms\n", - "- `ringatoms`: Number of atoms in rings\n", - "- `halogens`: Number of halogens\n", - "- `nitrogens`: Number of nitrogens\n", - "- `oxygens`: Number of oxygens\n", - "- `rotatable`: Number of rotatable bonds\n", - "- `XlogP`: Calculated logP (water to octanol partitioning coefficient)\n", - "- `aromaticrings`: Number of aromatic rings\n", - "- `PSA`: Polar surface area of the compound\n", - "- `SA`: Surface area of the compound\n", - "- `hbond-donors`: Number of hydrogen bond donors\n", - "- `hbond-acceptors`: Number of hydrogen bond acceptors\n", - "- `rings`: Number of rings \n", - "- `hydration`: Estimated hydration free energy (essentially a measure of the interactions with solvent)\n", - "\n", - "\n", - "As you might guess, some of these probably ought to have more to do with solubility than others. The number of atoms in rings is, perhaps, not that related to solubility, nor should the number of rings be that related to solubility. Perhaps there may generally be a trend that larger compounds are somewhat less soluble -- not for chemical reasons, but rather for reasons of pharmaceutical interest (many drugs tend to be somewhat large and somewhat less soluble), so some of the descriptors correlated with molecular weight (such as number of atoms, number of heavy atoms, etc.) may be better predictors of solubility than you might guess. On the other hand, hydration free energy is closely related to solubility (it’s the solution part of a solubility), and some of the other descriptors may be as well.\n", - "\n", - "In any case, one of the goals here is to build a variety of different models to start seeing (a) how you typically can get better results in the training set as you keep adding more descriptors; (b) which descriptors tend to work better; and (c) how well your best model(s) can do on the test set. You may also gain some insight into (d), how to avoid overfitting. \n", - "\n", - "So, overall, you should select some specific descriptors you think are interesting, and build models involving those. Be sure to also test the approach based on LINGO similarity if you are a grad student (you will have to implement it based on the LINGO examples already seen earlier in the course).\n", - "\n", - "### Solubility versus log S\n", - "\n", - "Solubilities potentially cover a huge range. In fact, this dataset tends to have a relatively large number of compounds which are not very soluble, and a small number which are extremely soluble. What this means is that if we aren’t careful, the few extremely soluble compounds will end up playing a huge role in the development of our models. Thus, here, it actually makes more sense to work with the logarithm of the solubility, which we’ll call logS. So, in our project, our real goal is going to be to calculate the logS, not the solubility itself. My code has been written to work with logS, so henceforth when I talk about solubility I’m really going to be talking about logS.\n", - "\n", - "### How to achieve your goal: Some specific hints\n", - "\n", - "To get going on the problem, view the code below and find the section below dealing with building first and second simple models. Here, I provide two initial models noted above -- one based on molecular weight as a descriptor, and one based on molecular weight plus hydration free energy. For your starting point, read through the code for [\"Build a first simple model\"](#Build-a-first-simple-model) based on hydration free energy and molecular weight. You will basically need to copy this code and modify it to handle your descriptors.\n", - "\n", - "Take a quick look for the code for [\"Build a second simple model\"](#Build-another-simple-model). There, the first step, before we can build a model, is to get values of our descriptors for the molecules of interest. We’ve already done that for the molecular weight in [\"Build a first simple model\"](#Build-a-first-simple-model) (refer there if you like), so this code begins by getting the hydration free energies for the knowns and the molecules we want to predict. The code is commented, but basically what you need to know is that if you want to switch to another metric, say, number of rings, you’d take the code like\n", - "\n", - "```python\n", - "known_hydr = [ compounds[mol]['hydration'] for mol in knownnames ] \n", - "known_hydr = np.array(known_hydr)\n", - "```\n", - "and switch it to\n", - "```python\n", - "known_rings = [ compounds[mol]['rings'] for mol in knownnames ] \n", - "known_rings = np.array(known_rings)\n", - "```\n", - "\n", - "This gets descriptor values for the number of rings for the knowns (training set molecules). You’d then need to do the same for changing `p_hydr` into `p_rings` (number of rings for the \"prediction\" or test set molecules).\n", - "\n", - "Then, in the next section, there least squares fit is done to actually get the parameters. The formatting here is a little tricky, but the main thing you need to know is that this code\n", - "\n", - "```python\n", - "A = np.vstack( [known_mw, known_hydr, np.ones(len(known_mw) ) ] ).T\n", - "```\n", - "provides your descriptors in a list, followed by `np.ones...`. So if you wanted to switch this to use rings, molecular weight, and hydration, you'd do something like:\n", - "```python\n", - "A = np.vstack( [known_rings, known_mw, known_hydr, np.ones(len(known_mw) ) ] ).T\n", - "```\n", - "\n", - "The actual least-squares fit is done by this:\n", - "```python\n", - "m, n, b = np.linalg.lstsq( A, known_solubilities)[0]\n", - "```\n", - "\n", - "For the case where you'd fitted rings, molecular weight, and hydration, you would calculate the resulting fitted values using:\n", - "```python\n", - "m, n, o, b = np.linalg.lstsq( A, known_solubilities)[0]\n", - "fittedvals = m*known_rings + n*known_mw + o*known_hydr + b\n", - "```\n", - "\n", - "You'd make similar changes to the computation of `predictvals` to parallel those made calculating `fittedvals`. You can leave all of the statistics code below that unchanged, and just modify the print statements to indicate what model it is you are testing. \n", - "\n", - "**Be sure to read the discussion below before getting too carried away on the problem**, as it provides some more information on assessing what is and what isn’t a good model.\n", - "\n", - "\n", - "### Performance metrics for your models\n", - "\n", - "As noted in class, one should always have metrics for judging the performance of a model. Here, my code (in `tools.py`, imported below) provides several. The Kendall tau value is a measure of ability to rank-order pairs of compounds, and runs from -1 (every pair ranked in the opposite order) to 1 (every pair ranked perfectly) with a value of 0 corresponding to every pair being ranked incorrectly. The RMS error measures a type of average error across the entire set of compounds relative to experiment; units here are logS, and lower values mean lower error on average. The $R^2$ (here called `R2` or `Rsquared`) value is the correlation coefficient, and like the Kendall tau has to do with predictive power (in this case, how well the calculated values correlate with the experimental ones), though it has some limitations (such as sensitivity to extremes of the data). It runs from -1 to 1, with -1 meaning perfect anticorrelation, 1 meaning perfect correlation, and 0 meaning no correlation. Also, for the purposes of comparison with the Hopfinger paper, I have provided code to calculate the percentage of predictions within 0.5 log units, which will allow you to compare with the different methods listed there in terms of both RMS error and percentage correct. \n", - "\n", - "In addition to these metrics, the code also automatically compares to the null hypothesis that there is no correlation between the calculated and measured logS values, and provides the probability (based on the Kendall tau test) that you could get this Kendall tau accidentally when in fact there was no correlation. When this probability is extremely small, it means that your model almost certainly has at least some predictive power.\n", - "\n", - "In general, what you should see is that as you make your models better, the Kendall tau and $R^2$ values should go up towards 1, and the RMS error should go down. You should also see the probability of getting the Kendall tau value by change go down towards zero. \n", - "\n", - "### Reminder concerning good versus bad models\n", - "\n", - "Remember that, as discussed in class, adding parameters to a model should always make it fit the data better. That is to say, if you compare to models, one using one descriptor, and another using two descriptors, in general the model with two descriptors should have a higher Kendall tau on the training set and a lower RMS error than the model with one descriptor. This doesn’t mean the model with two descriptors is better, necessarily -- it just means it has more parameters.\n", - "\n", - "So, as we noted in class, a good model is, in general, the simplest possible model that fits the data well enough. And a model with fewer parameters is generally preferable over one with more. Also, a good model should perform relatively similarly on the training set (the known compounds) versus the test set (those we are predicting). So, as you construct your models, you may want to keep this in mind. It also might be worth deliberately trying to construct a model which is overfitted, perhaps by including a whole lot of descriptors, until you reach the point where your performance is significantly worse in the test set than the training set.\n", - "\n", - "### Statistical significance tests\n", - "\n", - "In general, we should also be calculating uncertainties for our different metrics, and applying statistical significance tests to test whether each new model is significantly different than the old model. For example, the t-test could be used to attempt to reject the null hypothesis that a new model is no better on average than the old model. Also, having error bars (calculated via bootstrapping, for example) on the RMS error, $R^2$, etc., could also help us know when two models are not significantly different. However, because this assignment must be done fairly quickly, these tests are not included as part of it.\n", - "\n", - "### What to do and what to turn in\n", - "\n", - "You need to build and test at least five different models. You should try at least one model that uses four or more descriptors, hopefully getting to the point where you see significantly worse performance on the test set than on the training set. Keep track of every set of descriptors you try. \n", - "\n", - "When you complete the assignment, turn in a brief report (entering it below following the code is fine) containing your discussion and any relevant statistics, etc. This should include:\n", - "- Your Python code \n", - "- The sets of descriptors you tried\n", - "- The statistics describing performance of the model you believe is best, and a brief description of why you chose that model as best\n", - "- A brief discussion comparing your best model with performance of the contestants in Hopfinger et al., as per the logS section of table 2 on the 28 compound test. Specifically, you should be able to compare your Rsquared and percentage within 0.5 log units with the values given in that table. Is your simple model beating many of the contestants? Why do you think that is? How much worse is it than the best models? \n", - "- (If you did the LINGO section -- mandatory for graduate students) Comment on how well the LINGO similarity approach worked relative to other approaches you tried, and why you think it succeeded or failed.\n", - "\n", - "\n", - "# Now here's the material you need to get going\n", - "\n", - "Here's the Python code I'm providing for you which will form the starting point for your assignment.\n", - "\n", - "## Get some things set up" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "7uWX1z-f6dHP", - "outputId": "4b192bef-5913-438a-ea48-4d3b22ec1efd" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/dmobley/anaconda3/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['Arrow', 'Text']\n", - "`%matplotlib` prevents importing * from pylab and numpy\n", - " \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n" - ] - } - ], - "source": [ - "#============================================================================\n", - "#IMPORTS OF PACKAGES NEEDED\n", - "#============================================================================\n", - "import tools\n", - "import pickle\n", - "from openeye.oechem import *\n", - "from openeye.oemolprop import *\n", - "from openeye.oezap import *\n", - "import glob\n", - "import numpy as np\n", - "import scipy.stats\n", - "%pylab inline\n", - "\n", - "#============================================================================\n", - "#LOAD OUR MOLECULES FOR WHICH WE ARE PREDICTING SOLUBILITIES\n", - "#============================================================================\n", - "\n", - "#Load our molecules, storing lists of the names of the knowns and the ones to predict, and storing the actual molecules to a dictionary.\n", - "molecules = glob.glob('llinas_set/*.sdf')\n", - "molecules = molecules + glob.glob('llinas_predict/*.sdf')\n", - "compounds = {}\n", - "knownnames = [] #This will be a list of the molecules in our training set -- molecules with 'known' solubilities\n", - "predictnames = [] #This will be a list of molecules in the test set -- molecules with solubilities we are trying to 'predict'\n", - "\n", - "#Loop over molecules and load files, storing them to a 'compounds' dictionary\n", - "for filename in molecules:\n", - " name = filename.split('/')[1].replace('.sdf','')\n", - " compounds[name] = {}\n", - " istream = oemolistream(filename)\n", - " mol = OEMol()\n", - " OEReadMolecule( istream, mol )\n", - " compounds[name]['mol'] = mol\n", - " istream.close()\n", - " if 'predict' in filename:\n", - " predictnames.append(name)\n", - " else:\n", - " knownnames.append(name)\n", - " \n", - "#Make a list of all the molecule names\n", - "molnames = knownnames + predictnames\n", - "\n", - "#============================================================================\n", - "#MISCELLANEOUS PREP\n", - "#============================================================================\n", - "\n", - "#Get zap ready for electrostatics calculations\n", - "zap = OEZap()\n", - "zap.SetInnerDielectric( 1.0 )\n", - "zap.SetGridSpacing(0.5)\n", - "area = OEArea()\n", - "\n", - "#Reduce verbosity\n", - "OEThrow.SetLevel(OEErrorLevel_Warning)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "YPpWUEbR6dHR" - }, - "source": [ - "## Compute some descriptors and store" - ] - }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "w-XVZi3P1xKh" + }, + "source": [ + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MobleyLab/drug-computing/blob/master/uci-pharmsci/assignments/solubility/physprops_solubility.ipynb)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cFaGnxIO6dHH" + }, + "source": [ + "# Solubility calculation assignment, PharmSci 175/275\n", + "\n", + "Solubility estimation/prediction is a huge problem in drug discovery. Here, we will attempt to build a simple empirical model for solubility prediction as in a recent literature challenge. We will take a set of ~100 solubility values, and develop a simple model which reproduces those values reasonably well, then test this model on a new set of compounds (a test set). To put it another way, we have a test set and a training set, and want to use the known solubilities from the training set to predict solubilities for the test set. \n", + "\n", + "This builds on the solubility challenge of [Llinàs et al.](https://dx.doi.org/10.1021/ci800058v) and the conclusions/subsequent work of [Hopfinger et al.](https://dx.doi.org/10.1021/ci800436c).\n", + "\n", + "\n", + "## Overview\n", + "\n", + "Solubility calculation is an important problem for drug discovery, partly because it is so important that drugs be soluble. Solubility is an important factor in the design of orally bioavailable drugs, as we have discussed in class. However, no good physical models are available for work in this area yet, so most of the models for solubility estimation are empirical, based on measuring a set of simple molecular properties for molecules and combining these to estimate a solubility in some way, based on calibration to experimental data.\n", + "\n", + "Recently, Llinàs et al., [(J. Chem. Inf. Model 48:1289 (2008))](https://dx.doi.org/10.1021/ci800058v) posed a challenge: Can you predict a set of 32 solubilities on a test set, using a database (training set) of 100 reliable solubility measurements? Follow up work [(Hopfinger et al., J. Chem. Inf. Model 49:1 (2009))](https://dx.doi.org/10.1021/ci800436c) provided the solubility measurements of the test set and assessed performance of a wide variety of solubility estimation techniques in this challenge.\n", + "\n", + "Here, your job is to construct several simple linear models to predict solubilities using the training set of roughly 100 compounds, and then test their performance on the test set, comparing them with one another, with a null model, and with the performance of research groups which participated in the challenge. You should also implement and test a simple variant of the LINGO-based approach of Vidal et al. (J. Chem. Inf. Model 45(2):386-393 (2005)). \n", + "\n", + "A good deal of the technology you will need to use here is provided for you, including example models. Your job in this assignment is simply going to be to adjust the Python code I have provided to build several (five or more) new models for predicting solubilities, plus one based on the approach of Vidal, and compare their performance to select your favorite. \n", + "\n", + "## Some setup notes\n", + "\n", + "In this directory, you should also find a module you can import which will help with some statistics -- `tools.py`. You will also find two directories containing structures of molecules in the different sets -- `llinas_predict`, containing molecules whose solubilities we want to predict, and `llinas_set`, containing molecules in the training set. Additionally, in the `scripts` directory there is `solubilities.pickle` which contains solubility data (not human readable). (Note that **on Colab, you will need to either put the content of `tools.py` into this notebook or work carefully to ensure it's in a directory where it can be imported.**)\n", + "\n", + "I also provide some fairly extensive example code below which you can use as the basis for your assignment. To briefly summmarize the provided code (you can see more detail by reading the comments and code below) it loads the structures of the molecules and their names, computes a reasonably extensive set of descriptros or properties of the different molecules and loads in the actual solubility data. It then proceeds to build two extremely simple models for predicting solubilities based on a simple linear combination/fit of physical properties. You will be able to use this part of the program as a template for building your own solubility models.\n", + "\n", + "## For solubility prediction, we'll use a series of *descriptors*\n", + "\n", + "Descriptors are properties of our molecule which might (or might not) be related to the solubility. For example, we might think that solubility will in general tend to go down as molecular weight goes up, and go up as polarity increases (or go down as polarity decreases) and so on. \n", + "\n", + "Here, let's take a sample molecule and calculate a series of descriptors which we might want to use in constructing a simple solubility model. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7cjhKOdG6iSb" + }, + "source": [ + "## Installing Packages\n", + "\n", + "***If you are running this on Google Colab, please add the installation blocks from the [getting started notebook](https://github.com/MobleyLab/drug-computing/blob/master/uci-pharmsci/Getting_Started.ipynb) before executing the code below***; pay particular attention to installing of the OpenEye toolkits, mounting your Google Drive, and accessing the OpenEye license. (See also the [notebook associated with the relevant lecture](https://github.com/MobleyLab/drug-computing/blob/master/uci-pharmsci/lectures/empirical_physical_properties/physprops_solubility.ipynb))" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "euy7e9bs6dHL", + "outputId": "c22b5d8d-9966-4571-c205-46bc3e992f6d" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "vjdtSL126dHR", - "outputId": "879c0b1f-0b6f-48bf-9d64-369d0bbbc8c7" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Calculating descriptors for amitriptyline (1/123)...\n", - "Calculating descriptors for trimethoprim (2/123)...\n", - "Calculating descriptors for nortriptyline (3/123)...\n", - "Calculating descriptors for metoclopramide (4/123)...\n", - "Calculating descriptors for flurbiprofen (5/123)...\n", - "Calculating descriptors for procainamide (6/123)...\n", - "Calculating descriptors for diazoxide (7/123)...\n", - "Calculating descriptors for ciprofloxacin (8/123)...\n", - "Calculating descriptors for phenobarbital (9/123)...\n", - "Calculating descriptors for 110-phenanthroline (10/123)...\n", - "Calculating descriptors for norfloxacin (11/123)...\n", - "Calculating descriptors for enrofloxacin (12/123)...\n", - "Calculating descriptors for maprotiline (13/123)...\n", - "Calculating descriptors for 2-amino-3-bromobenzoicacid (14/123)...\n", - "Calculating descriptors for trimipramine (15/123)...\n", - "Calculating descriptors for 5-fluorouracil (16/123)...\n", - "Calculating descriptors for hexobarbital (17/123)...\n", - "Calculating descriptors for fenoprofen (18/123)...\n", - "Calculating descriptors for guanine (19/123)...\n", - "Calculating descriptors for loperamide (20/123)...\n", - "Calculating descriptors for metronidazole (21/123)...\n", - "Calculating descriptors for deprenyl (22/123)...\n", - "Calculating descriptors for amantadine (23/123)...\n", - "Calculating descriptors for tetracaine (24/123)...\n", - "Calculating descriptors for difloxacin (25/123)...\n", - "Calculating descriptors for niflumicacid (26/123)...\n", - "Calculating descriptors for carprofen (27/123)...\n", - "Calculating descriptors for glipizide (28/123)...\n", - "Calculating descriptors for papaverine (29/123)...\n", - "Calculating descriptors for procaine (30/123)...\n", - "Calculating descriptors for mefenamicacid (31/123)...\n", - "Calculating descriptors for nitrofurantoin (32/123)...\n", - "Calculating descriptors for ranitidine (33/123)...\n", - "Calculating descriptors for tryptamine (34/123)...\n", - "Calculating descriptors for acetaminophen (35/123)...\n", - "Calculating descriptors for desipramine (36/123)...\n", - "Calculating descriptors for sulfathiazole (37/123)...\n", - "Calculating descriptors for 1-benzylimidazole (38/123)...\n", - "Calculating descriptors for sulfacetamide (39/123)...\n", - "Calculating descriptors for 5-bromo-24-dihydroxy-benzoicacid (40/123)...\n", - "Calculating descriptors for meclizine (41/123)...\n", - "Calculating descriptors for danofloxacin (42/123)...\n", - "Calculating descriptors for ofloxacin (43/123)...\n", - "Calculating descriptors for bupivacaine (44/123)...\n", - "Calculating descriptors for 1-naphthol (45/123)...\n", - "Calculating descriptors for sertraline (46/123)...\n", - "Calculating descriptors for levofloxacin (47/123)...\n", - "Calculating descriptors for alprenolol (48/123)...\n", - "Calculating descriptors for l-proline (49/123)...\n", - "Calculating descriptors for phenazopyridine (50/123)...\n", - "Calculating descriptors for chlorprothixene (51/123)...\n", - "Calculating descriptors for oxytetracycline (52/123)...\n", - "Calculating descriptors for phenylbutazone (53/123)...\n", - "Calculating descriptors for trichlormethiazide (54/123)...\n", - "Calculating descriptors for micronazole (55/123)...\n", - "Calculating descriptors for thymol (56/123)...\n", - "Calculating descriptors for chlorpromazine (57/123)...\n", - "Calculating descriptors for 55-diphenylhydantoin (58/123)...\n", - "Calculating descriptors for sulindac (59/123)...\n", - "Calculating descriptors for tolmetin (60/123)...\n", - "Calculating descriptors for acetazolamide (61/123)...\n", - "Calculating descriptors for sparfloxacin (62/123)...\n", - "Calculating descriptors for diphenhydramine (63/123)...\n", - "Calculating descriptors for phthalicacid (64/123)...\n", - "Calculating descriptors for warfarin (65/123)...\n", - "Calculating descriptors for nalidixicacid (66/123)...\n", - "Calculating descriptors for naproxen (67/123)...\n", - "Calculating descriptors for hydroflumethiazide (68/123)...\n", - "Calculating descriptors for amodiaquine (69/123)...\n", - "Calculating descriptors for amiodarone (70/123)...\n", - "Calculating descriptors for chlorpheniramine (71/123)...\n", - "Calculating descriptors for lomefloxacin (72/123)...\n", - "Calculating descriptors for flufenamicacid (73/123)...\n", - "Calculating descriptors for cephalothin (74/123)...\n", - "Calculating descriptors for diclofenac (75/123)...\n", - "Calculating descriptors for famotidine (76/123)...\n", - "Calculating descriptors for 4-hydroxybenzoicacid (77/123)...\n", - "Calculating descriptors for quinine (78/123)...\n", - "Calculating descriptors for sulfamethazine (79/123)...\n", - "Calculating descriptors for diltiazem (80/123)...\n", - "Calculating descriptors for ibuprofen (81/123)...\n", - "Calculating descriptors for pindolol (82/123)...\n", - "Calculating descriptors for flumequine (83/123)...\n", - "Calculating descriptors for bromogramine (84/123)...\n", - "Calculating descriptors for atropine (85/123)...\n", - "Calculating descriptors for sarafloxacin (86/123)...\n", - "Calculating descriptors for 4-iodophenol (87/123)...\n", - "Calculating descriptors for chlorpropamide (88/123)...\n", - "Calculating descriptors for verapamil (89/123)...\n", - "Calculating descriptors for azathioprine (90/123)...\n", - "Calculating descriptors for propranolol (91/123)...\n", - "Calculating descriptors for orbifloxacin (92/123)...\n", - "Calculating descriptors for cimetidine (93/123)...\n", - "Calculating descriptors for piroxicam (94/123)...\n", - "Calculating descriptors for carvedilol (95/123)...\n", - "Calculating descriptors for tetracycline (96/123)...\n", - "Calculating descriptors for naloxone (97/123)...\n", - "Calculating descriptors for dipyridamole (98/123)...\n", - "Calculating descriptors for terfenadine (99/123)...\n", - "Calculating descriptors for pyrimethamine (100/123)...\n", - "Calculating descriptors for thiabendazole (101/123)...\n", - "Calculating descriptors for 1-napthoicacid (102/123)...\n", - "Calculating descriptors for imipramine (103/123)...\n", - "Calculating descriptors for sulfamerazine (104/123)...\n", - "Calculating descriptors for benzocaine (105/123)...\n", - "Calculating descriptors for dibucaine (106/123)...\n", - "Calculating descriptors for acebutolol (107/123)...\n", - "Calculating descriptors for hydrochlorothiazide (108/123)...\n", - "Calculating descriptors for meclofenamicacid (109/123)...\n", - "Calculating descriptors for probenecid (110/123)...\n", - "Calculating descriptors for lidocaine (111/123)...\n", - "Calculating descriptors for bendroflumethiazide (112/123)...\n", - "Calculating descriptors for trazodone (113/123)...\n", - "Calculating descriptors for indomethacin (114/123)...\n", - "Calculating descriptors for furosemide (115/123)...\n", - "Calculating descriptors for clozapine (116/123)...\n", - "Calculating descriptors for salicylicacid (117/123)...\n", - "Calculating descriptors for ketoprofen (118/123)...\n", - "Calculating descriptors for tolbutamide (119/123)...\n", - "Calculating descriptors for folicacid (120/123)...\n", - "Calculating descriptors for amoxicillin (121/123)...\n", - "Calculating descriptors for diethylstilbestrol (122/123)...\n", - "Calculating descriptors for diflunisal (123/123)...\n" - ] - } - ], - "source": [ - "#============================================================================\n", - "#COMPUTE DESCRIPTORS FOR OUR MOLECULES -- VARIOUS PROPERTIES OF THE MOLECULES WHICH MIGHT BE USEFUL IN SOLUBILITY ESTIMATION\n", - "#============================================================================\n", - "\n", - "#Compute a bunch of descriptors for our molecules. Descriptors will be stored in the compounds dictionary, by compound name.\n", - "#For example, compounds['terfenadine']['mw'] will give the 'mw' (molecular weight) of terfenadine).\n", - "#A full description of the descriptors calculated will be put in the homework writeup.\n", - "\n", - "#Loop over molecules\n", - "for molname in molnames:\n", - " print(\"Calculating descriptors for %s (%s/%s)...\" % (molname, molnames.index(molname)+1, len(molnames) )) #Print progress\n", - "\n", - " #Load the OEMol representation of our molecule from where it's stored\n", - " mol = compounds[molname]['mol']\n", - "\n", - " #Compute molecular weight and store\n", - " compounds[ molname ]['mw'] = OECalculateMolecularWeight( mol )\n", - "\n", - " #Number of atoms -- store\n", - " compounds[molname]['numatoms'] = mol.NumAtoms()\n", - "\n", - " #Number of heavy atoms\n", - " compounds[molname]['heavyatoms'] = OECount(mol, OEIsHeavy() )\n", - "\n", - " #Number of ring atoms\n", - " compounds[molname]['ringatoms'] = OECount(mol, OEAtomIsInRing() )\n", - "\n", - " #Number of halogens\n", - " compounds[molname]['halogens'] = OECount( mol, OEIsHalogen() )\n", - "\n", - " #Number of nitrogens\n", - " compounds[molname]['nitrogens'] = OECount( mol, OEIsNitrogen() )\n", - "\n", - " #Number of oxygens\n", - " compounds[molname]['oxygens'] = OECount( mol, OEIsOxygen() )\n", - "\n", - " #Number of rotatable bonds\n", - " compounds[molname]['rotatable'] = OECount( mol, OEIsRotor() )\n", - "\n", - " #Calculated logP\n", - " compounds[molname]['XlogP'] = OEGetXLogP( mol )\n", - "\n", - " #Number of aromatic rings\n", - " compounds[molname]['aromaticrings'] = OEGetAromaticRingCount( mol )\n", - "\n", - " #Calculate lots of other properties using molprop toolkit as per example in OE MolProp manual\n", - " #Handle the setup of 'filter', which computes lots of properties with the goal of filtering compounds. Here we'll not do any filtering\n", - " #and will use it solely for property calculation\n", - " filt = OEFilter()\n", - " ostr = oeosstream()\n", - " pwnd = False\n", - " filt.SetTable( ostr, pwnd)\n", - " headers = ostr.str().decode('UTF-8').split('\\t')\n", - " ostr.clear()\n", - " filt(mol)\n", - " fields = ostr.str().decode('UTF-8').split('\\t')\n", - " tmpdct = dict( zip(headers, fields) ) #Format the data we need into a dictionary for easy extraction\n", - " \n", - " #Extract polar surface area, store\n", - " compounds[molname]['PSA'] = tmpdct[ '2d PSA' ]\n", - " #Number of hbond donors\n", - " compounds[molname]['hbond-donors'] = int(tmpdct['hydrogen-bond donors'])\n", - " #Number of hbond acceptors\n", - " compounds[molname]['hbond-acceptors'] = int(tmpdct['hydrogen-bond acceptors'])\n", - " #Number of rings\n", - " compounds[molname]['rings'] = int(tmpdct['number of ring systems'])\n", - "\n", - " #Quickly estimate hydration free energy, or a value correlated with that -- from ZAP manual\n", - " #Do ZAP setup for molecule\n", - " OEAssignBondiVdWRadii(mol)\n", - " OEMMFFAtomTypes(mol)\n", - " OEMMFF94PartialCharges(mol)\n", - " zap.SetMolecule( mol )\n", - " solv = zap.CalcSolvationEnergy()\n", - " aval = area.GetArea( mol )\n", - " #Empirically estimate solvation free energy (hydration)\n", - " solvation = 0.59*solv + 0.01*aval #Convert electrostatic part to kcal/mol; use empirically determined kcal/sq angstrom value times surface area term\n", - " compounds[molname]['hydration'] = solvation\n", - " #Also store surface area\n", - " compounds[molname]['SA'] = aval" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Molecular weight: 128.17\n", + "Number of atoms: 18\n", + "Number of heavy atoms: 10\n", + "Number of ring atoms: 10\n", + "Number of halogens: 0\n", + "Number of nitrogens: 0\n", + "Number of oxygens: 0\n", + "Number of rotatable bonds: 0\n", + "Calculated logP: 3.57\n", + "Number of aromatic rings: 2\n", + "Polar surface area: 0.00\n", + "Number of hbond donors: 0\n", + "Number of hbond acceptors: 0\n", + "Number of rings: 1\n", + "Calculated solvation free energy: -4.21\n" + ] + } + ], + "source": [ + "from openeye.oechem import *\n", + "from openeye.oemolprop import *\n", + "from openeye.oeiupac import *\n", + "from openeye.oezap import *\n", + "from openeye.oeomega import *\n", + "import numpy as np\n", + "import scipy.stats\n", + "\n", + "#Initialize an OpenEye molecule\n", + "mol = OEMol()\n", + "\n", + "#let's look at phenol\n", + "OEParseIUPACName( mol, 'naphthalene' )\n", + "\n", + "#Generate conformation\n", + "omega = OEOmega()\n", + "omega(mol)\n", + "\n", + "#Here one of the descriptors we'll use is the calculated solvation free energy, from OpenEye's ZAP electrostatics solver\n", + "#Get zap ready for electrostatics calculations\n", + "zap = OEZap()\n", + "zap.SetInnerDielectric( 1.0 )\n", + "zap.SetGridSpacing(0.5)\n", + "area = OEArea()\n", + "\n", + "#Reduce verbosity\n", + "OEThrow.SetLevel(OEErrorLevel_Warning)\n", + "\n", + "\n", + "#Let's print a bunch of properties\n", + "#Molecular weight\n", + "print( \"Molecular weight: %.2f\" % OECalculateMolecularWeight(mol) )\n", + "#Number of atoms\n", + "print( \"Number of atoms: %s\" % mol.NumAtoms() ) \n", + "#Number of heavy atoms\n", + "print( \"Number of heavy atoms: %s\" % OECount(mol, OEIsHeavy() ) )\n", + "#Number of ring atoms\n", + "print( \"Number of ring atoms: %s\" % OECount(mol, OEAtomIsInRing() ) )\n", + "#Number of halogens\n", + "print( \"Number of halogens: %s\" % OECount( mol, OEIsHalogen() ))\n", + "print (\"Number of nitrogens: %s\" % OECount( mol, OEIsNitrogen() ) )\n", + "print( \"Number of oxygens: %s\" % OECount( mol, OEIsOxygen() ) )\n", + "print( \"Number of rotatable bonds: %s\" % OECount( mol, OEIsRotor() ) )\n", + "\n", + "#Calculated logP - water to octanol partitioning coefficient (which is often something which may correlate somewhat with solubility)\n", + "print( \"Calculated logP: %.2f\" % OEGetXLogP( mol ) )\n", + "\n", + "print( \"Number of aromatic rings: %s\" % OEGetAromaticRingCount( mol ) )\n", + "\n", + " \n", + " \n", + "#Calculate lots of other properties using molprop toolkit as per example in OE MolProp manual\n", + "#Handle the setup of 'filter', which computes lots of properties with the goal of filtering compounds. Here we'll not do any filtering\n", + "#and will use it solely for property calculation\n", + "filt = OEFilter()\n", + "ostr = oeosstream()\n", + "pwnd = False\n", + "filt.SetTable( ostr, pwnd)\n", + "headers = ostr.str().decode().split('\\t')\n", + "ostr.clear()\n", + "filt(mol)\n", + "fields = ostr.str().decode().split('\\t')\n", + "tmpdct = dict( zip(headers, fields) ) #Format the data we need into a dictionary for easy extraction\n", + "\n", + "print(\"Polar surface area: %s\" % tmpdct[ '2d PSA' ] )\n", + "print(\"Number of hbond donors: %s\" % int(tmpdct['hydrogen-bond donors']) )\n", + "print(\"Number of hbond acceptors: %s\" % int(tmpdct['hydrogen-bond acceptors']) )\n", + "print (\"Number of rings: %s\" % int(tmpdct['number of ring systems']) )\n", + "#print(tmpdct.keys())\n", + "\n", + "#Quickly estimate hydration free energy, or a value correlated with that -- from ZAP manual\n", + "#Do ZAP setup for molecule\n", + "OEAssignBondiVdWRadii(mol)\n", + "OEMMFFAtomTypes(mol)\n", + "OEMMFF94PartialCharges(mol)\n", + "zap.SetMolecule( mol )\n", + "solv = zap.CalcSolvationEnergy()\n", + "aval = area.GetArea( mol )\n", + "#Empirically estimate solvation free energy (hydration)\n", + "solvation = 0.59*solv + 0.01*aval #Convert electrostatic part to kcal/mol; use empirically determined kcal/sq angstrom value times surface area term\n", + "print (\"Calculated solvation free energy: %.2f\" % solvation)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5TjHi2Q86dHO" + }, + "source": [ + "## Linear models for solubility: Understanding your task\n", + "\n", + "Here, your first job is to construct some linear models for solubility and attempt to use them to predict solubilities for a test set of molecules. \n", + "Many different models for solubilities would be possible. Here, however, we focus on linear models -- that is, models having the form:\n", + "$y = mx + b$\n", + "\n", + "where $y$ is the solubility, $m$ and $b$ are constants, and $x$ is some descriptor of the molecule. Or with two variables:\n", + "$y = mx + nz + b$\n", + "\n", + "Here we've added a second descrptor, $z$, and another constant, $n$. Still more generally, we could write:\n", + "\n", + "$y = b + \\sum_i m_i x_i$\n", + "\n", + "In this case, we now have a constant, $b$, and a set of other constants, $m_i$, and descriptors, $x_i$; the sum runs over all values of $i$.\n", + "\n", + "What does this all mean? Basically, we are going to assume that we can predict solubilities out of some linear combination of descriptors or molecular properties. For example, (as a null model) I might assume that solubility can be predicted simply based on molecular weight -- perhaps heavier compounds will in general be less (or more) soluble. I might write:\n", + "\n", + "$y = m\\times MW + b$\n", + "\n", + "This has the form $y=mx + b$ but I replaced $x$ with $MW$, the molecular weight. To fit this model, I would then need to find the coefficients $m$ and $b$ to give the best agreement with the actual solublity data.\n", + "\n", + "Here, I would first develop parameters $m$ and $b$ to fit my training set -- that is, I would fit $m$ and $b$ on the training set data, the (roughly 100) compounds provided in the first paper. Then, I would apply the same $m$ and $b$ to the test set data to see how well I can predict the 32 \"new\" compounds. \n", + "\n", + "In this project, you will test the \"null model\" I just described (which turns out actually to be not too bad, here!), as well as another model I built, which has the form\n", + "\n", + "$y = m\\times MW +n\\times F + b$\n", + "where I've added a new descriptor, F, which is the calculated hydration free energy of the compound (calculated with a PB model). So my model predicts that solubility is a constant plus some factor times molecular weight and another factor times the calculated hydration free energy.\n", + "\n", + "Finding the parameters $m$, $n$, and $b$ is a very simple via a least-squares fit. This is done for you within Python. \n", + "Here you will need to develop several of your own linear solubility prediction models (as discussed below) and test their performance.\n", + "\n", + "## Lingo-based solubility models\n", + "\n", + "In class, when we discussed the LINGO similarity approach, I mentioned in passing that this approach had been used to attempt to estimate solubilities based on functional group/LINGO fragment contributions. This was done in work by Vidal et al. (J. Chem. Inf. Model 45(2):386-393 (2005)).\n", + "\n", + "While the approach of Vidal et al. is outside the scope of this assignment, you should quickly implement a related idea (optional for undergraduates). Particularly, you should test what happens if, for each compound in your test set you simply predict the value of the most similar (by LINGO) compound in the training set. This will allow you to quickly test how well you can predict solubilities based on pure molecular similarity to compounds in your training set. Obviously your training set is limited in size, but it’s still a worthwhile test.\n", + "\n", + "## Your assignment: Build and test at least five new solubility models plus (for graduate students) the Lingo-based approach\n", + "\n", + "This section deals with what you are trying to do. A separate section, below, deals with the “how to” aspect. Your goal in this project is to build and test five new solubility models plus the approach based on LINGO similarity. \n", + "\n", + "This section focuses primarily on building linear solubility models; I’ll assume the LINGO similarity idea is simple enough you can implement it yourself. (Though if you like, for extra credit, you can combine it with a linear solubility model to see if it can do better than either approach alone.)\n", + "\n", + "**Building solubility models**: Building a solubility model, here, amounts to selecting a set of descriptors (possible choices are listed below), getting their values, and then doing a least squares fit on the training set (the knowns) to find the parameters.\n", + "\n", + "**Testing solubility models**: Testing a solubility model, here, means taking the parameters that were found for a specific solubility model and applying that model to the test set, predicting solubility values and seeing how well the predicted values compare to experiment. \n", + "\n", + "**Descriptors**: Here, a variety of molecular descriptors are precalculated for you. These are stored below within a dictionary, `compounds`, such that `compounds[molname][descriptorname]` gives you the value of the descriptor named `descriptorname` for compound name `molname`. For example, `compounds['naloxone']['mw']` gives the molecular weight of naloxone. Here are the descriptors available to you below, by their abbreviation (i.e. \"mw\" for molecular weight) with a small amount of information about each:\n", + "- `mw`: Molecular weight\n", + "- `numatoms`: Number of atoms including hydrogens\n", + "- `heavyatoms`: Number of heavy atoms\n", + "- `ringatoms`: Number of atoms in rings\n", + "- `halogens`: Number of halogens\n", + "- `nitrogens`: Number of nitrogens\n", + "- `oxygens`: Number of oxygens\n", + "- `rotatable`: Number of rotatable bonds\n", + "- `XlogP`: Calculated logP (water to octanol partitioning coefficient)\n", + "- `aromaticrings`: Number of aromatic rings\n", + "- `PSA`: Polar surface area of the compound\n", + "- `SA`: Surface area of the compound\n", + "- `hbond-donors`: Number of hydrogen bond donors\n", + "- `hbond-acceptors`: Number of hydrogen bond acceptors\n", + "- `rings`: Number of rings \n", + "- `hydration`: Estimated hydration free energy (essentially a measure of the interactions with solvent)\n", + "\n", + "\n", + "As you might guess, some of these probably ought to have more to do with solubility than others. The number of atoms in rings is, perhaps, not that related to solubility, nor should the number of rings be that related to solubility. Perhaps there may generally be a trend that larger compounds are somewhat less soluble -- not for chemical reasons, but rather for reasons of pharmaceutical interest (many drugs tend to be somewhat large and somewhat less soluble), so some of the descriptors correlated with molecular weight (such as number of atoms, number of heavy atoms, etc.) may be better predictors of solubility than you might guess. On the other hand, hydration free energy is closely related to solubility (it’s the solution part of a solubility), and some of the other descriptors may be as well.\n", + "\n", + "In any case, one of the goals here is to build a variety of different models to start seeing (a) how you typically can get better results in the training set as you keep adding more descriptors; (b) which descriptors tend to work better; and (c) how well your best model(s) can do on the test set. You may also gain some insight into (d), how to avoid overfitting. \n", + "\n", + "So, overall, you should select some specific descriptors you think are interesting, and build models involving those. Be sure to also test the approach based on LINGO similarity if you are a grad student (you will have to implement it based on the LINGO examples already seen earlier in the course).\n", + "\n", + "### Solubility versus log S\n", + "\n", + "Solubilities potentially cover a huge range. In fact, this dataset tends to have a relatively large number of compounds which are not very soluble, and a small number which are extremely soluble. What this means is that if we aren’t careful, the few extremely soluble compounds will end up playing a huge role in the development of our models. Thus, here, it actually makes more sense to work with the logarithm of the solubility, which we’ll call logS. So, in our project, our real goal is going to be to calculate the logS, not the solubility itself. My code has been written to work with logS, so henceforth when I talk about solubility I’m really going to be talking about logS.\n", + "\n", + "### How to achieve your goal: Some specific hints\n", + "\n", + "To get going on the problem, view the code below and find the section below dealing with building first and second simple models. Here, I provide two initial models noted above -- one based on molecular weight as a descriptor, and one based on molecular weight plus hydration free energy. For your starting point, read through the code for [\"Build a first simple model\"](#Build-a-first-simple-model) based on hydration free energy and molecular weight. You will basically need to copy this code and modify it to handle your descriptors.\n", + "\n", + "Take a quick look for the code for [\"Build a second simple model\"](#Build-another-simple-model). There, the first step, before we can build a model, is to get values of our descriptors for the molecules of interest. We’ve already done that for the molecular weight in [\"Build a first simple model\"](#Build-a-first-simple-model) (refer there if you like), so this code begins by getting the hydration free energies for the knowns and the molecules we want to predict. The code is commented, but basically what you need to know is that if you want to switch to another metric, say, number of rings, you’d take the code like\n", + "\n", + "```python\n", + "known_hydr = [ compounds[mol]['hydration'] for mol in knownnames ] \n", + "known_hydr = np.array(known_hydr)\n", + "```\n", + "and switch it to\n", + "```python\n", + "known_rings = [ compounds[mol]['rings'] for mol in knownnames ] \n", + "known_rings = np.array(known_rings)\n", + "```\n", + "\n", + "This gets descriptor values for the number of rings for the knowns (training set molecules). You’d then need to do the same for changing `p_hydr` into `p_rings` (number of rings for the \"prediction\" or test set molecules).\n", + "\n", + "Then, in the next section, there least squares fit is done to actually get the parameters. The formatting here is a little tricky, but the main thing you need to know is that this code\n", + "\n", + "```python\n", + "A = np.vstack( [known_mw, known_hydr, np.ones(len(known_mw) ) ] ).T\n", + "```\n", + "provides your descriptors in a list, followed by `np.ones...`. So if you wanted to switch this to use rings, molecular weight, and hydration, you'd do something like:\n", + "```python\n", + "A = np.vstack( [known_rings, known_mw, known_hydr, np.ones(len(known_mw) ) ] ).T\n", + "```\n", + "\n", + "The actual least-squares fit is done by this:\n", + "```python\n", + "m, n, b = np.linalg.lstsq( A, known_solubilities)[0]\n", + "```\n", + "\n", + "For the case where you'd fitted rings, molecular weight, and hydration, you would calculate the resulting fitted values using:\n", + "```python\n", + "m, n, o, b = np.linalg.lstsq( A, known_solubilities)[0]\n", + "fittedvals = m*known_rings + n*known_mw + o*known_hydr + b\n", + "```\n", + "\n", + "You'd make similar changes to the computation of `predictvals` to parallel those made calculating `fittedvals`. You can leave all of the statistics code below that unchanged, and just modify the print statements to indicate what model it is you are testing. \n", + "\n", + "**Be sure to read the discussion below before getting too carried away on the problem**, as it provides some more information on assessing what is and what isn’t a good model.\n", + "\n", + "\n", + "### Performance metrics for your models\n", + "\n", + "As noted in class, one should always have metrics for judging the performance of a model. Here, my code (in `tools.py`, imported below) provides several. The Kendall tau value is a measure of ability to rank-order pairs of compounds, and runs from -1 (every pair ranked in the opposite order) to 1 (every pair ranked perfectly) with a value of 0 corresponding to every pair being ranked incorrectly. The RMS error measures a type of average error across the entire set of compounds relative to experiment; units here are logS, and lower values mean lower error on average. The $R^2$ (here called `R2` or `Rsquared`) value is the correlation coefficient, and like the Kendall tau has to do with predictive power (in this case, how well the calculated values correlate with the experimental ones), though it has some limitations (such as sensitivity to extremes of the data). It runs from -1 to 1, with -1 meaning perfect anticorrelation, 1 meaning perfect correlation, and 0 meaning no correlation. Also, for the purposes of comparison with the Hopfinger paper, I have provided code to calculate the percentage of predictions within 0.5 log units, which will allow you to compare with the different methods listed there in terms of both RMS error and percentage correct. \n", + "\n", + "In addition to these metrics, the code also automatically compares to the null hypothesis that there is no correlation between the calculated and measured logS values, and provides the probability (based on the Kendall tau test) that you could get this Kendall tau accidentally when in fact there was no correlation. When this probability is extremely small, it means that your model almost certainly has at least some predictive power.\n", + "\n", + "In general, what you should see is that as you make your models better, the Kendall tau and $R^2$ values should go up towards 1, and the RMS error should go down. You should also see the probability of getting the Kendall tau value by change go down towards zero. \n", + "\n", + "### Reminder concerning good versus bad models\n", + "\n", + "Remember that, as discussed in class, adding parameters to a model should always make it fit the data better. That is to say, if you compare to models, one using one descriptor, and another using two descriptors, in general the model with two descriptors should have a higher Kendall tau on the training set and a lower RMS error than the model with one descriptor. This doesn’t mean the model with two descriptors is better, necessarily -- it just means it has more parameters.\n", + "\n", + "So, as we noted in class, a good model is, in general, the simplest possible model that fits the data well enough. And a model with fewer parameters is generally preferable over one with more. Also, a good model should perform relatively similarly on the training set (the known compounds) versus the test set (those we are predicting). So, as you construct your models, you may want to keep this in mind. It also might be worth deliberately trying to construct a model which is overfitted, perhaps by including a whole lot of descriptors, until you reach the point where your performance is significantly worse in the test set than the training set.\n", + "\n", + "### Statistical significance tests\n", + "\n", + "In general, we should also be calculating uncertainties for our different metrics, and applying statistical significance tests to test whether each new model is significantly different than the old model. For example, the t-test could be used to attempt to reject the null hypothesis that a new model is no better on average than the old model. Also, having error bars (calculated via bootstrapping, for example) on the RMS error, $R^2$, etc., could also help us know when two models are not significantly different. However, because this assignment must be done fairly quickly, these tests are not included as part of it.\n", + "\n", + "### What to do and what to turn in\n", + "\n", + "You need to build and test at least five different models. You should try at least one model that uses four or more descriptors, hopefully getting to the point where you see significantly worse performance on the test set than on the training set. Keep track of every set of descriptors you try. \n", + "\n", + "When you complete the assignment, turn in a brief report (entering it below following the code is fine) containing your discussion and any relevant statistics, etc. This should include:\n", + "- Your Python code \n", + "- The sets of descriptors you tried\n", + "- The statistics describing performance of the model you believe is best, and a brief description of why you chose that model as best\n", + "- A brief discussion comparing your best model with performance of the contestants in Hopfinger et al., as per the logS section of table 2 on the 28 compound test. Specifically, you should be able to compare your Rsquared and percentage within 0.5 log units with the values given in that table. Is your simple model beating many of the contestants? Why do you think that is? How much worse is it than the best models? \n", + "- (If you did the LINGO section -- mandatory for graduate students) Comment on how well the LINGO similarity approach worked relative to other approaches you tried, and why you think it succeeded or failed.\n", + "\n", + "\n", + "# Now here's the material you need to get going\n", + "\n", + "Here's the Python code I'm providing for you which will form the starting point for your assignment.\n", + "\n", + "## Get some things set up" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "7uWX1z-f6dHP", + "outputId": "4b192bef-5913-438a-ea48-4d3b22ec1efd" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "i_z-a_T-6dHR" - }, - "source": [ - "## Load in the reference data from Llinas et al./Hopfinger et al." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "PLxF1xBH6dHS" - }, - "outputs": [], - "source": [ - "#============================================================================\n", - "# LOAD AND PREP THE ACTUAL SOLUBILITY DATA WE'LL BE USING\n", - "#============================================================================\n", - "\n", - "#Load solubility data\n", - "import pickle\n", - "file = open('scripts/solubilities.pickle', 'rb')\n", - "solubilities = pickle.load(file)\n", - "file.close()\n", - "new_solubilities = {}\n", - "#Adjust some naming to match that from file names\n", - "for name in solubilities.keys():\n", - " newname = name.replace(',','').replace(' ','')\n", - " new_solubilities[newname] = solubilities[name]\n", - "solubilities = new_solubilities\n", - " \n", - "#Build arrays of solubilities -- actually, work with logarithms of solubilities since they cover such a huge range\n", - "#Build a list of the solubilities for the molecules in the training set (knowns)\n", - "known_solubilities = [ solubilities[mol] for mol in knownnames]\n", - "#Convert to an array and take the log\n", - "known_solubilities = log(np.array( known_solubilities)) #Note conversion to log\n", - "#Build a list of the solubilities for molecules in the test set (unknowns)\n", - "predict_solubilities = [ solubilities[mol] for mol in predictnames]\n", - "#Convert to an array and take the log\n", - "predict_solubilities = log(np.array( predict_solubilities )) #Note conversion to log" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/dmobley/anaconda3/lib/python3.6/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['Arrow', 'Text']\n", + "`%matplotlib` prevents importing * from pylab and numpy\n", + " \"\\n`%matplotlib` prevents importing * from pylab and numpy\"\n" + ] + } + ], + "source": [ + "#============================================================================\n", + "#IMPORTS OF PACKAGES NEEDED\n", + "#============================================================================\n", + "import tools\n", + "import pickle\n", + "from openeye.oechem import *\n", + "from openeye.oemolprop import *\n", + "from openeye.oezap import *\n", + "import glob\n", + "import numpy as np\n", + "import scipy.stats\n", + "%pylab inline\n", + "\n", + "#============================================================================\n", + "#LOAD OUR MOLECULES FOR WHICH WE ARE PREDICTING SOLUBILITIES\n", + "#============================================================================\n", + "\n", + "#Load our molecules, storing lists of the names of the knowns and the ones to predict, and storing the actual molecules to a dictionary.\n", + "molecules = glob.glob('llinas_set/*.sdf')\n", + "molecules = molecules + glob.glob('llinas_predict/*.sdf')\n", + "compounds = {}\n", + "knownnames = [] #This will be a list of the molecules in our training set -- molecules with 'known' solubilities\n", + "predictnames = [] #This will be a list of molecules in the test set -- molecules with solubilities we are trying to 'predict'\n", + "\n", + "#Loop over molecules and load files, storing them to a 'compounds' dictionary\n", + "for filename in molecules:\n", + " name = filename.split('/')[1].replace('.sdf','')\n", + " compounds[name] = {}\n", + " istream = oemolistream(filename)\n", + " mol = OEMol()\n", + " OEReadMolecule( istream, mol )\n", + " compounds[name]['mol'] = mol\n", + " istream.close()\n", + " if 'predict' in filename:\n", + " predictnames.append(name)\n", + " else:\n", + " knownnames.append(name)\n", + " \n", + "#Make a list of all the molecule names\n", + "molnames = knownnames + predictnames\n", + "\n", + "#============================================================================\n", + "#MISCELLANEOUS PREP\n", + "#============================================================================\n", + "\n", + "#Get zap ready for electrostatics calculations\n", + "zap = OEZap()\n", + "zap.SetInnerDielectric( 1.0 )\n", + "zap.SetGridSpacing(0.5)\n", + "area = OEArea()\n", + "\n", + "#Reduce verbosity\n", + "OEThrow.SetLevel(OEErrorLevel_Warning)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YPpWUEbR6dHR" + }, + "source": [ + "## Compute some descriptors and store" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vjdtSL126dHR", + "outputId": "879c0b1f-0b6f-48bf-9d64-369d0bbbc8c7" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "cdNp-A3j6dHS" - }, - "source": [ - "## Build a first simple model" - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "Calculating descriptors for amitriptyline (1/123)...\n", + "Calculating descriptors for trimethoprim (2/123)...\n", + "Calculating descriptors for nortriptyline (3/123)...\n", + "Calculating descriptors for metoclopramide (4/123)...\n", + "Calculating descriptors for flurbiprofen (5/123)...\n", + "Calculating descriptors for procainamide (6/123)...\n", + "Calculating descriptors for diazoxide (7/123)...\n", + "Calculating descriptors for ciprofloxacin (8/123)...\n", + "Calculating descriptors for phenobarbital (9/123)...\n", + "Calculating descriptors for 110-phenanthroline (10/123)...\n", + "Calculating descriptors for norfloxacin (11/123)...\n", + "Calculating descriptors for enrofloxacin (12/123)...\n", + "Calculating descriptors for maprotiline (13/123)...\n", + "Calculating descriptors for 2-amino-3-bromobenzoicacid (14/123)...\n", + "Calculating descriptors for trimipramine (15/123)...\n", + "Calculating descriptors for 5-fluorouracil (16/123)...\n", + "Calculating descriptors for hexobarbital (17/123)...\n", + "Calculating descriptors for fenoprofen (18/123)...\n", + "Calculating descriptors for guanine (19/123)...\n", + "Calculating descriptors for loperamide (20/123)...\n", + "Calculating descriptors for metronidazole (21/123)...\n", + "Calculating descriptors for deprenyl (22/123)...\n", + "Calculating descriptors for amantadine (23/123)...\n", + "Calculating descriptors for tetracaine (24/123)...\n", + "Calculating descriptors for difloxacin (25/123)...\n", + "Calculating descriptors for niflumicacid (26/123)...\n", + "Calculating descriptors for carprofen (27/123)...\n", + "Calculating descriptors for glipizide (28/123)...\n", + "Calculating descriptors for papaverine (29/123)...\n", + "Calculating descriptors for procaine (30/123)...\n", + "Calculating descriptors for mefenamicacid (31/123)...\n", + "Calculating descriptors for nitrofurantoin (32/123)...\n", + "Calculating descriptors for ranitidine (33/123)...\n", + "Calculating descriptors for tryptamine (34/123)...\n", + "Calculating descriptors for acetaminophen (35/123)...\n", + "Calculating descriptors for desipramine (36/123)...\n", + "Calculating descriptors for sulfathiazole (37/123)...\n", + "Calculating descriptors for 1-benzylimidazole (38/123)...\n", + "Calculating descriptors for sulfacetamide (39/123)...\n", + "Calculating descriptors for 5-bromo-24-dihydroxy-benzoicacid (40/123)...\n", + "Calculating descriptors for meclizine (41/123)...\n", + "Calculating descriptors for danofloxacin (42/123)...\n", + "Calculating descriptors for ofloxacin (43/123)...\n", + "Calculating descriptors for bupivacaine (44/123)...\n", + "Calculating descriptors for 1-naphthol (45/123)...\n", + "Calculating descriptors for sertraline (46/123)...\n", + "Calculating descriptors for levofloxacin (47/123)...\n", + "Calculating descriptors for alprenolol (48/123)...\n", + "Calculating descriptors for l-proline (49/123)...\n", + "Calculating descriptors for phenazopyridine (50/123)...\n", + "Calculating descriptors for chlorprothixene (51/123)...\n", + "Calculating descriptors for oxytetracycline (52/123)...\n", + "Calculating descriptors for phenylbutazone (53/123)...\n", + "Calculating descriptors for trichlormethiazide (54/123)...\n", + "Calculating descriptors for micronazole (55/123)...\n", + "Calculating descriptors for thymol (56/123)...\n", + "Calculating descriptors for chlorpromazine (57/123)...\n", + "Calculating descriptors for 55-diphenylhydantoin (58/123)...\n", + "Calculating descriptors for sulindac (59/123)...\n", + "Calculating descriptors for tolmetin (60/123)...\n", + "Calculating descriptors for acetazolamide (61/123)...\n", + "Calculating descriptors for sparfloxacin (62/123)...\n", + "Calculating descriptors for diphenhydramine (63/123)...\n", + "Calculating descriptors for phthalicacid (64/123)...\n", + "Calculating descriptors for warfarin (65/123)...\n", + "Calculating descriptors for nalidixicacid (66/123)...\n", + "Calculating descriptors for naproxen (67/123)...\n", + "Calculating descriptors for hydroflumethiazide (68/123)...\n", + "Calculating descriptors for amodiaquine (69/123)...\n", + "Calculating descriptors for amiodarone (70/123)...\n", + "Calculating descriptors for chlorpheniramine (71/123)...\n", + "Calculating descriptors for lomefloxacin (72/123)...\n", + "Calculating descriptors for flufenamicacid (73/123)...\n", + "Calculating descriptors for cephalothin (74/123)...\n", + "Calculating descriptors for diclofenac (75/123)...\n", + "Calculating descriptors for famotidine (76/123)...\n", + "Calculating descriptors for 4-hydroxybenzoicacid (77/123)...\n", + "Calculating descriptors for quinine (78/123)...\n", + "Calculating descriptors for sulfamethazine (79/123)...\n", + "Calculating descriptors for diltiazem (80/123)...\n", + "Calculating descriptors for ibuprofen (81/123)...\n", + "Calculating descriptors for pindolol (82/123)...\n", + "Calculating descriptors for flumequine (83/123)...\n", + "Calculating descriptors for bromogramine (84/123)...\n", + "Calculating descriptors for atropine (85/123)...\n", + "Calculating descriptors for sarafloxacin (86/123)...\n", + "Calculating descriptors for 4-iodophenol (87/123)...\n", + "Calculating descriptors for chlorpropamide (88/123)...\n", + "Calculating descriptors for verapamil (89/123)...\n", + "Calculating descriptors for azathioprine (90/123)...\n", + "Calculating descriptors for propranolol (91/123)...\n", + "Calculating descriptors for orbifloxacin (92/123)...\n", + "Calculating descriptors for cimetidine (93/123)...\n", + "Calculating descriptors for piroxicam (94/123)...\n", + "Calculating descriptors for carvedilol (95/123)...\n", + "Calculating descriptors for tetracycline (96/123)...\n", + "Calculating descriptors for naloxone (97/123)...\n", + "Calculating descriptors for dipyridamole (98/123)...\n", + "Calculating descriptors for terfenadine (99/123)...\n", + "Calculating descriptors for pyrimethamine (100/123)...\n", + "Calculating descriptors for thiabendazole (101/123)...\n", + "Calculating descriptors for 1-napthoicacid (102/123)...\n", + "Calculating descriptors for imipramine (103/123)...\n", + "Calculating descriptors for sulfamerazine (104/123)...\n", + "Calculating descriptors for benzocaine (105/123)...\n", + "Calculating descriptors for dibucaine (106/123)...\n", + "Calculating descriptors for acebutolol (107/123)...\n", + "Calculating descriptors for hydrochlorothiazide (108/123)...\n", + "Calculating descriptors for meclofenamicacid (109/123)...\n", + "Calculating descriptors for probenecid (110/123)...\n", + "Calculating descriptors for lidocaine (111/123)...\n", + "Calculating descriptors for bendroflumethiazide (112/123)...\n", + "Calculating descriptors for trazodone (113/123)...\n", + "Calculating descriptors for indomethacin (114/123)...\n", + "Calculating descriptors for furosemide (115/123)...\n", + "Calculating descriptors for clozapine (116/123)...\n", + "Calculating descriptors for salicylicacid (117/123)...\n", + "Calculating descriptors for ketoprofen (118/123)...\n", + "Calculating descriptors for tolbutamide (119/123)...\n", + "Calculating descriptors for folicacid (120/123)...\n", + "Calculating descriptors for amoxicillin (121/123)...\n", + "Calculating descriptors for diethylstilbestrol (122/123)...\n", + "Calculating descriptors for diflunisal (123/123)...\n" + ] + } + ], + "source": [ + "#============================================================================\n", + "#COMPUTE DESCRIPTORS FOR OUR MOLECULES -- VARIOUS PROPERTIES OF THE MOLECULES WHICH MIGHT BE USEFUL IN SOLUBILITY ESTIMATION\n", + "#============================================================================\n", + "\n", + "#Compute a bunch of descriptors for our molecules. Descriptors will be stored in the compounds dictionary, by compound name.\n", + "#For example, compounds['terfenadine']['mw'] will give the 'mw' (molecular weight) of terfenadine).\n", + "#A full description of the descriptors calculated will be put in the homework writeup.\n", + "\n", + "#Loop over molecules\n", + "for molname in molnames:\n", + " print(\"Calculating descriptors for %s (%s/%s)...\" % (molname, molnames.index(molname)+1, len(molnames) )) #Print progress\n", + "\n", + " #Load the OEMol representation of our molecule from where it's stored\n", + " mol = compounds[molname]['mol']\n", + "\n", + " #Compute molecular weight and store\n", + " compounds[ molname ]['mw'] = OECalculateMolecularWeight( mol )\n", + "\n", + " #Number of atoms -- store\n", + " compounds[molname]['numatoms'] = mol.NumAtoms()\n", + "\n", + " #Number of heavy atoms\n", + " compounds[molname]['heavyatoms'] = OECount(mol, OEIsHeavy() )\n", + "\n", + " #Number of ring atoms\n", + " compounds[molname]['ringatoms'] = OECount(mol, OEAtomIsInRing() )\n", + "\n", + " #Number of halogens\n", + " compounds[molname]['halogens'] = OECount( mol, OEIsHalogen() )\n", + "\n", + " #Number of nitrogens\n", + " compounds[molname]['nitrogens'] = OECount( mol, OEIsNitrogen() )\n", + "\n", + " #Number of oxygens\n", + " compounds[molname]['oxygens'] = OECount( mol, OEIsOxygen() )\n", + "\n", + " #Number of rotatable bonds\n", + " compounds[molname]['rotatable'] = OECount( mol, OEIsRotor() )\n", + "\n", + " #Calculated logP\n", + " compounds[molname]['XlogP'] = OEGetXLogP( mol )\n", + "\n", + " #Number of aromatic rings\n", + " compounds[molname]['aromaticrings'] = OEGetAromaticRingCount( mol )\n", + "\n", + " #Calculate lots of other properties using molprop toolkit as per example in OE MolProp manual\n", + " #Handle the setup of 'filter', which computes lots of properties with the goal of filtering compounds. Here we'll not do any filtering\n", + " #and will use it solely for property calculation\n", + " filt = OEFilter()\n", + " ostr = oeosstream()\n", + " pwnd = False\n", + " filt.SetTable( ostr, pwnd)\n", + " headers = ostr.str().decode('UTF-8').split('\\t')\n", + " ostr.clear()\n", + " filt(mol)\n", + " fields = ostr.str().decode('UTF-8').split('\\t')\n", + " tmpdct = dict( zip(headers, fields) ) #Format the data we need into a dictionary for easy extraction\n", + " \n", + " #Extract polar surface area, store\n", + " compounds[molname]['PSA'] = tmpdct[ '2d PSA' ]\n", + " #Number of hbond donors\n", + " compounds[molname]['hbond-donors'] = int(tmpdct['hydrogen-bond donors'])\n", + " #Number of hbond acceptors\n", + " compounds[molname]['hbond-acceptors'] = int(tmpdct['hydrogen-bond acceptors'])\n", + " #Number of rings\n", + " compounds[molname]['rings'] = int(tmpdct['number of ring systems'])\n", + "\n", + " #Quickly estimate hydration free energy, or a value correlated with that -- from ZAP manual\n", + " #Do ZAP setup for molecule\n", + " OEAssignBondiVdWRadii(mol)\n", + " OEMMFFAtomTypes(mol)\n", + " OEMMFF94PartialCharges(mol)\n", + " zap.SetMolecule( mol )\n", + " solv = zap.CalcSolvationEnergy()\n", + " aval = area.GetArea( mol )\n", + " #Empirically estimate solvation free energy (hydration)\n", + " solvation = 0.59*solv + 0.01*aval #Convert electrostatic part to kcal/mol; use empirically determined kcal/sq angstrom value times surface area term\n", + " compounds[molname]['hydration'] = solvation\n", + " #Also store surface area\n", + " compounds[molname]['SA'] = aval" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "i_z-a_T-6dHR" + }, + "source": [ + "## Load in the reference data from Llinas et al./Hopfinger et al." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PLxF1xBH6dHS" + }, + "outputs": [], + "source": [ + "#============================================================================\n", + "# LOAD AND PREP THE ACTUAL SOLUBILITY DATA WE'LL BE USING\n", + "#============================================================================\n", + "\n", + "#Load solubility data\n", + "import pickle\n", + "file = open('scripts/solubilities.pickle', 'rb')\n", + "solubilities = pickle.load(file)\n", + "file.close()\n", + "new_solubilities = {}\n", + "#Adjust some naming to match that from file names\n", + "for name in solubilities.keys():\n", + " newname = name.replace(',','').replace(' ','')\n", + " new_solubilities[newname] = solubilities[name]\n", + "solubilities = new_solubilities\n", + " \n", + "#Build arrays of solubilities -- actually, work with logarithms of solubilities since they cover such a huge range\n", + "#Build a list of the solubilities for the molecules in the training set (knowns)\n", + "known_solubilities = [ solubilities[mol] for mol in knownnames]\n", + "#Convert to an array and take the log\n", + "known_solubilities = log(np.array( known_solubilities)) #Note conversion to log\n", + "#Build a list of the solubilities for molecules in the test set (unknowns)\n", + "predict_solubilities = [ solubilities[mol] for mol in predictnames]\n", + "#Convert to an array and take the log\n", + "predict_solubilities = log(np.array( predict_solubilities )) #Note conversion to log" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cdNp-A3j6dHS" + }, + "source": [ + "## Build a first simple model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "XVkitrjx6dHT", + "outputId": "d8fed7a2-7896-4f01-81c8-eb992cc34b59" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "XVkitrjx6dHT", - "outputId": "d8fed7a2-7896-4f01-81c8-eb992cc34b59" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Fit coefficients: -0.02, 9.14\n", - "For initial (molecular weight) model training, Kendall tau is 0.24, RMS error is 2.92, and Rsquared is 0.18. Probability of getting this Kendall tau value when in fact there is no correlation (null hypothesis): 0.00041\n", - "For initial (molecular weight) model test, Kendall tau is 0.22, RMS error is 3.17, and Rsquared is 0.18. Probability of getting this Kendall tau value when in fact there is no correlation (null hypothesis): 0.11. Percentage within 0.5 log units: 26.92\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#============================================================================\n", - "# BUILD SOME SAMPLE MODELS TO PREDICT SOLUBILITY\n", - "# You will want to read this code and make sure you get it, as your task takes off from here\n", - "#============================================================================\n", - "#SIMPLE MODEL #1: Predict solubility based on molecular weight alone\n", - "#============================================================================\n", - "\n", - "\n", - "#Build a really really simple model -- predict solubility based on molecular weight\n", - "\n", - "#To do this, start by obtaining molecular weights -- for both the knowns (training set) and unknowns (test set)\n", - "#Make a list of molecular weight for the knowns, convert to array\n", - "known_mw = [ compounds[mol]['mw'] for mol in knownnames ]\n", - "known_mw = np.array(known_mw)\n", - "#Make a list of molecular weights to predict (test set), convert to array\n", - "p_mw = [compounds[mol]['mw'] for mol in predictnames ]\n", - "p_mw = np.array(p_mw)\n", - "\n", - "#Our model will have the form (using y for logS, the log of the solubility), y = m*(mw) + b, which we rewrite (to feed into numpy) as y = A * p where A is an array consisting of [ mw, 1] and p is [m, b].\n", - "A = np.vstack( [known_mw, np.ones( len(known_mw) )] ).T #Write the array -- first our x value, then a 1 for the constant term\n", - "\n", - "#Solve for coefficients using least squares fit -- we just put the array A and the thing we want to fit (known_solubilities) into the least squares algorithm and get back the coefficients m and b\n", - "m, b = np.linalg.lstsq( A, known_solubilities)[0]\n", - "print(\"Fit coefficients: %.2f, %.2f\" % (m, b))\n", - "\n", - "#Compute the calculated y values, y = m*x + b, for the test set\n", - "fittedvals = m*known_mw + b\n", - "\n", - "#Compute some statistics for our model -- Kendall tau, RMS error, correlation coefficient\n", - "ktau, pvalue = scipy.stats.kendalltau( known_solubilities, fittedvals)\n", - "rms = tools.rmserr( known_solubilities, fittedvals)\n", - "R2 = tools.correl( known_solubilities, fittedvals)**2\n", - "print(\"For initial (molecular weight) model training, Kendall tau is %.2f, RMS error is %.2f, and Rsquared is %.2f. Probability of getting this Kendall tau value when in fact there is no correlation (null hypothesis): %.2g\" % (ktau, rms, R2, pvalue))\n", - "\n", - "#Now test its predictive power by applying it to the test set\n", - "predictvals = m*p_mw + b\n", - "ktau, pvalue = scipy.stats.kendalltau( predict_solubilities, predictvals)\n", - "rms = tools.rmserr( predict_solubilities, predictvals)\n", - "R2 = tools.correl( predict_solubilities, predictvals)**2\n", - "halflog = tools.percent_within_half( predict_solubilities, predictvals ) #Figure out percentage within 0.5 log units\n", - "print(\"For initial (molecular weight) model test, Kendall tau is %.2f, RMS error is %.2f, and Rsquared is %.2f. Probability of getting this Kendall tau value when in fact there is no correlation (null hypothesis): %.2g. Percentage within 0.5 log units: %.2f\" % (ktau, rms, R2, pvalue, halflog))\n", - "\n", - "#Now, for fun, take all of the data (training and test set) and do a plot of the actual values versus molecular weight (for test and training set separately) and then an overlay of the predicted fit\n", - "plot( known_mw, known_solubilities, 'bo' ) #Plot knowns with blue circles\n", - "plot( p_mw, predict_solubilities, 'rs' ) #Plot test set with red squares\n", - "\n", - "#Do a plot of the predicted fit\n", - "#First, figure out molecular weight range\n", - "minmw = min( known_mw.min(), p_mw.min() )\n", - "maxmw = max( known_mw.max(), p_mw.max() )\n", - "#Compute solubility estimates corresponding to the minimum and maximum\n", - "minsol = m*minmw+b\n", - "maxsol = m*maxmw+b\n", - "#Plot a line\n", - "plot( [ minmw, maxmw], [minsol, maxsol], 'k-' ) #Plot as a black line overlaid\n", - "xlabel('Molecular weight')\n", - "ylabel('logS')\n", - "\n", - "# Show figure\n", - "show()\n", - "\n", - "#Save figure\n", - "savefig('mw_model.pdf')\n", - "#Clear\n", - "figure()" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Fit coefficients: -0.02, 9.14\n", + "For initial (molecular weight) model training, Kendall tau is 0.24, RMS error is 2.92, and Rsquared is 0.18. Probability of getting this Kendall tau value when in fact there is no correlation (null hypothesis): 0.00041\n", + "For initial (molecular weight) model test, Kendall tau is 0.22, RMS error is 3.17, and Rsquared is 0.18. Probability of getting this Kendall tau value when in fact there is no correlation (null hypothesis): 0.11. Percentage within 0.5 log units: 26.92\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "id": "B0C7w3Uf6dHT" - }, - "source": [ - "## Build another simple model" + "data": { + "image/png": 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GPXKEd2mSgZ478iuxKSdXVdNPk+XffOs777B06dLOtbUS6XT333//zr6RU045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\nmZkZ8P+AF9z935JeqprzNLO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+ "text/plain": [ + "" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "kKBQREDA6dHT", - "outputId": "52cd1d8f-0772-44b1-eb1b-dc52173e0ea5" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "Hydration plus mw model:\n", - "Fit coefficients: -0.02 (mw), -0.15 (hyd), 7.93 (constant)\n", - "For mw+hydration model test, Kendall tau is 0.37, RMS error is 2.61, and Rsquared is 0.34. Probability of getting this Kendall tau value when in fact there is no correlation (null hypothesis): 1.2e-07. Percentage within 0.5 log units: 15.38\n" - ] - } - ], - "source": [ - "#============================================================================\n", - "#SIMPLE MODEL #2: Predict based on hydration free energy (ought to have something to do with solubility) plus molecular weight\n", - "#============================================================================\n", - "\n", - "\n", - "#Build another model -- this time using hydration free energy plus molecular weight (should do better on training set, not clear if it will on test set)\n", - "print(\"\\nHydration plus mw model:\")\n", - "known_hydr = [ compounds[mol]['hydration'] for mol in knownnames] #Build a list of hydration free energies for the knowns, with names listed in knownnames (that is, hydration free energies for the training set)\n", - "known_hydr = np.array(known_hydr) #Convert this to a numpy array\n", - "p_hydr = [ compounds[mol]['hydration'] for mol in predictnames] #Build list of hydration free energies for the test set\n", - "p_hydr = np.array(p_hydr) #Convert to numpy array\n", - "\n", - "#Prep for least squares fit and perform it\n", - "A = np.vstack( [known_mw, known_hydr, np.ones(len(known_mw) ) ] ).T #Write array for fit -- see more detailed discussion above in the molecular weight section\n", - "#Solve for coefficients\n", - "m, n, b = np.linalg.lstsq( A, known_solubilities)[0]\n", - "print(\"Fit coefficients: %.2f (mw), %.2f (hyd), %.2f (constant)\" % (m, n, b))\n", - "fittedvals = m*known_mw + n*known_hydr + b #Calculate the values we 'predict' based on our model for the training set\n", - "\n", - "#Computed test set results too\n", - "predictvals = m*p_mw + n*p_hydr + b\n", - "\n", - "#Do stats -- training set\n", - "#Compute kendall tau and pvalue\n", - "ktau, pvalue = scipy.stats.kendalltau( known_solubilities, fittedvals)\n", - "#RMS error\n", - "rms = tools.rmserr( known_solubilities, fittedvals)\n", - "#Correlation coefficient\n", - "R2 = tools.correl( known_solubilities, fittedvals)**2\n", - "halflog = tools.percent_within_half( predict_solubilities, predictvals ) #Figure out percentage within 0.5 log units\n", - "print(\"For mw+hydration model test, Kendall tau is %.2f, RMS error is %.2f, and Rsquared is %.2f. Probability of getting this Kendall tau value when in fact there is no correlation (null hypothesis): %.2g. Percentage within 0.5 log units: %.2f\" % (ktau, rms, R2, pvalue, halflog))" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "markdown", - "metadata": { - "id": "x6dbXpAM6dHT" - }, - "source": [ - "# Do your assignment below" + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "BTmDFmkC6dHU" - }, - "outputs": [], - "source": [ - "#============================================================================\n", - "#ADD YOUR MODELS HERE, FOLLOWING THE PATTERNS OF THE TWO SIMPLE MODELS ABOVE\n", - "#============================================================================" + "data": { + "text/plain": [ + "" ] + }, + "metadata": {}, + "output_type": "display_data" } - ], - "metadata": { - "kernelspec": { - "display_name": "Python [conda root]", - "language": "python", - "name": "conda-root-py" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.2" - }, - "toc": { - "nav_menu": {}, - "number_sections": true, - "sideBar": true, - "skip_h1_title": false, - "toc_cell": false, - "toc_position": {}, - "toc_section_display": "block", - "toc_window_display": false - }, - "colab": { - "name": "physprops_solubility.ipynb", - "provenance": [] + ], + "source": [ + "#============================================================================\n", + "# BUILD SOME SAMPLE MODELS TO PREDICT SOLUBILITY\n", + "# You will want to read this code and make sure you get it, as your task takes off from here\n", + "#============================================================================\n", + "#SIMPLE MODEL #1: Predict solubility based on molecular weight alone\n", + "#============================================================================\n", + "\n", + "\n", + "#Build a really really simple model -- predict solubility based on molecular weight\n", + "\n", + "#To do this, start by obtaining molecular weights -- for both the knowns (training set) and unknowns (test set)\n", + "#Make a list of molecular weight for the knowns, convert to array\n", + "known_mw = [ compounds[mol]['mw'] for mol in knownnames ]\n", + "known_mw = np.array(known_mw)\n", + "#Make a list of molecular weights to predict (test set), convert to array\n", + "p_mw = [compounds[mol]['mw'] for mol in predictnames ]\n", + "p_mw = np.array(p_mw)\n", + "\n", + "#Our model will have the form (using y for logS, the log of the solubility), y = m*(mw) + b, which we rewrite (to feed into numpy) as y = A * p where A is an array consisting of [ mw, 1] and p is [m, b].\n", + "A = np.vstack( [known_mw, np.ones( len(known_mw) )] ).T #Write the array -- first our x value, then a 1 for the constant term\n", + "\n", + "#Solve for coefficients using least squares fit -- we just put the array A and the thing we want to fit (known_solubilities) into the least squares algorithm and get back the coefficients m and b\n", + "m, b = np.linalg.lstsq( A, known_solubilities)[0]\n", + "print(\"Fit coefficients: %.2f, %.2f\" % (m, b))\n", + "\n", + "#Compute the calculated y values, y = m*x + b, for the test set\n", + "fittedvals = m*known_mw + b\n", + "\n", + "#Compute some statistics for our model -- Kendall tau, RMS error, correlation coefficient\n", + "ktau, pvalue = scipy.stats.kendalltau( known_solubilities, fittedvals)\n", + "rms = tools.rmserr( known_solubilities, fittedvals)\n", + "R2 = tools.correl( known_solubilities, fittedvals)**2\n", + "print(\"For initial (molecular weight) model training, Kendall tau is %.2f, RMS error is %.2f, and Rsquared is %.2f. Probability of getting this Kendall tau value when in fact there is no correlation (null hypothesis): %.2g\" % (ktau, rms, R2, pvalue))\n", + "\n", + "#Now test its predictive power by applying it to the test set\n", + "predictvals = m*p_mw + b\n", + "ktau, pvalue = scipy.stats.kendalltau( predict_solubilities, predictvals)\n", + "rms = tools.rmserr( predict_solubilities, predictvals)\n", + "R2 = tools.correl( predict_solubilities, predictvals)**2\n", + "halflog = tools.percent_within_half( predict_solubilities, predictvals ) #Figure out percentage within 0.5 log units\n", + "print(\"For initial (molecular weight) model test, Kendall tau is %.2f, RMS error is %.2f, and Rsquared is %.2f. Probability of getting this Kendall tau value when in fact there is no correlation (null hypothesis): %.2g. Percentage within 0.5 log units: %.2f\" % (ktau, rms, R2, pvalue, halflog))\n", + "\n", + "#Now, for fun, take all of the data (training and test set) and do a plot of the actual values versus molecular weight (for test and training set separately) and then an overlay of the predicted fit\n", + "plot( known_mw, known_solubilities, 'bo' ) #Plot knowns with blue circles\n", + "plot( p_mw, predict_solubilities, 'rs' ) #Plot test set with red squares\n", + "\n", + "#Do a plot of the predicted fit\n", + "#First, figure out molecular weight range\n", + "minmw = min( known_mw.min(), p_mw.min() )\n", + "maxmw = max( known_mw.max(), p_mw.max() )\n", + "#Compute solubility estimates corresponding to the minimum and maximum\n", + "minsol = m*minmw+b\n", + "maxsol = m*maxmw+b\n", + "#Plot a line\n", + "plot( [ minmw, maxmw], [minsol, maxsol], 'k-' ) #Plot as a black line overlaid\n", + "xlabel('Molecular weight')\n", + "ylabel('logS')\n", + "\n", + "# Show figure\n", + "show()\n", + "\n", + "#Save figure\n", + "savefig('mw_model.pdf')\n", + "#Clear\n", + "figure()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "B0C7w3Uf6dHT" + }, + "source": [ + "## Build another simple model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kKBQREDA6dHT", + "outputId": "52cd1d8f-0772-44b1-eb1b-dc52173e0ea5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Hydration plus mw model:\n", + "Fit coefficients: -0.02 (mw), -0.15 (hyd), 7.93 (constant)\n", + "For mw+hydration model test, Kendall tau is 0.37, RMS error is 2.61, and Rsquared is 0.34. Probability of getting this Kendall tau value when in fact there is no correlation (null hypothesis): 1.2e-07. Percentage within 0.5 log units: 15.38\n" + ] } + ], + "source": [ + "#============================================================================\n", + "#SIMPLE MODEL #2: Predict based on hydration free energy (ought to have something to do with solubility) plus molecular weight\n", + "#============================================================================\n", + "\n", + "\n", + "#Build another model -- this time using hydration free energy plus molecular weight (should do better on training set, not clear if it will on test set)\n", + "print(\"\\nHydration plus mw model:\")\n", + "known_hydr = [ compounds[mol]['hydration'] for mol in knownnames] #Build a list of hydration free energies for the knowns, with names listed in knownnames (that is, hydration free energies for the training set)\n", + "known_hydr = np.array(known_hydr) #Convert this to a numpy array\n", + "p_hydr = [ compounds[mol]['hydration'] for mol in predictnames] #Build list of hydration free energies for the test set\n", + "p_hydr = np.array(p_hydr) #Convert to numpy array\n", + "\n", + "#Prep for least squares fit and perform it\n", + "A = np.vstack( [known_mw, known_hydr, np.ones(len(known_mw) ) ] ).T #Write array for fit -- see more detailed discussion above in the molecular weight section\n", + "#Solve for coefficients\n", + "m, n, b = np.linalg.lstsq( A, known_solubilities)[0]\n", + "print(\"Fit coefficients: %.2f (mw), %.2f (hyd), %.2f (constant)\" % (m, n, b))\n", + "fittedvals = m*known_mw + n*known_hydr + b #Calculate the values we 'predict' based on our model for the training set\n", + "\n", + "#Computed test set results too\n", + "predictvals = m*p_mw + n*p_hydr + b\n", + "\n", + "#Do stats -- training set\n", + "#Compute kendall tau and pvalue\n", + "ktau, pvalue = scipy.stats.kendalltau( known_solubilities, fittedvals)\n", + "#RMS error\n", + "rms = tools.rmserr( known_solubilities, fittedvals)\n", + "#Correlation coefficient\n", + "R2 = tools.correl( known_solubilities, fittedvals)**2\n", + "halflog = tools.percent_within_half( predict_solubilities, predictvals ) #Figure out percentage within 0.5 log units\n", + "print(\"For mw+hydration model test, Kendall tau is %.2f, RMS error is %.2f, and Rsquared is %.2f. Probability of getting this Kendall tau value when in fact there is no correlation (null hypothesis): %.2g. Percentage within 0.5 log units: %.2f\" % (ktau, rms, R2, pvalue, halflog))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "x6dbXpAM6dHT" + }, + "source": [ + "# Do your assignment below" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "BTmDFmkC6dHU" + }, + "outputs": [], + "source": [ + "#============================================================================\n", + "#ADD YOUR MODELS HERE, FOLLOWING THE PATTERNS OF THE TWO SIMPLE MODELS ABOVE\n", + "#============================================================================" + ] + } + ], + "metadata": { + "colab": { + "name": "physprops_solubility.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "drugcomp", + "language": "python", + "name": "drugcomp" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "toc": { + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "toc_cell": false, + "toc_position": {}, + "toc_section_display": "block", + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/uci-pharmsci/getting-started.md b/uci-pharmsci/getting-started.md index 44a8e83..4ea2e2e 100644 --- a/uci-pharmsci/getting-started.md +++ b/uci-pharmsci/getting-started.md @@ -14,11 +14,13 @@ The one major **downside of the Google Colab approach is that doing calculations ## Working on Google Colab -For Google Colab, you will be running everything in the cloud ON Google Colab, which is free. If you plan to go this route (which we recommend unless you have a strong reason to run locally, though see caveat above about internet access) [this getting started notebook](https://github.com/MobleyLab/drug-computing/blob/master/uci-pharmsci/Getting_Started_condacolab.ipynb) can be opened in Colab and used to install/test out the requisite software. (Two getting started notebooks for Colab are provided; the `condacolab` one is probably superior, but the other is a fallback option.) +For Google Colab, you will be running everything in the cloud ON Google Colab, which is free. If you plan to go this route (which we recommend unless you have a strong reason to run locally, though see caveat above about internet access) [this getting started notebook](https://github.com/MobleyLab/drug-computing/blob/master/uci-pharmsci/Getting_Started.ipynb) can be opened in Colab and used to install/test out the requisite software. + +Typical notebooks here contain an "Open in Google Colab" button you can use, so e.g. click on the notebook on GitHub, open a preview of it, then click "Open in Google Colab". Occasionally, on some browser configurations, however, the GitHub preview will not load, so you need another mechanism to access the notebook. For this, use [NBViewer](https://nbviewer.org/). It allows you to enter a URL of any Jupyter notebook (such as one on GitHub as in this course) and it will display it to you in the browser, then you can click through to Colab. Alternatively, you can download the Jupyter notebook from GitHub and upload it Colab yourself. ### Subsequent use of Google Colab -Each time you begin using Google Colab, it's like beginning on a new computer. This means that **before every lecture or assignment, you will need to install the required software on Google Colab**, which takes just a few minutes. In other words, you will need to insert the commands from the [getting started notebook](https://github.com/MobleyLab/drug-computing/blob/master/uci-pharmsci/Getting_Started_condacolab.ipynb) at the beginning of the notebook you will be using on Colab, and run them. +Each time you begin using Google Colab, it's like beginning on a new computer. This means that **before every lecture or assignment, you will need to install the required software on Google Colab**, which takes just a few minutes. In other words, you will need to insert the commands from the [getting started notebook](https://github.com/MobleyLab/drug-computing/blob/master/uci-pharmsci/Getting_Started.ipynb) at the beginning of the notebook you will be using on Colab, and run them. Notebooks we use in class will have pointers to this "getting started" content to remind you of this. @@ -30,7 +32,7 @@ As noted, we can support Google Colab across platforms and even for novices. Loc ### Prerequisites -Before getting started, note that the below assumes at least some modest familiarity with the BASH shell and the idea of paths, file names, and basic Linux commands. +Before getting started, note that the below assumes at least some modest familiarity with the BASH/zsh shell and the idea of paths, file names, and basic Linux commands. If you do not have this familiarity, you may need to consult the [BASH cheat sheet](docs/bash_cheatsheet.jpg) and other sources of information (such as the [Linux/bash crash course](docs/linux_crashcourse.md) here) before proceeding. (MIT's [The Missing Semester of Your CS Education](https://missing.csail.mit.edu/) provides a more complete and diverse introduction in the form of a full course.) ### Setup and Installation - WINDOWS ONLY: @@ -66,118 +68,73 @@ Because of hardware differences, it is unlikely that the same installation (belo ### Setup and Installation: For everyone (except Google Colab) Here, you will need to complete several main steps in the install, each of which has its own section: -1) Install Anaconda Python and get the repository -2) (Optionally) Configure a conda environment if desired -3) Use conda to install gfortran -4) Use f2py3 to compile libraries for lectures/assignments (sometimes breaks if step (5) is done first) -5) Use conda to finish installing prerequisites +1) Install the mamba package manager via mambaforge (a lot like conda, but faster) +2) Configure a virtual environment +3) Install gfortran +4) Use f2py to compile libraries for lectures/assignments (sometimes breaks if step (5) is done first) +5) Use mamba to finish installing prerequisites 6) Install the OpenEye license -#### 1. Install Anaconda Python -Download the Anaconda Python 3 installation file or download it from the [website](https://www.anaconda.com/distribution/) or use (from the command prompt): - -(Linux/OSX) -> wget https://repo.anaconda.com/archive/Anaconda3-2021.05-MacOSX-x86_64.sh - -(You can get a related link for Windows or Linux and use a similar command.) - -Install Anaconda (this may take 15-30mins), filling in the "fill in the rest here" part with the appropriate name of the file you downloaded above (or run the interactive installer if you downloaded that): -> bash Anaconda_fillintheresthere.sh -b - -Make sure the anaconda3 path is added to your `~/.bash_profile` (often this is automatically added by the installer, but make sure it ends up there), e.g. via: ->echo 'PATH="$HOME/anaconda3/bin:$PATH"' >> ~/.bash_profile +#### 1. Install the mamba package manager -When it asks you to add Anaconda to your bash shell PATH, select **YES**. -(If you are using a different shell, you need to make a similar change to your shell's configuration.) +(This, and much of the following, generally follows the [OpenFF install guide](https://docs.openforcefield.org/en/latest/install.html) which is well maintained and has a lot of the same prerequisites). -Check that Anaconda installed properly by first running `which python`, which should show your newly installed python, e.g.: -``` -#: which python -$HOME/anaconda3/bin/python -``` -To ensure it works, run the command `python` in a new terminal. Its output should look something like: -``` -Python 3.8.8 (default, April 13 2021, 12:59:45) -[Clang 10.0.0 ] :: Anaconda, Inc. on darwin -Type "help", "copyright", "credits" or "license" for more information. ->>> -``` +Download the appropriate MambaForge/MiniForge installer [from the repository](https://github.com/conda-forge/miniforge?tab=readme-ov-file#user-content-mambaforge). If you are on OS X, use an installer ending with "MacOSX-x86_64". -Type `exit()` or ctrl-d ("control-d") to leave the python shell. +Then, from the command line (terminal), run +> bash Mambaforge-(rest of name).sh +or +> bash Miniforge-(rest of name).sh +where you fill in the rest of the name as appropriate for what you downloaded. -**Troubleshooting python** - -If `which python` just gives a blank line, then it means it cannot find any python in your `$PATH`. Ensuring that `~/.bash_profile` was modified correctly, use `grep -c anaconda3/bin ~/.bash_profile` and check that it prints a number greater than 0 (0 means not found). If you do get 0, then go back and follow the above steps. Now, try to source it (`source ~/.bash_profile`) and repeat the checks above. If it works this time, it means your terminal is not sourcing `~/.bash_profile` automatically. Some OS e.g. certain linux distributions, will source `~/.bashrc` rather than `~/.bash_profile`, since `bash_profile` is only sourced for login shells (this is a technicality not important here). A common thing to do is put everything in bashrc, and have bash_profile source it, ensuring every terminal will work the same: -``` -$: cat ~/.bash_profile -# /etc/skel/.bash_profile - -# This file is sourced by bash for login shells. The following line -# runs your .bashrc and is recommended by the bash info pages. -[[ -f ~/.bashrc ]] && . ~/.bashrc -``` -where `cat` is a program that prints the file. The last line is bash code for "if ~/.bashrc is a file, then source it" where `. ~/.bashrc` is short for `source ~/.bashrc` and `~` is short for `$HOME`. - -**Q: What if `which python` shows a python that is not from anaconda3?** -Then another python is installed on your system, and you likely did not follow the above steps regarding modifying `$PATH`. If you prefer to not have anaconda load its own python for every terminal, you can add the following to your `~/.bashrc`: - -``` -alias miniconda="source ~/miniconda3/etc/profile.d/conda.sh; conda activate base" -``` - -where `alias` is essentially a macro. The conda.sh is what is normally sourced by automatic vanilla installation, and will modify `$PATH` to give its own python first choice. This will then activate a "base" environment, which you may or may not want to use (see below in the next section). - -**Q: Anaconda versus miniconda?** -The only difference is the inclusion of GUI (graphical user interface) and a lot of prepackaged software in the full Anaconda installation given above. You may opt to install miniconda instead, which provides the exact same terminal functionality, and only installs the basics (python, conda, etc.). As a quick comparison, Anaconda is ~500MB where miniconda is ~50MB. See [this thread](https://stackoverflow.com/questions/45421163/anaconda-vs-miniconda) for more discussion). - -Go to [https://conda.io/en/latest/miniconda.html] to download miniconda. +As per the OpenFF instructions: +- When asked for an install destination, choose a directory owned by your user account (such as the default). +- When asked if you’d like to initialize MambaForge, answer “yes”. +- Once the installer is finished, close the terminal window, open a new one, and run: +> conda config --set auto_activate_base false #### Get the repository locally -Anytime after Anaconda is installed (and before step 4 involving pre-compiling the libraries), run the following on the command-prompt +Anytime after mamba is installed (and before step 4 involving pre-compiling the libraries), run the following on the command-prompt ```bash git clone git@github.com:MobleyLab/drug-computing.git cd drug-computing ``` This checks out (obtains) a copy of this repository so you can work with it and the files in it, if you like (you'll be using this to access lecture content and other materials from this class.) (If you have trouble with this, you may want to try the https version of the command, `git clone https://github.com/MobleyLab/drug-computing.git`) -#### 2. (Optionally) Configure a conda environment if desired - -Anaconda includes with it the `conda` environment/package manager, meaning that it can also install other software which you need. -Here we will use the `conda` package manager to install the software you need. +#### 2. Configure a virtual environment -**First, you need to decide whether or not to use a conda environment (`env`) for the course**: -- If you have no idea what this means, only just installed anaconda, and do not have an existing set of conda packages you use extensively, you probably do not want to use an `env` -- If you already have an extensive set of packages managed with `conda` and you want to ensure you do not break or modify your existing installation, you probably DO want to create a custom environment (`env`) for this course. +Install the desired packages into a new virtual environment named `drugcomp` using: +> mamba create -c conda-forge -n drugcomp openff-toolkit-examples -If you are do not need an `env`, just proceed straight to installation. -If you do need an `env`, [use this info](https://conda.io/docs/user-guide/tasks/manage-environments.html) to create a new Python 3.8 conda environment called `drugcomp` (e.g. `conda create -n drugcomp python=3.8`) and activate this environment (`source activate drugcomp`) before doing the installs discussed below. -Whenever you do work for the class, you will need to activate this environment. +This uses a recipe provided by OpenFF to create a new virtual environment containing most of what we will need for the course. (For those who want to peek inside the recipe, it's roughly [here](https://github.com/conda-forge/openff-toolkit-feedstock/blob/main/recipe/meta.yaml#L79-L95) and installs python, nglview, notebook, several qc packages, openmmforcefields, pdbfixer and a couple of others, along with their dependencies.) -#### 3) Use conda to install gfortran +This step may take a while, depending on the speed of your connection, as quite a bit needs to be downloaded. + +Whenever you do work for the class, you will need to activate this environment via `mamba activate drugcomp` -For some of our assignments/lectures (energy minimization, MD, MC) we will use `f2py3` to compile some fortran code for use in Python (to make some numerical operations fast), which requires a fortran compiler. +#### 3) Install gfortran -We do this step first because in some cases, we're seeing that other packages cause problems for gfortran. - -**Proceed to installation**: - -```bash -conda install -c conda-forge libgfortran --yes -``` +For some of our assignments/lectures (energy minimization, MD, MC) we will use `f2py` to compile some fortran code for use in Python (to make some numerical operations fast), which requires a fortran compiler. **If on Mac OS, you also need XCode's command-line tools**: To use this on OS X, you will need to install the XCode command-line tools via something like `xcode-select --install` from the command-line. -Without this you will get an error message relating to `limits.h` when attempting to execute f2py3. +Without this you will get an error message relating to libraries or includes (perhaps `limits.h`) when attempting to execute f2py. -A subtle problem arises if you install a compiler with conda (e.g. `gcc`) and have XCode installed as well. This can be a source of headache/confusion, so just be aware that multiple compilers will exist on your machine, and care must be taken to ensure only one is used at a time. +After installing xcode's tools, do +``mamba install gfortran`` +and accept in order to install. -#### 4) Use f2py3 to compile the fortran libraries for the course +In 2025, also ``mamba install meson``; apparently gfortran recently switched platforms so this is required. -f2py3 can turn prepared Fortran code into Python libraries; here, we use this for a few computationally intensive portions of the course. +A subtle problem arises if you install a compiler with mamba/conda (e.g. `gcc`) and have XCode installed as well. This can be a source of headache/confusion, so just be aware that multiple compilers will exist on your machine, and care must be taken to ensure only one is used at a time. -**You should pre-compile the course libraries before finishing installation** as we find that sometimes, subsequent installations in the same conda environment break gfortran. +#### 4) Use f2py to compile the fortran libraries for the course + +f2py (packed with numpy) can turn prepared Fortran code into Python libraries; here, we use this for a few computationally intensive portions of the course. + +**You should pre-compile the course libraries before finishing installation** as we find that sometimes, subsequent installations in the same mamba environment break gfortran. Using the command-line, pre-compile the relevant libraries listed here: - assignments: @@ -188,34 +145,23 @@ Using the command-line, pre-compile the relevant libraries listed here: - MC/mc_sandbox.f90 - MD/md_sandbox.f90 -To compile each, navigate to the relevant directory (which you will have checked out to your local computer from GitHub) and use the command `f2py3 -c -m mclib mclib.f90`, for example (for the MC library); the first argument is the final name of the module and the second argument is the name of the .f90 file. You would execute the above in the `MC` directory, then do a similar thing for the MD assignment/library, energy_minimization assignment/library, and the MC and MD lectures. You should end up with a total of five files, each in the appropriate directory. +To compile each, navigate to the relevant directory (which you will have checked out to your local computer from GitHub) and use the command `f2py -c -m mclib mclib.f90`, for example (for the MC library); the first argument is the final name of the module and the second argument is the name of the .f90 file. You would execute the above in the `MC` directory, then do a similar thing for the MD assignment/library, energy_minimization assignment/library, and the MC and MD lectures. You should end up with a total of five files, each in the appropriate directory. -(If you fail to pre-compile these libraries, you can fix this issue by creating a clean conda environment later, installing only gfortran, then compiling the libraries. Then you can use them back in your original conda environment.) +(If you fail to pre-compile these libraries, usually this causes no problem and you can compile them later. However, in some cases later installations cause library problems that make this fail. If you encounter problems later, you can fix this issue by creating a clean mamba environment later, installing only gfortran, then compiling the libraries. Then you can use them back in your original mamba environment.) -#### 5) Use conda to finish installing prerequisites +#### 5) Use mamba to finish installing prerequisites **Proceed to finish installation**: ```bash -conda config --add channels conda-forge --yes -conda install parmed --yes -conda install openff-toolkit pdbfixer nb_conda mpld3 scikit-learn seaborn bokeh py3dmol --yes -conda install -c openeye openeye-toolkits --yes -conda install -c anaconda requests -conda install pyemma --yes -conda install -c openeye/label/Orion oeommtools --yes -# Optional (enables 3D movies in Jupyter notebooks, but can be finicky) -conda install -c conda-forge nglview --yes +mamba install -c openeye openeye-toolkits --yes +mamba install -c conda-forge requests ``` -The above installs quite a variety of software packages and may take a reasonable chunk of time to complete, even on a fairly fast connection. - Specific notebooks/assignments used in class may have additional requirements and in general these will be mentioned at the top of the notebook; you should set aside some extra time to install before using a particular notebook. -**Also install the OpenEye oenotebook Jupyter helper**, IF it installs OK in your environment (this is handy, but nonessential): +Finally, make sure your environment is activated as a kernel for Jupyter notebooks: +`python -m ipykernel install --user --name=drugcomp` (or whatever environment name is being used above) to make sure this environment is activated in the notebook -```bash -pip install --extra-index-url https://pypi.org/simple --extra-index-url https://pypi.anaconda.org/openeye/simple/ -i https://pypi.anaconda.org/openeye/label/oenotebook/simple openeye-oenotebook -``` #### 6) Install the OpenEye license @@ -236,12 +182,11 @@ Verify the installation with: The output should look something like: ``` -oechem -info.py -Installed OEChem version: 3.1.1.1 platform: osx-clang++-x64 built: 20210708 release name: 2021.1.1 +oecheminfo.py +Installed OEChem version: 3.4.0.1 debug platform: osx-clang++-universal built: 20230910 release name: 2023.1.1 -Examples: /Users/dmobley/opt/anaconda3/envs/drugcomp/lib/python3.8/site-packages/openeye/examples -Doc Examples: /Users/dmobley/opt/anaconda3/envs/drugcomp/lib/python3.8/site-packages/openeye/docexamples +Examples: /Users/dmobley/mambaforge/envs/openff/lib/python3.11/site-packages/openeye/examples +Doc Examples: /Users/dmobley/mambaforge/envs/openff/lib/python3.11/site-packages/openeye/docexamples code| ext | description |read? |write? ----+---------------+------------------------------------------+------+------ @@ -268,9 +213,10 @@ code| ext | description |read? |write? 21 | inchikey | IUPAC InChI Key | no | yes 22 | csv | Comma Separated Values | yes | yes 23 | json | JavaScript Object Notation | yes | yes - 24 | cif | Crystallographic Information File (CIF) | yes | no + 24 | cif | Crystallographic Information File (CIF) | yes | yes 25 | oez | Zstd Compressed OEBinary | yes | yes - 26 | cif | Macromolecular CIF | yes | no + 26 | cif | Macromolecular CIF | yes | yes + 27 | cxsmiles | Chemaxon Extended SMILES | yes | yes ----+---------------+------------------------------------------+------+------ ``` @@ -281,20 +227,16 @@ mol = OEMol() ``` and you should get no errors. -If you have errors with your OpenEye installation and have verified that you have an OpenEye license file, it is in the correct place, and properly listed in your `~/.bash_profile` file, you may need to edit your `~/.bashrc` file to point to your `~/.bash_profile` file. Particularly, I have noticed that on some dual boot installations of Ubuntu in some cases this step may be necessary. You would just add a line to the end of your `~/.bashrc` file that says `source ~/.bash_profile` - - -#### One additional step - -After the above, please also use `jupyter-nbextension enable nglview --py --sys-prefix` on the command-line to enable the `nglview` extension for interactive visualization in Jupyter notebooks. +If you have errors with your OpenEye installation and have verified that you have an OpenEye license file, it is in the correct place, and properly listed in your appropriate profile file (e.g. `~/.bash_profile` or ~/.zshrc or similar). # Instructor notes Many of the notebooks are formatted as RISE notebooks which can be presented as slides using the RISE plugin. To get this to work, I have needed to: - Install as normal (above) -- `conda update notebook` (RISE needed more recent version) -- `conda install -c conda-forge rise` +- `mamba install jupyter_contrib_nbextensions` +- `mamba install rise` +- `mamba install git-lfs` for large files - `python -m ipykernel install --user --name=drugcomp` (or whatever environment is being used above) to make sure this environment is activated in the notebook - Once installed, if a notebook with RISE slides is active, use option-R to enter the slideshow. diff --git a/uci-pharmsci/lectures/3D_structure_shape/3D_Structure_Shape.ipynb b/uci-pharmsci/lectures/3D_structure_shape/3D_Structure_Shape.ipynb index 415750b..6e95147 100644 --- a/uci-pharmsci/lectures/3D_structure_shape/3D_Structure_Shape.ipynb +++ b/uci-pharmsci/lectures/3D_structure_shape/3D_Structure_Shape.ipynb @@ -81,18 +81,39 @@ } }, "source": [ - "2) **If you are running this on Google Colab, pip install dependencies**:" + "2) **If you are running this on Google Colab, install dependencies** (ideally follow the general mamba install process in the getting started notebook; if you wish to use py3dmol, below, for visualization you will need to mamba install and then `mamba install py3dmol`).\n", + "\n", + "Alternatively you can do a rapid install of the openeye toolkits alone using pip:\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": { "slideshow": { "slide_type": "skip" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Looking in indexes: https://pypi.anaconda.org/openeye/simple\n", + "Collecting openeye-toolkits\n", + " Downloading https://pypi.anaconda.org/openeye/simple/openeye-toolkits/2023.1.1/OpenEye-toolkits-2023.1.1.tar.gz (4.4 kB)\n", + " Preparing metadata (setup.py) ... \u001b[?25ldone\n", + "\u001b[?25hRequirement already satisfied: OpenEye-toolkits-python3-osx-universal==2023.1.1 in /Users/dmobley/mambaforge/envs/openff/lib/python3.11/site-packages (from openeye-toolkits) (2023.1.1)\n", + "Building wheels for collected packages: openeye-toolkits\n", + " Building wheel for openeye-toolkits (setup.py) ... \u001b[?25ldone\n", + "\u001b[?25h Created wheel for openeye-toolkits: filename=OpenEye_toolkits-2023.1.1-py3-none-any.whl size=4537 sha256=0b5823982f239065d1ac29e70a3359a6f9707a75bfaec49df00efc1547c78f64\n", + " Stored in directory: /Users/dmobley/Library/Caches/pip/wheels/4d/00/ba/84e9813f1f3e0ff6809caf8edda4bf9a1e85dd8f49ae162801\n", + "Successfully built openeye-toolkits\n", + "Installing collected packages: openeye-toolkits\n", + "Successfully installed openeye-toolkits-2023.1.1\n" + ] + } + ], "source": [ "!pip install -i https://pypi.anaconda.org/openeye/simple openeye-toolkits" ] @@ -290,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -305,12 +326,12 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "" ] }, - "execution_count": 8, + "execution_count": 2, "metadata": { "image/png": { "width": 600 @@ -352,7 +373,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -370,7 +391,7 @@ "True" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -403,7 +424,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 4, "metadata": { "slideshow": { "slide_type": "fragment" @@ -412,12 +433,12 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "" ] }, - "execution_count": 13, + "execution_count": 4, "metadata": { "image/png": { "width": 200 @@ -459,7 +480,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -477,7 +498,7 @@ "True" ] }, - "execution_count": 14, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -497,7 +518,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -512,12 +533,12 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "" ] }, - "execution_count": 15, + "execution_count": 6, "metadata": { "image/png": { "width": 200 @@ -546,7 +567,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 7, "metadata": { "id": "vOo7MfhdIfB6", "slideshow": { @@ -584,7 +605,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -602,7 +623,7 @@ "True" ] }, - "execution_count": 18, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -639,7 +660,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -655,17 +676,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "3D_Structure_Shape.ipynb mymolecule.mol2\r\n", - "test_image.pdf morphine_tramadol_mine.png\r\n", - "DepictMolName.png morphine_tramadol.png\r\n", - "DepictMolSimple.png Marvin_molecule.png\r\n", - "ref.mol2 Marvin_molecule.mol\r\n" + "test_image.pdf ref.mol2\r\n", + "DepictMolName.png tramadol.png\r\n", + "DepictMolSimple.png morphine.png\r\n", + "3D_Structure_Shape.ipynb morphine_tramadol_mine.png\r\n", + "mymolecule.mol2 morphine_tramadol.png\r\n", + "mymolecule.pdb Marvin_molecule.png\r\n", + "fitted_output.mol2 Marvin_molecule.mol\r\n" ] } ], "source": [ - "ls -t\n", - "# On colab you will need to prefix this with % to indicate it is a shell command" + "%ls -t\n", + "# prefix this with % to indicate it is a shell command" ] }, { @@ -711,7 +734,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -758,7 +781,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -845,7 +868,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 12, "metadata": { "id": "ZbQYc77GIfCB", "slideshow": { @@ -867,7 +890,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 13, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -913,7 +936,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 17, "metadata": { "id": "WmMupoUrIfCB", "slideshow": { @@ -923,11 +946,19 @@ "outputs": [], "source": [ "#First we create an output stream (just like we did for an input stream)\n", + "\n", + "ostream = oemolostream( 'mymolecule.pdb')\n", + "#Now we write our molecule\n", + "OEWriteMolecule(ostream, mol)\n", + "#And close the output stream\n", + "ostream.close()\n", + "\n", + "# Write two formats just so we have on hand\n", "ostream = oemolostream( 'mymolecule.mol2')\n", "#Now we write our molecule\n", "OEWriteMolecule(ostream, mol)\n", "#And close the output stream\n", - "ostream.close()" + "ostream.close()\n" ] }, { @@ -944,36 +975,35 @@ }, { "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 497 - }, - "id": "H0IGjxJMIfCC", - "outputId": "6fda8b3b-48bf-41f3-fcef-edca8b936298", - "slideshow": { - "slide_type": "fragment" - } - }, + "execution_count": 18, + "metadata": {}, "outputs": [ { "data": { - "application/3dmoljs_load.v0": "
\n

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n jupyter labextension install jupyterlab_3dmol

\n
\n", + "application/3dmoljs_load.v0": "
\n

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n jupyter labextension install jupyterlab_3dmol

\n
\n", "text/html": [ - "
\n", - "

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n", + "

\n", + "

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n", " jupyter labextension install jupyterlab_3dmol

\n", "
\n", "" ] @@ -1005,21 +1035,56 @@ } ], "source": [ - "# Visualize using py3dmol\n", + "# Visualize using py3dmol - for which you'd need to install it first\n", "import py3Dmol\n", "\n", "viewer = py3Dmol.view(data=open('mymolecule.mol2','r').read(), \n", " style={'stick': {}})\n", - "viewer.show()\n", - "\n", - "\n", + "viewer.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 497 + }, + "id": "H0IGjxJMIfCC", + "outputId": "6fda8b3b-48bf-41f3-fcef-edca8b936298", + "slideshow": { + "slide_type": "fragment" + } + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ac222ffd0e4940f1a93837135d6ba3bd", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "NGLWidget()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "# Can also try visualizing with nglview; nglview has more features, but is more finicky to get working\n", - "#import mdtraj, nglview\n", + "import mdtraj, nglview\n", + "\n", + "#For google colab:\n", "#from google.colab import output\n", "#output.enable_custom_widget_manager()\n", - "#traj = mdtraj.load('mymolecule.mol2')\n", - "#view = nglview.show_mdtraj(traj)\n", - "#view" + "\n", + "#Visualize\n", + "traj = mdtraj.load('mymolecule.pdb') #this will also read mol2 but sometimes behaves strangely with mol2\n", + "view = nglview.show_mdtraj(traj)\n", + "view" ] }, { @@ -1066,7 +1131,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 22, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1081,12 +1146,12 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "" ] }, - "execution_count": 26, + "execution_count": 22, "metadata": { "image/png": { "width": 800 @@ -1129,7 +1194,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1147,7 +1212,7 @@ "True" ] }, - "execution_count": 37, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } @@ -1161,7 +1226,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 24, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1179,7 +1244,7 @@ "True" ] }, - "execution_count": 38, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -1192,7 +1257,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 25, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1210,7 +1275,7 @@ "True" ] }, - "execution_count": 39, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1234,7 +1299,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 26, "metadata": { "id": "nDJrqjiaIfCE", "slideshow": { @@ -1260,7 +1325,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 27, "metadata": { "id": "qy690COYIfCE", "slideshow": { @@ -1283,7 +1348,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 28, "metadata": { "id": "nqaM7PpAIfCE", "slideshow": { @@ -1323,7 +1388,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 29, "metadata": { "id": "0Afaj78LIfCE", "slideshow": { @@ -1340,7 +1405,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 30, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1358,7 +1423,7 @@ "0" ] }, - "execution_count": 34, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1374,7 +1439,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 31, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -1419,7 +1484,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 32, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1434,21 +1499,30 @@ "outputs": [ { "data": { - "application/3dmoljs_load.v0": "
\n

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n jupyter labextension install jupyterlab_3dmol

\n
\n", + "application/3dmoljs_load.v0": "
\n

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n jupyter labextension install jupyterlab_3dmol

\n
\n", "text/html": [ - "
\n", - "

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n", + "

\n", + "

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n", " jupyter labextension install jupyterlab_3dmol

\n", "
\n", "" ] @@ -1482,6 +1556,7 @@ } ], "source": [ + "# With py3dmol\n", "import py3Dmol\n", "#First we assign the py3Dmol.view as viewer\n", "viewer = py3Dmol.view()\n", @@ -1496,7 +1571,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": { "slideshow": { "slide_type": "skip" @@ -1513,8 +1588,8 @@ "#fitted = mdtraj.load('fitted_output.mol2')\n", "#view.add_trajectory(fitted)\n", "#the two lines below may not work for you\n", - "##view[1].clear_representations() #Use this and the below to switch one to blue sticks to make it easier to see\n", - "##view[1].add_ball_and_stick(color='blue')\n", + "#view[1].clear_representations() #Use this and the below to switch one to blue sticks to make it easier to see\n", + "#view[1].add_ball_and_stick(color='blue')\n", "#view" ] }, @@ -1541,9 +1616,9 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python [conda env:drugcomp] *", + "display_name": "openff", "language": "python", - "name": "conda-env-drugcomp-py" + "name": "openff" }, "language_info": { "codemirror_mode": { @@ -1555,7 +1630,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.11.7" }, "livereveal": { "height": 900, diff --git a/uci-pharmsci/lectures/MC/MC_Sandbox.ipynb b/uci-pharmsci/lectures/MC/MC_Sandbox.ipynb index 472befa..4ce907a 100644 --- a/uci-pharmsci/lectures/MC/MC_Sandbox.ipynb +++ b/uci-pharmsci/lectures/MC/MC_Sandbox.ipynb @@ -19,7 +19,7 @@ "\n", "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MobleyLab/drug-computing/blob/master/uci-pharmsci/lectures/MC/MC_Sandbox.ipynb)\n", "\n", - "For Google Colab, pip installation of software is faster, but we've only been able to get `gfortran` working via a conda installation, so we'll need to go that route. Begin by unsetting the PYTHONPATH to prevent issues with miniconda, then installing miniconda (which will take perhaps 20 seconds to a couple of minutes):" + "For Google Colab, follow the instructions in `Getting_Started.ipynb` and propagate those install steps below:" ] }, { @@ -27,14 +27,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "%env PYTHONPATH=\n", - "! wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.10.3-Linux-x86_64.sh\n", - "! chmod +x Miniconda3-py37_4.10.3-Linux-x86_64.sh\n", - "! bash ./Miniconda3-py37_4.10.3-Linux-x86_64.sh -b -f -p /usr/local\n", - "import sys\n", - "sys.path.append('/usr/local/lib/python3.7/site-packages/')" - ] + "source": [] }, { "cell_type": "markdown", @@ -49,7 +42,7 @@ "metadata": {}, "outputs": [], "source": [ - "!conda install -c conda-forge libgfortran --yes" + "!conda install -c conda-forge gfortran --yes" ] }, { @@ -88,7 +81,7 @@ "metadata": {}, "outputs": [], "source": [ - "!f2py3 -c -m mc_sandbox mc_sandbox.f90" + "!f2py -c -m mc_sandbox mc_sandbox.f90" ] }, { @@ -97,7 +90,7 @@ "source": [ "### Preparation for local use\n", "\n", - "For local use, you need to compile the fortran module `mc_sandbox` for use as a Python library. This accelerates the numerical calculations and a similar framework will be used in the MD and MC assignments. Use `f2py3 -c -m mc_sandbox mc_sandbox.f90` to compile. (As noted above you may need to do this in advance of installing certain modules, such as the beginning of the course, or by making a clean conda environment.)" + "For local use, you need to compile the fortran module `mc_sandbox` for use as a Python library. This accelerates the numerical calculations and a similar framework will be used in the MD and MC assignments. Use `f2py -c -m mc_sandbox mc_sandbox.f90` to compile. (As noted above you may need to do this in advance of installing certain modules, such as the beginning of the course, or by making a clean conda environment.)" ] }, { @@ -145,7 +138,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "3.152\n" + "3.164\n" ] } ], @@ -213,19 +206,18 @@ "name": "stdout", "output_type": "stream", "text": [ + "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n", "Populating the interactive namespace from numpy and matplotlib\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ - "
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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -286,7 +278,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -316,7 +308,7 @@ "Cut = 2.5\n", "L = 3.0 #Let's put these in a small box so they don't lose each other\n", "max_displacement = 0.1 #Maximum move size\n", - "T = 1. #You should play with this and see how the results change. Don't make it an integer - use a floating point value (i.e. 1., not 1)\n", + "T = 2. #You should play with this and see how the results change. Don't make it an integer - use a floating point value (i.e. 1., not 1)\n", "\n", "#Choose N for number of particles; you could adjust this later.\n", "N = 2\n", @@ -346,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -371,8 +363,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "U, Unew, DeltaU: -0.3366534854145746 -0.42428826461302754 -0.08763477919845292\n", - "Acceptance probability Pacc= 1.0915893780702166\n", + "U, Unew, DeltaU: -0.3366534854145746 -0.44618372728243927 -0.10953024186786464\n", + "Acceptance probability Pacc= 1.0562924845440842\n", "Accepted\n" ] } @@ -440,7 +432,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": { "id": "KN0hOwTumiYy" }, @@ -474,22 +466,63 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "id": "92OBZmk8miYy" }, "outputs": [], "source": [ - "# Put your code here\n" + "# Put your code here\n", + "#Define a counter to count how many moves are accepted\n", + "\n", + "#Run a 'for' loop over steps (starting with step 2, since we did step 1 above, and running up to max_steps)\n", + "\n", + " #Pick a random particle to move\n", + "\n", + " #Store the old positions before updating\n", + " #Update the positions with the proposed move\n", + " #Calculate the new energy\n", + " #Evaluate the change in energy\n", + "\n", + " #Calculate the acceptance probability\n", + "\n", + " #Decide whether to accept or reject the move with if/else statements,\n", + " #reverting the position array if it's rejected\n", + " #Also update your counter to track whether or not the move was accepted\n", + " #Store the positions for the current step (whether or not they changed)\n", + "\n", + "#Print out the fraction of moves which were accepted, and the final energy if you like.\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "id": "v8bmWIH1miYz" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "##GET READY TO PLOT\n", "#Find x axis (MC steps rather than time, as it was in the MD sandbox)\n", @@ -548,7 +581,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.12.8" }, "toc": { "nav_menu": {}, diff --git a/uci-pharmsci/lectures/MC/MD_MC.key b/uci-pharmsci/lectures/MC/MD_MC.key index 33f5c57..387ed9b 100644 Binary files a/uci-pharmsci/lectures/MC/MD_MC.key and b/uci-pharmsci/lectures/MC/MD_MC.key differ diff --git a/uci-pharmsci/lectures/MD/MD.key b/uci-pharmsci/lectures/MD/MD.key index d051ddd..24e8b6e 100644 Binary files a/uci-pharmsci/lectures/MD/MD.key and b/uci-pharmsci/lectures/MD/MD.key differ diff --git a/uci-pharmsci/lectures/MD/MD_Sandbox.ipynb b/uci-pharmsci/lectures/MD/MD_Sandbox.ipynb index da71da7..501c825 100644 --- a/uci-pharmsci/lectures/MD/MD_Sandbox.ipynb +++ b/uci-pharmsci/lectures/MD/MD_Sandbox.ipynb @@ -25,9 +25,9 @@ "\n", "### Preparation for Google Colab (NOT FOR LOCAL USE)\n", "\n", - "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MobleyLab/drug-computing/blob/master/uci-pharmsci/lectures/MD/MD_sandbox.ipynb)\n", + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MobleyLab/drug-computing/blob/master/uci-pharmsci/lectures/MD/MD_Sandbox.ipynb)\n", "\n", - "For Google Colab, pip installation of software is faster, but we've only been able to get `gfortran` working via a conda installation, so we'll need to go that route. Begin by unsetting the PYTHONPATH to prevent issues with miniconda, then installing miniconda (which will take perhaps 20 seconds to a couple of minutes):" + "**For Google Colab, install mamba and gfortran as in the `Getting_Started.ipynb` in the intro course materials (insert that code below):**" ] }, { @@ -35,30 +35,7 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "%env PYTHONPATH=\n", - "! wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.10.3-Linux-x86_64.sh\n", - "! chmod +x Miniconda3-py37_4.10.3-Linux-x86_64.sh\n", - "! bash ./Miniconda3-py37_4.10.3-Linux-x86_64.sh -b -f -p /usr/local\n", - "import sys\n", - "sys.path.append('/usr/local/lib/python3.7/site-packages/')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once that is done, install `gfortran`, which will take roughly a similar amount of time:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "!conda install -c conda-forge libgfortran --yes" - ] + "source": [] }, { "cell_type": "markdown", @@ -92,11 +69,138 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[39mrunning build\u001b[0m\n", + "\u001b[39mrunning config_cc\u001b[0m\n", + "\u001b[39munifing config_cc, config, build_clib, build_ext, build commands --compiler options\u001b[0m\n", + "\u001b[39mrunning config_fc\u001b[0m\n", + "\u001b[39munifing config_fc, config, build_clib, build_ext, build commands --fcompiler options\u001b[0m\n", + "\u001b[39mrunning build_src\u001b[0m\n", + "\u001b[39mbuild_src\u001b[0m\n", + "\u001b[39mbuilding extension \"md_sandbox\" sources\u001b[0m\n", + "\u001b[39mf2py options: []\u001b[0m\n", + "\u001b[39mf2py:> /var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/src.macosx-10.9-x86_64-3.8/md_sandboxmodule.c\u001b[0m\n", + "\u001b[39mcreating /var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/src.macosx-10.9-x86_64-3.8\u001b[0m\n", + "Reading fortran codes...\n", + "\tReading file 'md_sandbox.f90' (format:free)\n", + "Post-processing...\n", + "\tBlock: md_sandbox\n", + "\t\t\tBlock: calcenergy\n", + "\t\t\tBlock: calcenergyforces\n", + "\t\t\tBlock: vvintegrate\n", + "Post-processing (stage 2)...\n", + "Building modules...\n", + "\tBuilding module \"md_sandbox\"...\n", + "\t\tConstructing wrapper function \"calcenergy\"...\n", + "\t\t penergy = calcenergy(pos,m,l,rc,[dim,natom])\n", + "\t\tConstructing wrapper function \"calcenergyforces\"...\n", + "\t\t penergy,forces = calcenergyforces(pos,m,l,rc,forces,[dim,natom])\n", + "\t\tConstructing wrapper function \"vvintegrate\"...\n", + "\t\t pos,vel,accel,kenergy,penergy = vvintegrate(pos,vel,accel,m,l,rc,dt,[dim,natom])\n", + "\tWrote C/API module \"md_sandbox\" to file \"/var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/src.macosx-10.9-x86_64-3.8/md_sandboxmodule.c\"\n", + "\u001b[39m adding '/var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/src.macosx-10.9-x86_64-3.8/fortranobject.c' to sources.\u001b[0m\n", + "\u001b[39m adding '/var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/src.macosx-10.9-x86_64-3.8' to include_dirs.\u001b[0m\n", + "\u001b[39mcopying /Users/dmobley/opt/anaconda3/envs/drugcomp/lib/python3.8/site-packages/numpy/f2py/src/fortranobject.c -> /var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/src.macosx-10.9-x86_64-3.8\u001b[0m\n", + "\u001b[39mcopying /Users/dmobley/opt/anaconda3/envs/drugcomp/lib/python3.8/site-packages/numpy/f2py/src/fortranobject.h -> /var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/src.macosx-10.9-x86_64-3.8\u001b[0m\n", + "\u001b[39mbuild_src: building npy-pkg config files\u001b[0m\n", + "\u001b[39mrunning build_ext\u001b[0m\n", + "\u001b[39mcustomize UnixCCompiler\u001b[0m\n", + "\u001b[39mcustomize UnixCCompiler using build_ext\u001b[0m\n", + "\u001b[39mget_default_fcompiler: matching types: '['gnu95', 'nag', 'absoft', 'ibm', 'intel', 'gnu', 'g95', 'pg']'\u001b[0m\n", + "\u001b[39mcustomize Gnu95FCompiler\u001b[0m\n", + "\u001b[39mFound executable /usr/local/bin/gfortran\u001b[0m\n", + "\u001b[39mcustomize Gnu95FCompiler\u001b[0m\n", + "\u001b[39mcustomize Gnu95FCompiler using build_ext\u001b[0m\n", + "\u001b[39mbuilding 'md_sandbox' extension\u001b[0m\n", + "\u001b[39mcompiling C sources\u001b[0m\n", + "\u001b[39mC compiler: gcc -Wno-unused-result -Wsign-compare -Wunreachable-code -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -I/Users/dmobley/opt/anaconda3/envs/drugcomp/include -arch x86_64 -I/Users/dmobley/opt/anaconda3/envs/drugcomp/include -arch x86_64\n", + "\u001b[0m\n", + "\u001b[39mcreating /var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/var\u001b[0m\n", + "\u001b[39mcreating /var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/var/folders\u001b[0m\n", + "\u001b[39mcreating /var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/var/folders/7z\u001b[0m\n", + "\u001b[39mcreating /var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn\u001b[0m\n", + "\u001b[39mcreating /var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T\u001b[0m\n", + "\u001b[39mcreating /var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3\u001b[0m\n", + "\u001b[39mcreating /var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/src.macosx-10.9-x86_64-3.8\u001b[0m\n", + "\u001b[39mcompile options: '-DNPY_DISABLE_OPTIMIZATION=1 -I/var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/src.macosx-10.9-x86_64-3.8 -I/Users/dmobley/opt/anaconda3/envs/drugcomp/lib/python3.8/site-packages/numpy/core/include -I/Users/dmobley/opt/anaconda3/envs/drugcomp/include/python3.8 -c'\u001b[0m\n", + "\u001b[39mgcc: /var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/src.macosx-10.9-x86_64-3.8/md_sandboxmodule.c\u001b[0m\n", + "\u001b[39mgcc: /var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/src.macosx-10.9-x86_64-3.8/fortranobject.c\u001b[0m\n", + "In file included from \u001b[01m\u001b[K/Users/dmobley/opt/anaconda3/envs/drugcomp/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1969\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K/Users/dmobley/opt/anaconda3/envs/drugcomp/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K/Users/dmobley/opt/anaconda3/envs/drugcomp/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:4\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K/var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/src.macosx-10.9-x86_64-3.8/fortranobject.h:13\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K/var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/src.macosx-10.9-x86_64-3.8/md_sandboxmodule.c:16\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/Users/dmobley/opt/anaconda3/envs/drugcomp/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K#warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wcpp\u0007-Wcpp\u001b]8;;\u0007\u001b[m\u001b[K]\n", + " 17 | #\u001b[01;35m\u001b[Kwarning\u001b[m\u001b[K \"Using deprecated NumPy API, disable it with \" \\\n", + " | \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n", + "In file included from \u001b[01m\u001b[K/Users/dmobley/opt/anaconda3/envs/drugcomp/lib/python3.8/site-packages/numpy/core/include/numpy/ndarraytypes.h:1969\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K/Users/dmobley/opt/anaconda3/envs/drugcomp/lib/python3.8/site-packages/numpy/core/include/numpy/ndarrayobject.h:12\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K/Users/dmobley/opt/anaconda3/envs/drugcomp/lib/python3.8/site-packages/numpy/core/include/numpy/arrayobject.h:4\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K/var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/src.macosx-10.9-x86_64-3.8/fortranobject.h:13\u001b[m\u001b[K,\n", + " from \u001b[01m\u001b[K/var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/src.macosx-10.9-x86_64-3.8/fortranobject.c:2\u001b[m\u001b[K:\n", + "\u001b[01m\u001b[K/Users/dmobley/opt/anaconda3/envs/drugcomp/lib/python3.8/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h:17:2:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K#warning \"Using deprecated NumPy API, disable it with \" \"#define NPY_NO_DEPRECATED_API NPY_1_7_API_VERSION\" [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wcpp\u0007-Wcpp\u001b]8;;\u0007\u001b[m\u001b[K]\n", + " 17 | #\u001b[01;35m\u001b[Kwarning\u001b[m\u001b[K \"Using deprecated NumPy API, disable it with \" \\\n", + " | \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n", + "\u001b[01m\u001b[K/var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/src.macosx-10.9-x86_64-3.8/md_sandboxmodule.c:109:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K'\u001b[01m\u001b[Kf2py_size\u001b[m\u001b[K' defined but not used [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-function\u0007-Wunused-function\u001b]8;;\u0007\u001b[m\u001b[K]\n", + " 109 | static int \u001b[01;35m\u001b[Kf2py_size\u001b[m\u001b[K(PyArrayObject* var, ...)\n", + " | \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n", + "\u001b[39mcompiling Fortran sources\u001b[0m\n", + "\u001b[39mFortran f77 compiler: /usr/local/bin/gfortran -Wall -g -ffixed-form -fno-second-underscore -arch x86_64 -fPIC -O3 -funroll-loops\n", + "Fortran f90 compiler: /usr/local/bin/gfortran -Wall -g -fno-second-underscore -arch x86_64 -fPIC -O3 -funroll-loops\n", + "Fortran fix compiler: /usr/local/bin/gfortran -Wall -g -ffixed-form -fno-second-underscore -Wall -g -fno-second-underscore -arch x86_64 -fPIC -O3 -funroll-loops\u001b[0m\n", + "\u001b[39mcompile options: '-I/var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/src.macosx-10.9-x86_64-3.8 -I/Users/dmobley/opt/anaconda3/envs/drugcomp/lib/python3.8/site-packages/numpy/core/include -I/Users/dmobley/opt/anaconda3/envs/drugcomp/include/python3.8 -c'\u001b[0m\n", + "\u001b[39mgfortran:f90: md_sandbox.f90\u001b[0m\n", + "\u001b[01m\u001b[Kmd_sandbox.f90:59:28:\u001b[m\u001b[K\n", + "\n", + " 59 | real(8) :: d2, sep, disp, shiftval, rc2, id6, id2, id12\n", + " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", + "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Unused variable '\u001b[01m\u001b[Kdisp\u001b[m\u001b[K' declared at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[Kmd_sandbox.f90:49:34:\u001b[m\u001b[K\n", + "\n", + " 49 | subroutine CalcEnergyForces(Pos, M, L, rc, PEnergy, Forces, Dim, NAtom)\n", + " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", + "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Unused dummy argument '\u001b[01m\u001b[Km\u001b[m\u001b[K' at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gfortran/Error-and-Warning-Options.html#index-Wunused-dummy-argument\u0007-Wunused-dummy-argument\u001b]8;;\u0007\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[Kmd_sandbox.f90:59:22:\u001b[m\u001b[K\n", + "\n", + " 59 | real(8) :: d2, sep, disp, shiftval, rc2, id6, id2, id12\n", + " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", + "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Unused variable '\u001b[01m\u001b[Ksep\u001b[m\u001b[K' declared at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[Kmd_sandbox.f90:10:28:\u001b[m\u001b[K\n", + "\n", + " 10 | real(8) :: d2, sep, disp, shiftval, rc2, id6, id2, id12\n", + " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", + "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Unused variable '\u001b[01m\u001b[Kdisp\u001b[m\u001b[K' declared at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[Kmd_sandbox.f90:2:28:\u001b[m\u001b[K\n", + "\n", + " 2 | subroutine CalcEnergy(Pos, M, L, rc, PEnergy, Dim, NAtom)\n", + " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", + "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Unused dummy argument '\u001b[01m\u001b[Km\u001b[m\u001b[K' at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gfortran/Error-and-Warning-Options.html#index-Wunused-dummy-argument\u0007-Wunused-dummy-argument\u001b]8;;\u0007\u001b[m\u001b[K]\n", + "\u001b[01m\u001b[Kmd_sandbox.f90:10:22:\u001b[m\u001b[K\n", + "\n", + " 10 | real(8) :: d2, sep, disp, shiftval, rc2, id6, id2, id12\n", + " | \u001b[01;35m\u001b[K1\u001b[m\u001b[K\n", + "\u001b[01;35m\u001b[KWarning:\u001b[m\u001b[K Unused variable '\u001b[01m\u001b[Ksep\u001b[m\u001b[K' declared at \u001b[01;35m\u001b[K(1)\u001b[m\u001b[K [\u001b[01;35m\u001b[K\u001b]8;;https://gcc.gnu.org/onlinedocs/gcc/Warning-Options.html#index-Wunused-variable\u0007-Wunused-variable\u001b]8;;\u0007\u001b[m\u001b[K]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[39m/usr/local/bin/gfortran -Wall -g -arch x86_64 -Wall -g -undefined dynamic_lookup -bundle /var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/src.macosx-10.9-x86_64-3.8/md_sandboxmodule.o /var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/src.macosx-10.9-x86_64-3.8/fortranobject.o /var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3/md_sandbox.o -L/usr/local/lib/gcc/x86_64-apple-darwin19/11.2.0 -L/usr/local/lib/gcc/x86_64-apple-darwin19/11.2.0/../../.. -L/usr/local/lib/gcc/x86_64-apple-darwin19/11.2.0/../../.. -lgfortran -o ./md_sandbox.cpython-38-darwin.so\u001b[0m\n", + "ld: warning: dylib (/usr/local/lib/libgfortran.dylib) was built for newer macOS version (11.0) than being linked (10.9)\n", + "ld: warning: dylib (/usr/local/lib/libquadmath.dylib) was built for newer macOS version (11.0) than being linked (10.9)\n", + "Removing build directory /var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/tmpm7bkjeb3\n" + ] + } + ], "source": [ - "!f2py3 -c -m md_sandbox md_sandbox.f90" + "!f2py -c -m md_sandbox md_sandbox.f90" ] }, { @@ -167,47 +271,7 @@ "id": "buySWM8J-E6E", "outputId": "1bcae8b1-f5b5-4ff6-cad2-b5b15a56c241" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":10: RuntimeWarning: divide by zero encountered in power\n", - " U = 4.*(r**(-12.) - r**(-6.))\n", - ":10: RuntimeWarning: invalid value encountered in subtract\n", - " U = 4.*(r**(-12.) - r**(-6.))\n" - ] - }, - { - "data": { - "text/plain": [ - "(-2.0, 2.0)" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#Get pylab ready for plotting in this notebook - \"magic\" command specific for iPython notebooks\n", "%pylab inline\n", @@ -231,7 +295,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -292,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 3, "metadata": { "executionInfo": { "elapsed": 170, @@ -331,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -351,19 +415,7 @@ "id": "Mmgv7Bvo-E6N", "outputId": "5d387627-4860-4bb1-ab82-3527e2dc2d82" }, - "outputs": [ - { - "ename": "NameError", - "evalue": "ignored", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;31m#Define storage for positions at each time so we can track them\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mmax_steps\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5000\u001b[0m \u001b[0;31m#Maximum number of steps we will take - so we know how many positions we might store\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mPos_t\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzeros\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0mN\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mmax_steps\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0;31m#Store initial positions\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'N' is not defined" - ] - } - ], + "outputs": [], "source": [ "#Define storage for positions at each time so we can track them\n", "max_steps = 5000 #Maximum number of steps we will take - so we know how many positions we might store\n", @@ -404,7 +456,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "executionInfo": { "elapsed": 18, @@ -422,10 +474,8 @@ "outputs": [], "source": [ "for i in range(max_steps):\n", - " #Your code goes here\n", - " energy, Forces = calcenergyforces( Pos, M, L, Cut, Forces )\n", - " Pos, Vel, Accel, KEnergy, PEnergy = vvintegrate( Pos, Vel, Forces, M, L, Cut, dt )\n", - " Pos_t[:,:,i] = Pos\n" + " #Your code goes here\n", + " \n" ] }, { @@ -439,7 +489,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": { "executionInfo": { "elapsed": 20, @@ -454,7 +504,28 @@ }, "id": "OXwBGy-C-E6P" }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "#Find x axis (time values)\n", "t = dt*np.arange(0,max_steps)\n", @@ -498,9 +569,9 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "openff", "language": "python", - "name": "python3" + "name": "openff" }, "language_info": { "codemirror_mode": { @@ -512,7 +583,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.11.7" }, "toc": { "nav_menu": {}, diff --git a/uci-pharmsci/lectures/MD/MD_Sandbox_Molecule.ipynb b/uci-pharmsci/lectures/MD/MD_Sandbox_Molecule.ipynb index cdb680c..4333255 100644 --- a/uci-pharmsci/lectures/MD/MD_Sandbox_Molecule.ipynb +++ b/uci-pharmsci/lectures/MD/MD_Sandbox_Molecule.ipynb @@ -1,2585 +1,2551 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "OqKzYKT1-E5_" - }, - "source": [ - "# Sandbox/demo to go along with the MD assignment and lectures\n", - "\n", - "## PharmSci 175/275\n", - "Author: David Mobley\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "wFteMA87orKJ" - }, - "source": [ - "## Preparation \n", - "\n", - "To use this notebook you will either need to run it locally using Jupyter or on Google Colab. To run this notebook you will need the OpenEye toolkits installed, as well as the OpenFF toolkits (`openff-toolkit`), `openmm`, and other libraries. If you installed these in a local environment during prep for the class, you should already be prepared; if you are using Google Colab, you will need to install them here. \n", - "\n", - "### Preparation for Google Colab (NOT FOR LOCAL USE)\n", - "\n", - "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MobleyLab/drug-computing/blob/master/uci-pharmsci/lectures/MD/MD_Sandbox_Molecule.ipynb)\n", - "\n", - "For Google Colab, we'll use `conda` for installation because it will work better with packaging for OpenFF and OpenMM. Begin by unsetting the PYTHONPATH to prevent issues with miniconda, then installing miniconda (which will take perhaps 20 seconds to a couple of minutes):" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "m1ZRbIDeorKK", - "outputId": "9f0ae4ad-c316-4663-c59b-4b9b85a18a83" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "⏬ Downloading https://github.com/jaimergp/miniforge/releases/latest/download/Mambaforge-colab-Linux-x86_64.sh...\n", - "📦 Installing...\n", - "📌 Adjusting configuration...\n", - "🩹 Patching environment...\n", - "⏲ Done in 0:00:28\n", - "🔁 Restarting kernel...\n" - ] - } - ], - "source": [ - "!pip install -q condacolab\n", - "import condacolab\n", - "condacolab.install()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "C4j51ocaorKL" - }, - "source": [ - "**WAIT FOR THAT TO FINISH BEFORE RUNNING ANY OTHER CELLS**; it actually needs to get to the point where it finishes AND RESTARTS YOUR KERNEL before you can continue running additional cells.\n", - "\n", - "Once that is done, install the OpenEye toolkits:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vusj-hEhorKL", - "outputId": "f9b3f9f7-0d48-4fce-99de-a91b93e6ea64" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - " __ __ __ __\n", - " / \\ / \\ / \\ / \\\n", - " / \\/ \\/ \\/ \\\n", - "███████████████/ /██/ /██/ /██/ /████████████████████████\n", - " / / \\ / \\ / \\ / \\ \\____\n", - " / / \\_/ \\_/ \\_/ \\ o \\__,\n", - " / _/ \\_____/ `\n", - " |/\n", - " ███╗ ███╗ █████╗ ███╗ ███╗██████╗ █████╗\n", - " ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗\n", - " ██╔████╔██║███████║██╔████╔██║██████╔╝███████║\n", - " ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║\n", - " ██║ ╚═╝ ██║██║ ██║██║ ╚═╝ ██║██████╔╝██║ ██║\n", - " ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚═════╝ ╚═╝ ╚═╝\n", - "\n", - " mamba (0.8.0) supported by @QuantStack\n", - "\n", - " GitHub: https://github.com/mamba-org/mamba\n", - " Twitter: https://twitter.com/QuantStack\n", - "\n", - "█████████████████████████████████████████████████████████████\n", - "\n", - "\n", - "Looking for: ['openeye-toolkits']\n", - "\n", - "conda-forge/linux-64 [] (00m:00s) \n", - "conda-forge/linux-64 [] (00m:00s) 219 KB / ?? 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(4.17 MB/s)\n", - "conda-forge/linux-64 [] (00m:04s) Finalizing...\n", - "conda-forge/linux-64 [] (00m:04s) Done\n", - "conda-forge/linux-64 [] (00m:04s) Done\n", - "conda-forge/linux-64 [] (00m:04s) Done\n", - "Transaction\n", - "\n", - " Prefix: /usr/local\n", - "\n", - " Updating specs:\n", - "\n", - " - openeye-toolkits\n", - "\n", - "\n", - " Package Version Build Channel Size\n", - "──────────────────────────────────────────────────────────────────────────────────\n", - " Install:\n", - "──────────────────────────────────────────────────────────────────────────────────\n", - "\n", - "\u001b[32m attrs \u001b[00m 21.4.0 pyhd8ed1ab_0 conda-forge/noarch 49 KB\n", - "\u001b[32m importlib-metadata\u001b[00m 4.10.0 py37h89c1867_0 conda-forge/linux-64 32 KB\n", - "\u001b[32m importlib_metadata\u001b[00m 4.10.0 hd8ed1ab_0 conda-forge/noarch 4 KB\n", - "\u001b[32m iniconfig \u001b[00m 1.1.1 pyh9f0ad1d_0 conda-forge/noarch 8 KB\n", - "\u001b[32m openeye-toolkits \u001b[00m 2021.2.0 py37_0 openeye/linux-64 80 MB\n", - "\u001b[32m packaging \u001b[00m 21.3 pyhd8ed1ab_0 conda-forge/noarch 36 KB\n", - "\u001b[32m pluggy \u001b[00m 1.0.0 py37h89c1867_2 conda-forge/linux-64 25 KB\n", - "\u001b[32m py \u001b[00m 1.11.0 pyh6c4a22f_0 conda-forge/noarch 74 KB\n", - "\u001b[32m pyparsing \u001b[00m 3.0.6 pyhd8ed1ab_0 conda-forge/noarch 79 KB\n", - "\u001b[32m pytest \u001b[00m 6.2.5 py37h89c1867_2 conda-forge/linux-64 432 KB\n", - "\u001b[32m toml \u001b[00m 0.10.2 pyhd8ed1ab_0 conda-forge/noarch 18 KB\n", - "\u001b[32m typing_extensions \u001b[00m 4.0.1 pyha770c72_0 conda-forge/noarch 26 KB\n", - "\u001b[32m zipp \u001b[00m 3.7.0 pyhd8ed1ab_0 conda-forge/noarch 12 KB\n", - "\n", - " Upgrade:\n", - "──────────────────────────────────────────────────────────────────────────────────\n", - "\n", - "\u001b[31m ca-certificates \u001b[00m 2020.12.5 ha878542_0 installed \n", - "\u001b[32m ca-certificates \u001b[00m 2021.10.8 ha878542_0 conda-forge/linux-64 139 KB\n", - "\u001b[31m certifi \u001b[00m 2020.12.5 py37h89c1867_1 installed \n", - "\u001b[32m certifi \u001b[00m 2021.10.8 py37h89c1867_1 conda-forge/linux-64 145 KB\n", - "\u001b[31m libgcc-ng \u001b[00m 9.3.0 h2828fa1_18 installed \n", - "\u001b[32m libgcc-ng \u001b[00m 11.2.0 h1d223b6_11 conda-forge/linux-64 887 KB\n", - "\u001b[31m libgomp \u001b[00m 9.3.0 h2828fa1_18 installed \n", - "\u001b[32m libgomp \u001b[00m 11.2.0 h1d223b6_11 conda-forge/linux-64 427 KB\n", - "\u001b[31m openssl \u001b[00m 1.1.1j h7f98852_0 installed \n", - "\u001b[32m openssl \u001b[00m 1.1.1l h7f98852_0 conda-forge/linux-64 2 MB\n", - "\n", - " Summary:\n", - "\n", - " Install: 13 packages\n", - " Upgrade: 5 packages\n", - "\n", - " Total download: 84 MB\n", - "\n", - "──────────────────────────────────────────────────────────────────────────────────\n", - "\n", - "ca-certificates [] (00m:00s) \n", - "ca-certificates [] (00m:00s) 115 KB / 139 KB (768.94 KB/s)\n", - "ca-certificates [] (00m:00s) 115 KB / 139 KB (768.94 KB/s)\n", - "certifi [] (00m:00s) \n", - "ca-certificates [] (00m:00s) 115 KB / 139 KB (768.94 KB/s)\n", - "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", - "ca-certificates [] (00m:00s) 115 KB / 139 KB (768.94 KB/s)\n", - "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", - "libgcc-ng [] (00m:00s) \n", - "ca-certificates [] (00m:00s) 115 KB / 139 KB (768.94 KB/s)\n", - "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", - "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", - "ca-certificates [] (00m:00s) 115 KB / 139 KB (768.94 KB/s)\n", - "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", - "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", - "libgomp [] (00m:00s) \n", - "ca-certificates [] (00m:00s) 115 KB / 139 KB (768.94 KB/s)\n", - "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", - "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", - "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", - "ca-certificates [] (00m:00s) 115 KB / 139 KB (768.94 KB/s)\n", - "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", - "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", - "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", - "openssl [] (00m:00s) \n", - "ca-certificates [] (00m:00s) 115 KB / 139 KB (768.94 KB/s)\n", - "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", - "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", - "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", - "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", - "ca-certificates [] (00m:00s) 115 KB / 139 KB (768.94 KB/s)\n", - "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", - "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", - "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", - "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", - "ca-certificates [] (00m:00s) Validating...\n", - "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", - "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", - "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", - "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", - "ca-certificates [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", - "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", - "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", - "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", - "ca-certificates [] (00m:00s) Decompressing...\n", - "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", - "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", - "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", - "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", - "ca-certificates [] (00m:00s) Decompressing...\n", - "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", - "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", - "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", - "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", - "ca-certificates [] (00m:00s) Decompressing...\n", - "certifi [] (00m:00s) Validating...\n", - "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", - "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", - "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", - "ca-certificates [] (00m:00s) Decompressing...\n", - "certifi [] (00m:00s) Waiting...\n", - "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", - "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", - "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", - "\u001b[5A\u001b[0KFinished ca-certificates (00m:00s) 139 KB 909 KB/s\n", - "certifi [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", - "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", - "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", - "certifi [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", - "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", - "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", - "certifi [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", - "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", - "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", - "certifi [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", - "libgomp [] (00m:00s) Validating...\n", - "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", - "certifi [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", - "libgomp [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", - "certifi [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", - "libgomp [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", - "packaging [] (00m:00s) \n", - "certifi [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", - "libgomp [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", - "packaging [] (00m:00s) Validating...\n", - "certifi [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", - "libgomp [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", - "packaging [] (00m:00s) Validating...\n", - "certifi [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Validating...\n", - "libgomp [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", - "packaging [] (00m:00s) Validating...\n", - "certifi [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Validating...\n", - "libgomp [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", - "packaging [] (00m:00s) Waiting...\n", - "certifi [] (00m:00s) Decompressing...\n", - "libgcc-ng [] (00m:00s) Validating...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", - "packaging [] (00m:00s) Waiting...\n", - "\u001b[5A\u001b[0KFinished certifi (00m:00s) 145 KB 857 KB/s\n", - "libgcc-ng [] (00m:00s) Validating...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", - "packaging [] (00m:00s) Waiting...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", - "packaging [] (00m:00s) Waiting...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", - "packaging [] (00m:00s) Waiting...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Validating...\n", - "packaging [] (00m:00s) Waiting...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Validating...\n", - "packaging [] (00m:00s) Waiting...\n", - "py [] (00m:00s) \n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Validating...\n", - "packaging [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Validating...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Validating...\n", - "packaging [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "packaging [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "packaging [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) \n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "packaging [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Validating...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "packaging [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "packaging [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) \n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "packaging [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Validating...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "packaging [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "packaging [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) \n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "packaging [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Validating...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "packaging [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "packaging [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) \n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "packaging [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Validating...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "packaging [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "packaging [] (00m:00s) Decompressing...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "packaging [] (00m:00s) Decompressing...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) \n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "packaging [] (00m:00s) Decompressing...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Validating...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "packaging [] (00m:00s) Decompressing...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Validating...\n", - "zipp [] (00m:00s) \n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "packaging [] (00m:00s) Decompressing...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Validating...\n", - "zipp [] (00m:00s) Validating...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "libgomp [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "packaging [] (00m:00s) Decompressing...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Validating...\n", - "\u001b[11A\u001b[0KFinished libgomp (00m:00s) 427 KB 2 MB/s\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "packaging [] (00m:00s) Decompressing...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Validating...\n", - "libgcc-ng [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Waiting...\n", - "packaging [] (00m:00s) Decompressing...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "packaging [] (00m:00s) Decompressing...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "\u001b[10A\u001b[0KFinished packaging (00m:00s) 36 KB 187 KB/s\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) \n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) Validating...\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) Waiting...\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) Waiting...\n", - "toml [] (00m:00s) \n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) Waiting...\n", - "toml [] (00m:00s) Validating...\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) Waiting...\n", - "toml [] (00m:00s) Waiting...\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - 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"toml [] (00m:00s) Waiting...\n", - "pluggy [] (00m:00s) Waiting...\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) Waiting...\n", - "toml [] (00m:00s) Waiting...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) \n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) Waiting...\n", - "toml [] (00m:00s) Waiting...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) Validating...\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) Waiting...\n", - "toml [] (00m:00s) Waiting...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "libgcc-ng [] (00m:00s) Decompressing...\n", - "openssl [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Decompressing...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) Waiting...\n", - "toml [] (00m:00s) Waiting...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "\u001b[13A\u001b[0KFinished libgcc-ng (00m:00s) 887 KB 5 MB/s\n", - "openssl [] (00m:00s) Waiting...\n", - "py [] (00m:00s) Decompressing...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) Waiting...\n", - "toml [] (00m:00s) Waiting...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "\u001b[12A\u001b[0KFinished py (00m:00s) 74 KB 354 KB/s\n", - "openssl [] (00m:00s) Decompressing...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) Waiting...\n", - "toml [] (00m:00s) Waiting...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Decompressing...\n", - "attrs [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) Waiting...\n", - "toml [] (00m:00s) Waiting...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "openssl [] (00m:00s) Decompressing...\n", - "attrs [] (00m:00s) Decompressing...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) Waiting...\n", - "toml [] (00m:00s) Waiting...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "\u001b[11A\u001b[0KFinished openssl (00m:00s) 2 MB 10 MB/s\n", - "attrs [] (00m:00s) Decompressing...\n", - "typing_extensions [] (00m:00s) Waiting...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) Waiting...\n", - "toml [] (00m:00s) Waiting...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "attrs [] (00m:00s) Decompressing...\n", - "typing_extensions [] (00m:00s) Decompressing...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) Waiting...\n", - "toml [] (00m:00s) Waiting...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "\u001b[10A\u001b[0KFinished attrs (00m:00s) 49 KB 210 KB/s\n", - "typing_extensions [] (00m:00s) Decompressing...\n", - "importlib_metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) Waiting...\n", - "toml [] (00m:00s) Waiting...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Decompressing...\n", - "importlib_metadata [] (00m:00s) Decompressing...\n", - "pytest [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) Waiting...\n", - "toml [] (00m:00s) Waiting...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "\u001b[9A\u001b[0KFinished importlib_metadata (00m:00s) 4 KB 15 KB/s\n", - "typing_extensions [] (00m:00s) Decompressing...\n", - "pytest [] (00m:00s) Decompressing...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) Waiting...\n", - "toml [] (00m:00s) Waiting...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "typing_extensions [] (00m:00s) Decompressing...\n", - "pytest [] (00m:00s) Decompressing...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) Waiting...\n", - "toml [] (00m:00s) Waiting...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "\u001b[8A\u001b[0KFinished typing_extensions (00m:00s) 26 KB 111 KB/s\n", - "pytest [] (00m:00s) Decompressing...\n", - "pyparsing [] (00m:00s) Waiting...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) Waiting...\n", - "toml [] (00m:00s) Waiting...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "pytest [] (00m:00s) Decompressing...\n", - "pyparsing [] (00m:00s) Decompressing...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) Waiting...\n", - "toml [] (00m:00s) Waiting...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "\u001b[7A\u001b[0KFinished pytest (00m:00s) 432 KB 2 MB/s\n", - "pyparsing [] (00m:00s) Decompressing...\n", - "zipp [] (00m:00s) Waiting...\n", - "iniconfig [] (00m:00s) Waiting...\n", - "toml [] (00m:00s) Waiting...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Decompressing...\n", - "zipp [] (00m:00s) Decompressing...\n", - "iniconfig [] (00m:00s) Waiting...\n", - "toml [] (00m:00s) Waiting...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "\u001b[6A\u001b[0KFinished zipp (00m:00s) 12 KB 43 KB/s\n", - "pyparsing [] (00m:00s) Decompressing...\n", - "iniconfig [] (00m:00s) Decompressing...\n", - "toml [] (00m:00s) Waiting...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Decompressing...\n", - "iniconfig [] (00m:00s) Decompressing...\n", - "toml [] (00m:00s) Waiting...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "pyparsing [] (00m:00s) Decompressing...\n", - "iniconfig [] (00m:00s) Decompressing...\n", - "toml [] (00m:00s) Decompressing...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "\u001b[5A\u001b[0KFinished pyparsing (00m:00s) 79 KB 287 KB/s\n", - "iniconfig [] (00m:00s) Decompressing...\n", - "toml [] (00m:00s) Decompressing...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "\u001b[4A\u001b[0KFinished iniconfig (00m:00s) 8 KB 28 KB/s\n", - "toml [] (00m:00s) Decompressing...\n", - "pluggy [] (00m:00s) Waiting...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "toml [] (00m:00s) Decompressing...\n", - "pluggy [] (00m:00s) Decompressing...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "\u001b[3A\u001b[0KFinished toml (00m:00s) 18 KB 58 KB/s\n", - "pluggy [] (00m:00s) Decompressing...\n", - "importlib-metadata [] (00m:00s) Waiting...\n", - "pluggy [] (00m:00s) Decompressing...\n", - "importlib-metadata [] (00m:00s) Decompressing...\n", - "\u001b[2A\u001b[0KFinished pluggy (00m:00s) 25 KB 79 KB/s\n", - "importlib-metadata [] (00m:00s) Decompressing...\n", - "\u001b[1A\u001b[0KFinished importlib-metadata (00m:00s) 32 KB 98 KB/s\n", - "openeye-toolkits [] (00m:00s) \n", - "openeye-toolkits [] (00m:00s) 100 KB / 80 MB ( 83.46 KB/s)\n", - "openeye-toolkits [] (00m:00s) 100 KB / 80 MB ( 83.46 KB/s)\n", - "openeye-toolkits [] (00m:00s) 882 KB / 80 MB (634.89 KB/s)\n", - "openeye-toolkits [] (00m:00s) 882 KB / 80 MB (634.89 KB/s)\n", - "openeye-toolkits [] (00m:00s) 4 MB / 80 MB ( 2.48 MB/s)\n", - "openeye-toolkits [] (00m:00s) 4 MB / 80 MB ( 2.48 MB/s)\n", - "openeye-toolkits [] (00m:00s) 9 MB / 80 MB ( 5.59 MB/s)\n", - "openeye-toolkits [] (00m:00s) 9 MB / 80 MB ( 5.59 MB/s)\n", - "openeye-toolkits [] (00m:00s) 15 MB / 80 MB ( 8.06 MB/s)\n", - "openeye-toolkits [] (00m:00s) 15 MB / 80 MB ( 8.06 MB/s)\n", - "openeye-toolkits [] (00m:00s) 23 MB / 80 MB ( 11.47 MB/s)\n", - "openeye-toolkits [] (00m:00s) 23 MB / 80 MB ( 11.47 MB/s)\n", - "openeye-toolkits [] (00m:00s) 30 MB / 80 MB ( 13.86 MB/s)\n", - "openeye-toolkits [] (00m:01s) 30 MB / 80 MB ( 13.86 MB/s)\n", - "openeye-toolkits [] (00m:01s) 40 MB / 80 MB ( 17.38 MB/s)\n", - "openeye-toolkits [] (00m:01s) 40 MB / 80 MB ( 17.38 MB/s)\n", - "openeye-toolkits [] (00m:01s) 50 MB / 80 MB ( 20.53 MB/s)\n", - "openeye-toolkits [] (00m:01s) 50 MB / 80 MB ( 20.53 MB/s)\n", - "openeye-toolkits [] (00m:01s) 59 MB / 80 MB ( 22.56 MB/s)\n", - "openeye-toolkits [] (00m:01s) 59 MB / 80 MB ( 22.56 MB/s)\n", - "openeye-toolkits [] (00m:01s) 67 MB / 80 MB ( 24.14 MB/s)\n", - "openeye-toolkits [] (00m:01s) 67 MB / 80 MB ( 24.14 MB/s)\n", - "openeye-toolkits [] (00m:01s) 76 MB / 80 MB ( 26.23 MB/s)\n", - "openeye-toolkits [] (00m:01s) 76 MB / 80 MB ( 26.23 MB/s)\n", - "openeye-toolkits [] (00m:01s) Validating...\n", - "openeye-toolkits [] (00m:01s) Waiting...\n", - "openeye-toolkits [] (00m:01s) Decompressing...\n", - "\u001b[1A\u001b[0KFinished openeye-toolkits (00m:11s) 80 MB 27 MB/s\n", - "Preparing transaction: - \b\bdone\n", - "Verifying transaction: | \b\b/ \b\b- \b\b\\ \b\bdone\n", - "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n" - ] - } - ], - "source": [ - "!mamba install -c openeye openeye-toolkits --yes" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "G_wInh78orKM" - }, - "source": [ - "Next, mount your Google Drive, which you will need at least for your OpenEye license:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "NRos5DSVorKM", - "outputId": "7e69ce4f-f0c1-4417-df54-885b6fb416b9" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Mounted at /content/drive\n" - ] - } - ], - "source": [ - "# Run cell if using collab\n", - "\n", - "# Mount google drive to Colab Notebooks to access files\n", - "from google.colab import drive\n", - "drive.mount('/content/drive', force_remount = True)" - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "OqKzYKT1-E5_" + }, + "source": [ + "# Sandbox/demo to go along with the MD assignment and lectures\n", + "\n", + "## PharmSci 175/275\n", + "Author: David Mobley\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wFteMA87orKJ" + }, + "source": [ + "## Preparation \n", + "\n", + "To use this notebook you will either need to run it locally using Jupyter or on Google Colab. To run this notebook you will need the OpenEye toolkits installed, as well as the OpenFF toolkits (`openff-toolkit`), `openmm`, and other libraries. If you installed these in a local environment during prep for the class, you should already be prepared; if you are using Google Colab, you will need to install them here. \n", + "\n", + "### Preparation for Google Colab (NOT FOR LOCAL USE)\n", + "\n", + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MobleyLab/drug-computing/blob/master/uci-pharmsci/lectures/MD/MD_Sandbox_Molecule.ipynb)\n", + "\n", + "For Google Colab, we'll use `conda` for installation because it will work better with packaging for OpenFF and OpenMM. Begin by unsetting the PYTHONPATH to prevent issues with miniconda, then installing miniconda (which will take perhaps 20 seconds to a couple of minutes):" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "m1ZRbIDeorKK", + "outputId": "9f0ae4ad-c316-4663-c59b-4b9b85a18a83" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "uJmODg7HorKN" - }, - "source": [ - "Prep OpenEye license and check: " - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "⏬ Downloading https://github.com/jaimergp/miniforge/releases/latest/download/Mambaforge-colab-Linux-x86_64.sh...\n", + "📦 Installing...\n", + "📌 Adjusting configuration...\n", + "🩹 Patching environment...\n", + "⏲ Done in 0:00:28\n", + "🔁 Restarting kernel...\n" + ] + } + ], + "source": [ + "!pip install -q condacolab\n", + "import condacolab\n", + "condacolab.install()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "C4j51ocaorKL" + }, + "source": [ + "**WAIT FOR THAT TO FINISH BEFORE RUNNING ANY OTHER CELLS**; it actually needs to get to the point where it finishes AND RESTARTS YOUR KERNEL before you can continue running additional cells.\n", + "\n", + "Once that is done, install the OpenEye toolkits:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "vusj-hEhorKL", + "outputId": "f9b3f9f7-0d48-4fce-99de-a91b93e6ea64" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "SurQD0JgorKO", - "outputId": "e0fd00f8-b38f-4985-8bd3-8443e12aac94" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Was your OpenEye license correctly installed (True/False)? True\n" - ] - } - ], - "source": [ - "license_filename = '/content/drive/MyDrive/oe_license.txt'\n", - "import openeye\n", - "\n", - "import os\n", - "if os.path.isfile(license_filename):\n", - " license_file = open(license_filename, 'r')\n", - " openeye.OEAddLicenseData(license_file.read())\n", - " license_file.close()\n", - "else:\n", - " print(\"Error: Your OpenEye license is not readable; please check your filename and that you have mounted your Google Drive\")\n", - "\n", - "licensed = openeye.oechem.OEChemIsLicensed()\n", - "print(\"Was your OpenEye license correctly installed (True/False)? \" + str(licensed))\n", - "if not licensed:\n", - " print(\"Error: Your OpenEye license is not correctly installed.\")\n", - " raise Exception(\"Error: Your OpenEye license is not correctly installed.\")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " __ __ __ __\n", + " / \\ / \\ / \\ / \\\n", + " / \\/ \\/ \\/ \\\n", + "███████████████/ /██/ /██/ /██/ /████████████████████████\n", + " / / \\ / \\ / \\ / \\ \\____\n", + " / / \\_/ \\_/ \\_/ \\ o \\__,\n", + " / _/ \\_____/ `\n", + " |/\n", + " ███╗ ███╗ █████╗ ███╗ ███╗██████╗ █████╗\n", + " ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗\n", + " ██╔████╔██║███████║██╔████╔██║██████╔╝███████║\n", + " ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║\n", + " ██║ ╚═╝ ██║██║ ██║██║ ╚═╝ ██║██████╔╝██║ ██║\n", + " ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚═════╝ ╚═╝ ╚═╝\n", + "\n", + " mamba (0.8.0) supported by @QuantStack\n", + "\n", + " GitHub: https://github.com/mamba-org/mamba\n", + " Twitter: https://twitter.com/QuantStack\n", + "\n", + "█████████████████████████████████████████████████████████████\n", + "\n", + "\n", + "Looking for: ['openeye-toolkits']\n", + "\n", + "conda-forge/linux-64 [] (00m:00s) \n", + "conda-forge/linux-64 [] (00m:00s) 219 KB / ?? (720.10 KB/s)\n", + "conda-forge/linux-64 [] (00m:00s) 219 KB / ?? (720.10 KB/s)\n", + "conda-forge/noarch [] (00m:00s) \n", + "conda-forge/linux-64 [] (00m:00s) 219 KB / ?? (720.10 KB/s)\n", + "conda-forge/noarch [] (00m:00s) 357 KB / ?? (1.15 MB/s)\n", + "conda-forge/linux-64 [] (00m:00s) 219 KB / ?? (720.10 KB/s)\n", + "conda-forge/noarch [] (00m:00s) 357 KB / ?? (1.15 MB/s)\n", + "conda-forge/linux-64 [] (00m:00s) 906 KB / ?? (1.95 MB/s)\n", + "conda-forge/noarch [] (00m:00s) 357 KB / ?? (1.15 MB/s)\n", + "conda-forge/linux-64 [] (00m:00s) 906 KB / ?? (1.95 MB/s)\n", + "conda-forge/noarch [] (00m:00s) 357 KB / ?? (1.15 MB/s)\n", + "conda-forge/linux-64 [] (00m:00s) 906 KB / ?? (1.95 MB/s)\n", + "conda-forge/noarch [] (00m:00s) 1 MB / ?? (2.32 MB/s)\n", + "conda-forge/linux-64 [] (00m:00s) 906 KB / ?? (1.95 MB/s)\n", + "conda-forge/noarch [] (00m:00s) 1 MB / ?? (2.32 MB/s)\n", + "openeye/noarch [] (--:--) Finalizing...\n", + "conda-forge/linux-64 [] (00m:00s) 906 KB / ?? 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(2.32 MB/s)\n", + "openeye/linux-64 [] (00m:00s) Done\n", + "openeye/linux-64 [] (00m:00s) Done\n", + "conda-forge/linux-64 [] (00m:00s) 906 KB / ?? (1.95 MB/s)\n", + "conda-forge/noarch [] (00m:00s) 1 MB / ?? (2.32 MB/s)\n", + "conda-forge/linux-64 [] (00m:00s) 906 KB / ?? (1.95 MB/s)\n", + "conda-forge/noarch [] (00m:00s) 1 MB / ?? (2.32 MB/s)\n", + "conda-forge/linux-64 [] (00m:00s) 2 MB / ?? (2.52 MB/s)\n", + "conda-forge/noarch [] (00m:00s) 1 MB / ?? (2.32 MB/s)\n", + "conda-forge/linux-64 [] (00m:00s) 2 MB / ?? (2.52 MB/s)\n", + "conda-forge/noarch [] (00m:00s) 1 MB / ?? (2.32 MB/s)\n", + "conda-forge/linux-64 [] (00m:00s) 2 MB / ?? (2.52 MB/s)\n", + "conda-forge/noarch [] (00m:00s) 2 MB / ?? (2.83 MB/s)\n", + "conda-forge/linux-64 [] (00m:00s) 2 MB / ?? (2.52 MB/s)\n", + "conda-forge/noarch [] (00m:00s) 2 MB / ?? (2.83 MB/s)\n", + "conda-forge/linux-64 [] (00m:00s) 2 MB / ?? (2.76 MB/s)\n", + "conda-forge/noarch [] (00m:00s) 2 MB / ?? 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(4.17 MB/s)\n", + "conda-forge/linux-64 [] (00m:04s) 18 MB / ?? (4.16 MB/s)\n", + "conda-forge/linux-64 [] (00m:04s) 18 MB / ?? (4.16 MB/s)\n", + "conda-forge/linux-64 [] (00m:04s) 19 MB / ?? (4.17 MB/s)\n", + "conda-forge/linux-64 [] (00m:04s) Finalizing...\n", + "conda-forge/linux-64 [] (00m:04s) Done\n", + "conda-forge/linux-64 [] (00m:04s) Done\n", + "conda-forge/linux-64 [] (00m:04s) Done\n", + "Transaction\n", + "\n", + " Prefix: /usr/local\n", + "\n", + " Updating specs:\n", + "\n", + " - openeye-toolkits\n", + "\n", + "\n", + " Package Version Build Channel Size\n", + "──────────────────────────────────────────────────────────────────────────────────\n", + " Install:\n", + "──────────────────────────────────────────────────────────────────────────────────\n", + "\n", + "\u001b[32m attrs \u001b[00m 21.4.0 pyhd8ed1ab_0 conda-forge/noarch 49 KB\n", + "\u001b[32m importlib-metadata\u001b[00m 4.10.0 py37h89c1867_0 conda-forge/linux-64 32 KB\n", + "\u001b[32m importlib_metadata\u001b[00m 4.10.0 hd8ed1ab_0 conda-forge/noarch 4 KB\n", + "\u001b[32m iniconfig \u001b[00m 1.1.1 pyh9f0ad1d_0 conda-forge/noarch 8 KB\n", + "\u001b[32m openeye-toolkits \u001b[00m 2021.2.0 py37_0 openeye/linux-64 80 MB\n", + "\u001b[32m packaging \u001b[00m 21.3 pyhd8ed1ab_0 conda-forge/noarch 36 KB\n", + "\u001b[32m pluggy \u001b[00m 1.0.0 py37h89c1867_2 conda-forge/linux-64 25 KB\n", + "\u001b[32m py \u001b[00m 1.11.0 pyh6c4a22f_0 conda-forge/noarch 74 KB\n", + "\u001b[32m pyparsing \u001b[00m 3.0.6 pyhd8ed1ab_0 conda-forge/noarch 79 KB\n", + "\u001b[32m pytest \u001b[00m 6.2.5 py37h89c1867_2 conda-forge/linux-64 432 KB\n", + "\u001b[32m toml \u001b[00m 0.10.2 pyhd8ed1ab_0 conda-forge/noarch 18 KB\n", + "\u001b[32m typing_extensions \u001b[00m 4.0.1 pyha770c72_0 conda-forge/noarch 26 KB\n", + "\u001b[32m zipp \u001b[00m 3.7.0 pyhd8ed1ab_0 conda-forge/noarch 12 KB\n", + "\n", + " Upgrade:\n", + "──────────────────────────────────────────────────────────────────────────────────\n", + "\n", + "\u001b[31m ca-certificates \u001b[00m 2020.12.5 ha878542_0 installed \n", + "\u001b[32m ca-certificates \u001b[00m 2021.10.8 ha878542_0 conda-forge/linux-64 139 KB\n", + "\u001b[31m certifi \u001b[00m 2020.12.5 py37h89c1867_1 installed \n", + "\u001b[32m certifi \u001b[00m 2021.10.8 py37h89c1867_1 conda-forge/linux-64 145 KB\n", + "\u001b[31m libgcc-ng \u001b[00m 9.3.0 h2828fa1_18 installed \n", + "\u001b[32m libgcc-ng \u001b[00m 11.2.0 h1d223b6_11 conda-forge/linux-64 887 KB\n", + "\u001b[31m libgomp \u001b[00m 9.3.0 h2828fa1_18 installed \n", + "\u001b[32m libgomp \u001b[00m 11.2.0 h1d223b6_11 conda-forge/linux-64 427 KB\n", + "\u001b[31m openssl \u001b[00m 1.1.1j h7f98852_0 installed \n", + "\u001b[32m openssl \u001b[00m 1.1.1l h7f98852_0 conda-forge/linux-64 2 MB\n", + "\n", + " Summary:\n", + "\n", + " Install: 13 packages\n", + " Upgrade: 5 packages\n", + "\n", + " Total download: 84 MB\n", + "\n", + "──────────────────────────────────────────────────────────────────────────────────\n", + "\n", + "ca-certificates [] (00m:00s) \n", + "ca-certificates [] (00m:00s) 115 KB / 139 KB (768.94 KB/s)\n", + "ca-certificates [] (00m:00s) 115 KB / 139 KB (768.94 KB/s)\n", + "certifi [] (00m:00s) \n", + "ca-certificates [] (00m:00s) 115 KB / 139 KB (768.94 KB/s)\n", + "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", + "ca-certificates [] (00m:00s) 115 KB / 139 KB (768.94 KB/s)\n", + "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", + "libgcc-ng [] (00m:00s) \n", + "ca-certificates [] (00m:00s) 115 KB / 139 KB (768.94 KB/s)\n", + "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", + "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", + "ca-certificates [] (00m:00s) 115 KB / 139 KB (768.94 KB/s)\n", + "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", + "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", + "libgomp [] (00m:00s) \n", + "ca-certificates [] (00m:00s) 115 KB / 139 KB (768.94 KB/s)\n", + "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", + "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", + "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", + "ca-certificates [] (00m:00s) 115 KB / 139 KB (768.94 KB/s)\n", + "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", + "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", + "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", + "openssl [] (00m:00s) \n", + "ca-certificates [] (00m:00s) 115 KB / 139 KB (768.94 KB/s)\n", + "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", + "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", + "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", + "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", + "ca-certificates [] (00m:00s) 115 KB / 139 KB (768.94 KB/s)\n", + "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", + "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", + "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", + "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", + "ca-certificates [] (00m:00s) Validating...\n", + "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", + "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", + "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", + "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", + "ca-certificates [] (00m:00s) Waiting...\n", + "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", + "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", + "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", + "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", + "ca-certificates [] (00m:00s) Decompressing...\n", + "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", + "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", + "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", + "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", + "ca-certificates [] (00m:00s) Decompressing...\n", + "certifi [] (00m:00s) 62 KB / 145 KB (413.25 KB/s)\n", + "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", + "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", + "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", + "ca-certificates [] (00m:00s) Decompressing...\n", + "certifi [] (00m:00s) Validating...\n", + "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", + "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", + "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", + "ca-certificates [] (00m:00s) Decompressing...\n", + "certifi [] (00m:00s) Waiting...\n", + "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", + "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", + "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", + "\u001b[5A\u001b[0KFinished ca-certificates (00m:00s) 139 KB 909 KB/s\n", + "certifi [] (00m:00s) Decompressing...\n", + "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", + "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", + "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", + "certifi [] (00m:00s) Decompressing...\n", + "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", + "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", + "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", + "certifi [] (00m:00s) Decompressing...\n", + "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", + "libgomp [] (00m:00s) 103 KB / 427 KB (688.40 KB/s)\n", + "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", + "certifi [] (00m:00s) Decompressing...\n", + "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", + "libgomp [] (00m:00s) Validating...\n", + "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", + "certifi [] (00m:00s) Decompressing...\n", + "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", + "libgomp [] (00m:00s) Waiting...\n", + "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", + "certifi [] (00m:00s) Decompressing...\n", + "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", + "libgomp [] (00m:00s) Waiting...\n", + "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", + "packaging [] (00m:00s) \n", + "certifi [] (00m:00s) Decompressing...\n", + "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", + "libgomp [] (00m:00s) Waiting...\n", + "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", + "packaging [] (00m:00s) Validating...\n", + "certifi [] (00m:00s) Decompressing...\n", + "libgcc-ng [] (00m:00s) 119 KB / 887 KB (794.84 KB/s)\n", + "libgomp [] (00m:00s) Waiting...\n", + "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", + "packaging [] (00m:00s) Validating...\n", + "certifi [] (00m:00s) Decompressing...\n", + "libgcc-ng [] (00m:00s) Validating...\n", + "libgomp [] (00m:00s) Waiting...\n", + "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", + "packaging [] (00m:00s) Validating...\n", + "certifi [] (00m:00s) Decompressing...\n", + "libgcc-ng [] (00m:00s) Validating...\n", + "libgomp [] (00m:00s) Waiting...\n", + "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", + "packaging [] (00m:00s) Waiting...\n", + "certifi [] (00m:00s) Decompressing...\n", + "libgcc-ng [] (00m:00s) Validating...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", + "packaging [] (00m:00s) Waiting...\n", + "\u001b[5A\u001b[0KFinished certifi (00m:00s) 145 KB 857 KB/s\n", + "libgcc-ng [] (00m:00s) Validating...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", + "packaging [] (00m:00s) Waiting...\n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", + "packaging [] (00m:00s) Waiting...\n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) 121 KB / 2 MB (805.20 KB/s)\n", + "packaging [] (00m:00s) Waiting...\n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Validating...\n", + "packaging [] (00m:00s) Waiting...\n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Validating...\n", + "packaging [] (00m:00s) Waiting...\n", + "py [] (00m:00s) \n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Validating...\n", + "packaging [] (00m:00s) Waiting...\n", + "py [] (00m:00s) Validating...\n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Validating...\n", + "packaging [] (00m:00s) Waiting...\n", + "py [] (00m:00s) Waiting...\n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "packaging [] (00m:00s) Waiting...\n", + "py [] (00m:00s) Waiting...\n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "packaging [] (00m:00s) Waiting...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) \n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "packaging [] (00m:00s) Waiting...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Validating...\n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "packaging [] (00m:00s) Waiting...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Waiting...\n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "packaging [] (00m:00s) Waiting...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) \n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "packaging [] (00m:00s) Waiting...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Validating...\n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "packaging [] (00m:00s) Waiting...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Waiting...\n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "packaging [] (00m:00s) Waiting...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Waiting...\n", + "importlib_metadata [] (00m:00s) \n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "packaging [] (00m:00s) Waiting...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Waiting...\n", + "importlib_metadata [] (00m:00s) Validating...\n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "packaging [] (00m:00s) Waiting...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Waiting...\n", + "importlib_metadata [] (00m:00s) Waiting...\n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "packaging [] (00m:00s) Waiting...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Waiting...\n", + "importlib_metadata [] (00m:00s) Waiting...\n", + "pytest [] (00m:00s) \n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "packaging [] (00m:00s) Waiting...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Waiting...\n", + "importlib_metadata [] (00m:00s) Waiting...\n", + "pytest [] (00m:00s) Validating...\n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "packaging [] (00m:00s) Waiting...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Waiting...\n", + "importlib_metadata [] (00m:00s) Waiting...\n", + "pytest [] (00m:00s) Waiting...\n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "packaging [] (00m:00s) Decompressing...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Waiting...\n", + "importlib_metadata [] (00m:00s) Waiting...\n", + "pytest [] (00m:00s) Waiting...\n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "packaging [] (00m:00s) Decompressing...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Waiting...\n", + "importlib_metadata [] (00m:00s) Waiting...\n", + "pytest [] (00m:00s) Waiting...\n", + "pyparsing [] (00m:00s) \n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "packaging [] (00m:00s) Decompressing...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Waiting...\n", + "importlib_metadata [] (00m:00s) Waiting...\n", + "pytest [] (00m:00s) Waiting...\n", + "pyparsing [] (00m:00s) Validating...\n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "packaging [] (00m:00s) Decompressing...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Waiting...\n", + "importlib_metadata [] (00m:00s) Waiting...\n", + "pytest [] (00m:00s) Waiting...\n", + "pyparsing [] (00m:00s) Validating...\n", + "zipp [] (00m:00s) \n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "packaging [] (00m:00s) Decompressing...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Waiting...\n", + "importlib_metadata [] (00m:00s) Waiting...\n", + "pytest [] (00m:00s) Waiting...\n", + "pyparsing [] (00m:00s) Validating...\n", + "zipp [] (00m:00s) Validating...\n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "libgomp [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "packaging [] (00m:00s) Decompressing...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Waiting...\n", + "importlib_metadata [] (00m:00s) Waiting...\n", + "pytest [] (00m:00s) Waiting...\n", + "pyparsing [] (00m:00s) Waiting...\n", + "zipp [] (00m:00s) Validating...\n", + "\u001b[11A\u001b[0KFinished libgomp (00m:00s) 427 KB 2 MB/s\n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "openssl [] (00m:00s) Waiting...\n", + "packaging [] (00m:00s) Decompressing...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Waiting...\n", + "importlib_metadata [] (00m:00s) Waiting...\n", + "pytest [] (00m:00s) Waiting...\n", + "pyparsing [] (00m:00s) Waiting...\n", + "zipp [] (00m:00s) Validating...\n", + "libgcc-ng [] (00m:00s) Waiting...\n", + "openssl [] (00m:00s) Waiting...\n", + "packaging [] (00m:00s) Decompressing...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Waiting...\n", + "importlib_metadata [] (00m:00s) Waiting...\n", + "pytest [] (00m:00s) Waiting...\n", + "pyparsing [] (00m:00s) Waiting...\n", + "zipp [] (00m:00s) Waiting...\n", + "libgcc-ng [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "packaging [] (00m:00s) Decompressing...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] 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Waiting...\n", + "importlib_metadata [] (00m:00s) Waiting...\n", + "pytest [] (00m:00s) Waiting...\n", + "pyparsing [] (00m:00s) Waiting...\n", + "zipp [] (00m:00s) Waiting...\n", + "iniconfig [] (00m:00s) Waiting...\n", + "toml [] (00m:00s) Waiting...\n", + "libgcc-ng [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Waiting...\n", + "importlib_metadata [] (00m:00s) Waiting...\n", + "pytest [] (00m:00s) Waiting...\n", + "pyparsing [] (00m:00s) Waiting...\n", + "zipp [] (00m:00s) Waiting...\n", + "iniconfig [] (00m:00s) Waiting...\n", + "toml [] (00m:00s) Waiting...\n", + "pluggy [] (00m:00s) \n", + "libgcc-ng [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Waiting...\n", + "importlib_metadata [] (00m:00s) Waiting...\n", + "pytest 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Waiting...\n", + "pyparsing [] (00m:00s) Waiting...\n", + "zipp [] (00m:00s) Waiting...\n", + "iniconfig [] (00m:00s) Waiting...\n", + "toml [] (00m:00s) Waiting...\n", + "pluggy [] (00m:00s) Waiting...\n", + "importlib-metadata [] (00m:00s) \n", + "libgcc-ng [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Waiting...\n", + "importlib_metadata [] (00m:00s) Waiting...\n", + "pytest [] (00m:00s) Waiting...\n", + "pyparsing [] (00m:00s) Waiting...\n", + "zipp [] (00m:00s) Waiting...\n", + "iniconfig [] (00m:00s) Waiting...\n", + "toml [] (00m:00s) Waiting...\n", + "pluggy [] (00m:00s) Waiting...\n", + "importlib-metadata [] (00m:00s) Validating...\n", + "libgcc-ng [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "py [] (00m:00s) Waiting...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Waiting...\n", + "importlib_metadata [] (00m:00s) Waiting...\n", + "pytest [] (00m:00s) Waiting...\n", + "pyparsing [] (00m:00s) Waiting...\n", + "zipp [] (00m:00s) Waiting...\n", + "iniconfig [] (00m:00s) Waiting...\n", + "toml [] (00m:00s) Waiting...\n", + "pluggy [] (00m:00s) Waiting...\n", + "importlib-metadata [] (00m:00s) Waiting...\n", + "libgcc-ng [] (00m:00s) Decompressing...\n", + "openssl [] (00m:00s) Waiting...\n", + "py [] (00m:00s) Decompressing...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Waiting...\n", + "importlib_metadata [] (00m:00s) Waiting...\n", + "pytest [] (00m:00s) Waiting...\n", + "pyparsing [] (00m:00s) Waiting...\n", + "zipp [] (00m:00s) Waiting...\n", + "iniconfig [] (00m:00s) Waiting...\n", + "toml [] (00m:00s) Waiting...\n", + "pluggy [] (00m:00s) Waiting...\n", + "importlib-metadata [] (00m:00s) Waiting...\n", + "\u001b[13A\u001b[0KFinished libgcc-ng (00m:00s) 887 KB 5 MB/s\n", + "openssl [] (00m:00s) Waiting...\n", + "py [] (00m:00s) Decompressing...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Waiting...\n", + "importlib_metadata [] (00m:00s) Waiting...\n", + "pytest [] (00m:00s) Waiting...\n", + "pyparsing [] (00m:00s) Waiting...\n", + "zipp [] (00m:00s) Waiting...\n", + "iniconfig [] (00m:00s) Waiting...\n", + "toml [] (00m:00s) Waiting...\n", + "pluggy [] (00m:00s) Waiting...\n", + "importlib-metadata [] (00m:00s) Waiting...\n", + "\u001b[12A\u001b[0KFinished py (00m:00s) 74 KB 354 KB/s\n", + "openssl [] (00m:00s) Decompressing...\n", + "attrs [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Waiting...\n", + "importlib_metadata [] (00m:00s) Waiting...\n", + "pytest [] (00m:00s) Waiting...\n", + "pyparsing [] (00m:00s) Waiting...\n", + "zipp [] (00m:00s) Waiting...\n", + "iniconfig [] (00m:00s) Waiting...\n", + "toml [] (00m:00s) Waiting...\n", + "pluggy [] (00m:00s) Waiting...\n", + "importlib-metadata [] (00m:00s) Waiting...\n", + "openssl [] (00m:00s) 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"pytest [] (00m:00s) Waiting...\n", + "pyparsing [] (00m:00s) Waiting...\n", + "zipp [] (00m:00s) Waiting...\n", + "iniconfig [] (00m:00s) Waiting...\n", + "toml [] (00m:00s) Waiting...\n", + "pluggy [] (00m:00s) Waiting...\n", + "importlib-metadata [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Decompressing...\n", + "importlib_metadata [] (00m:00s) Decompressing...\n", + "pytest [] (00m:00s) Waiting...\n", + "pyparsing [] (00m:00s) Waiting...\n", + "zipp [] (00m:00s) Waiting...\n", + "iniconfig [] (00m:00s) Waiting...\n", + "toml [] (00m:00s) Waiting...\n", + "pluggy [] (00m:00s) Waiting...\n", + "importlib-metadata [] (00m:00s) Waiting...\n", + "\u001b[9A\u001b[0KFinished importlib_metadata (00m:00s) 4 KB 15 KB/s\n", + "typing_extensions [] (00m:00s) Decompressing...\n", + "pytest [] (00m:00s) Decompressing...\n", + "pyparsing [] (00m:00s) Waiting...\n", + "zipp [] (00m:00s) Waiting...\n", + "iniconfig [] (00m:00s) Waiting...\n", + "toml [] (00m:00s) Waiting...\n", + "pluggy [] (00m:00s) Waiting...\n", + "importlib-metadata [] (00m:00s) Waiting...\n", + "typing_extensions [] (00m:00s) Decompressing...\n", + "pytest [] (00m:00s) Decompressing...\n", + "pyparsing [] (00m:00s) Waiting...\n", + "zipp [] (00m:00s) Waiting...\n", + "iniconfig [] (00m:00s) Waiting...\n", + "toml [] (00m:00s) Waiting...\n", + "pluggy [] (00m:00s) Waiting...\n", + "importlib-metadata [] (00m:00s) Waiting...\n", + "\u001b[8A\u001b[0KFinished typing_extensions (00m:00s) 26 KB 111 KB/s\n", + "pytest [] (00m:00s) Decompressing...\n", + "pyparsing [] (00m:00s) Waiting...\n", + "zipp [] (00m:00s) Waiting...\n", + "iniconfig [] (00m:00s) Waiting...\n", + "toml [] (00m:00s) Waiting...\n", + "pluggy [] (00m:00s) Waiting...\n", + "importlib-metadata [] (00m:00s) Waiting...\n", + "pytest [] (00m:00s) Decompressing...\n", + "pyparsing [] (00m:00s) Decompressing...\n", + "zipp [] (00m:00s) Waiting...\n", + "iniconfig [] (00m:00s) Waiting...\n", + "toml [] (00m:00s) Waiting...\n", + "pluggy [] (00m:00s) Waiting...\n", + "importlib-metadata [] (00m:00s) Waiting...\n", + "\u001b[7A\u001b[0KFinished pytest (00m:00s) 432 KB 2 MB/s\n", + "pyparsing [] (00m:00s) Decompressing...\n", + "zipp [] (00m:00s) Waiting...\n", + "iniconfig [] (00m:00s) Waiting...\n", + "toml [] (00m:00s) Waiting...\n", + "pluggy [] (00m:00s) Waiting...\n", + "importlib-metadata [] (00m:00s) Waiting...\n", + "pyparsing [] (00m:00s) Decompressing...\n", + "zipp [] (00m:00s) Decompressing...\n", + "iniconfig [] (00m:00s) Waiting...\n", + "toml [] (00m:00s) Waiting...\n", + "pluggy [] (00m:00s) Waiting...\n", + "importlib-metadata [] (00m:00s) Waiting...\n", + "\u001b[6A\u001b[0KFinished zipp (00m:00s) 12 KB 43 KB/s\n", + "pyparsing [] (00m:00s) Decompressing...\n", + "iniconfig [] (00m:00s) Decompressing...\n", + "toml [] (00m:00s) Waiting...\n", + "pluggy [] (00m:00s) Waiting...\n", + "importlib-metadata [] (00m:00s) Waiting...\n", + "pyparsing [] (00m:00s) Decompressing...\n", + "iniconfig [] (00m:00s) Decompressing...\n", + "toml [] (00m:00s) Waiting...\n", + "pluggy [] (00m:00s) Waiting...\n", + "importlib-metadata [] (00m:00s) Waiting...\n", + "pyparsing [] (00m:00s) Decompressing...\n", + "iniconfig [] (00m:00s) Decompressing...\n", + "toml [] (00m:00s) Decompressing...\n", + "pluggy [] (00m:00s) Waiting...\n", + "importlib-metadata [] (00m:00s) Waiting...\n", + "\u001b[5A\u001b[0KFinished pyparsing (00m:00s) 79 KB 287 KB/s\n", + "iniconfig [] (00m:00s) Decompressing...\n", + "toml [] (00m:00s) Decompressing...\n", + "pluggy [] (00m:00s) Waiting...\n", + "importlib-metadata [] (00m:00s) Waiting...\n", + "\u001b[4A\u001b[0KFinished iniconfig (00m:00s) 8 KB 28 KB/s\n", + "toml [] (00m:00s) Decompressing...\n", + "pluggy [] (00m:00s) Waiting...\n", + "importlib-metadata [] (00m:00s) Waiting...\n", + "toml [] (00m:00s) Decompressing...\n", + "pluggy [] (00m:00s) Decompressing...\n", + "importlib-metadata [] (00m:00s) Waiting...\n", + "\u001b[3A\u001b[0KFinished toml (00m:00s) 18 KB 58 KB/s\n", + "pluggy [] (00m:00s) Decompressing...\n", + "importlib-metadata [] (00m:00s) Waiting...\n", + "pluggy [] (00m:00s) Decompressing...\n", + "importlib-metadata [] (00m:00s) Decompressing...\n", + "\u001b[2A\u001b[0KFinished pluggy (00m:00s) 25 KB 79 KB/s\n", + "importlib-metadata [] (00m:00s) Decompressing...\n", + "\u001b[1A\u001b[0KFinished importlib-metadata (00m:00s) 32 KB 98 KB/s\n", + "openeye-toolkits [] (00m:00s) \n", + "openeye-toolkits [] (00m:00s) 100 KB / 80 MB ( 83.46 KB/s)\n", + "openeye-toolkits [] (00m:00s) 100 KB / 80 MB ( 83.46 KB/s)\n", + "openeye-toolkits [] (00m:00s) 882 KB / 80 MB (634.89 KB/s)\n", + "openeye-toolkits [] (00m:00s) 882 KB / 80 MB (634.89 KB/s)\n", + "openeye-toolkits [] (00m:00s) 4 MB / 80 MB ( 2.48 MB/s)\n", + "openeye-toolkits [] (00m:00s) 4 MB / 80 MB ( 2.48 MB/s)\n", + "openeye-toolkits [] (00m:00s) 9 MB / 80 MB ( 5.59 MB/s)\n", + "openeye-toolkits [] (00m:00s) 9 MB / 80 MB ( 5.59 MB/s)\n", + "openeye-toolkits [] (00m:00s) 15 MB / 80 MB ( 8.06 MB/s)\n", + "openeye-toolkits [] (00m:00s) 15 MB / 80 MB ( 8.06 MB/s)\n", + "openeye-toolkits [] (00m:00s) 23 MB / 80 MB ( 11.47 MB/s)\n", + "openeye-toolkits [] (00m:00s) 23 MB / 80 MB ( 11.47 MB/s)\n", + "openeye-toolkits [] (00m:00s) 30 MB / 80 MB ( 13.86 MB/s)\n", + "openeye-toolkits [] (00m:01s) 30 MB / 80 MB ( 13.86 MB/s)\n", + "openeye-toolkits [] (00m:01s) 40 MB / 80 MB ( 17.38 MB/s)\n", + "openeye-toolkits [] (00m:01s) 40 MB / 80 MB ( 17.38 MB/s)\n", + "openeye-toolkits [] (00m:01s) 50 MB / 80 MB ( 20.53 MB/s)\n", + "openeye-toolkits [] (00m:01s) 50 MB / 80 MB ( 20.53 MB/s)\n", + "openeye-toolkits [] (00m:01s) 59 MB / 80 MB ( 22.56 MB/s)\n", + "openeye-toolkits [] (00m:01s) 59 MB / 80 MB ( 22.56 MB/s)\n", + "openeye-toolkits [] (00m:01s) 67 MB / 80 MB ( 24.14 MB/s)\n", + "openeye-toolkits [] (00m:01s) 67 MB / 80 MB ( 24.14 MB/s)\n", + "openeye-toolkits [] (00m:01s) 76 MB / 80 MB ( 26.23 MB/s)\n", + "openeye-toolkits [] (00m:01s) 76 MB / 80 MB ( 26.23 MB/s)\n", + "openeye-toolkits [] (00m:01s) Validating...\n", + "openeye-toolkits [] (00m:01s) Waiting...\n", + "openeye-toolkits [] (00m:01s) Decompressing...\n", + "\u001b[1A\u001b[0KFinished openeye-toolkits (00m:11s) 80 MB 27 MB/s\n", + "Preparing transaction: - \b\bdone\n", + "Verifying transaction: | \b\b/ \b\b- \b\b\\ \b\bdone\n", + "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n" + ] + } + ], + "source": [ + "!mamba install -c openeye openeye-toolkits --yes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "G_wInh78orKM" + }, + "source": [ + "Next, mount your Google Drive, which you will need at least for your OpenEye license:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "NRos5DSVorKM", + "outputId": "7e69ce4f-f0c1-4417-df54-885b6fb416b9" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "hJ9oc_3AqGZv" - }, - "source": [ - "#### Now(for Colab) we conda install the other prerequisites" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Mounted at /content/drive\n" + ] + } + ], + "source": [ + "# Run cell if using collab\n", + "\n", + "# Mount google drive to Colab Notebooks to access files\n", + "from google.colab import drive\n", + "drive.mount('/content/drive', force_remount = True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uJmODg7HorKN" + }, + "source": [ + "Prep OpenEye license and check: " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "SurQD0JgorKO", + "outputId": "e0fd00f8-b38f-4985-8bd3-8443e12aac94" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "vU4oSJrbqLDb", - "outputId": "e1bbbb21-afbd-4af8-ba82-ec525e9c6f85" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - " __ __ __ __\n", - " / \\ / \\ / \\ / \\\n", - " / \\/ \\/ \\/ \\\n", - "███████████████/ /██/ /██/ /██/ /████████████████████████\n", - " / / \\ / \\ / \\ / \\ \\____\n", - " / / \\_/ \\_/ \\_/ \\ o \\__,\n", - " / _/ \\_____/ `\n", - " |/\n", - " ███╗ ███╗ █████╗ ███╗ ███╗██████╗ █████╗\n", - " ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗\n", - " ██╔████╔██║███████║██╔████╔██║██████╔╝███████║\n", - " ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║\n", - " ██║ ╚═╝ ██║██║ ██║██║ ╚═╝ ██║██████╔╝██║ ██║\n", - " ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚═════╝ ╚═╝ ╚═╝\n", - "\n", - " Supported by @QuantStack\n", - "\n", - " GitHub: https://github.com/QuantStack/mamba\n", - " Twitter: https://twitter.com/QuantStack\n", - "\n", - "█████████████████████████████████████████████████████████████\n", - "\n", - "Getting conda-forge linux-64\n", - "Getting conda-forge noarch\n", - "Getting pkgs/main linux-64\n", - "Getting pkgs/main noarch\n", - "Getting pkgs/r linux-64\n", - "Getting pkgs/r noarch\n", - "\n", - "Looking for: ['openff-toolkit', 'openmm', 'nglview', 'python 3.7.10']\n", - "\n", - "235541 packages in https://conda.anaconda.org/conda-forge/linux-64\n", - "79365 packages in https://conda.anaconda.org/conda-forge/noarch\n", - "24262 packages in https://repo.anaconda.com/pkgs/main/linux-64\n", - "4551 packages in https://repo.anaconda.com/pkgs/main/noarch\n", - "6637 packages in https://repo.anaconda.com/pkgs/r/linux-64\n", - "5025 packages in https://repo.anaconda.com/pkgs/r/noarch\n", - "\n", - "## Package Plan ##\n", - "\n", - " environment location: /usr/local\n", - "\n", - " added / updated specs:\n", - " - nglview\n", - " - openff-toolkit\n", - " - openmm\n", - " - python==3.7.10\n", - "\n", - "\n", - "The following packages will be downloaded:\n", - "\n", - " package | build\n", - " ---------------------------|-----------------\n", - " argon2-cffi-21.3.0 | pyhd8ed1ab_0 15 KB conda-forge\n", - " argon2-cffi-bindings-21.2.0| py37h5e8e339_1 34 KB conda-forge\n", - " backcall-0.2.0 | pyhd3eb1b0_0 13 KB\n", - " backports-1.0 | py37_1 4 KB\n", - " backports.functools_lru_cache-1.6.4| pyhd8ed1ab_0 9 KB conda-forge\n", - " bleach-4.1.0 | pyhd8ed1ab_0 121 KB conda-forge\n", - " debugpy-1.5.1 | py37hcd2ae1e_0 2.0 MB conda-forge\n", - " decorator-5.1.1 | pyhd8ed1ab_0 12 KB conda-forge\n", - " defusedxml-0.7.1 | pyhd8ed1ab_0 23 KB conda-forge\n", - " entrypoints-0.3 |py37hc8dfbb8_1002 13 KB conda-forge\n", - " flit-core-3.6.0 | pyhd8ed1ab_0 42 KB conda-forge\n", - " importlib_resources-5.4.0 | pyhd8ed1ab_0 21 KB conda-forge\n", - " ipykernel-6.7.0 | py37h6531663_0 179 KB conda-forge\n", - " ipython-7.31.0 | py37h89c1867_0 1.1 MB conda-forge\n", - " ipython_genutils-0.2.0 | py37_0 39 KB\n", - " ipywidgets-7.6.5 | pyhd3eb1b0_1 105 KB\n", - " jedi-0.18.1 | py37h89c1867_0 997 KB conda-forge\n", - " jinja2-3.0.3 | pyhd8ed1ab_0 99 KB conda-forge\n", - " jsonschema-4.4.0 | pyhd8ed1ab_0 57 KB conda-forge\n", - " jupyter_client-7.1.1 | pyhd8ed1ab_0 90 KB conda-forge\n", - " jupyter_core-4.9.1 | py37h89c1867_1 80 KB conda-forge\n", - " jupyterlab_pygments-0.1.2 | pyh9f0ad1d_0 8 KB conda-forge\n", - " jupyterlab_widgets-1.0.2 | pyhd8ed1ab_0 130 KB conda-forge\n", - " libsodium-1.0.18 | h516909a_1 366 KB conda-forge\n", - " markupsafe-2.0.1 | py37h5e8e339_1 22 KB conda-forge\n", - " matplotlib-inline-0.1.3 | pyhd8ed1ab_0 11 KB conda-forge\n", - " mistune-0.8.4 |py37h5e8e339_1005 54 KB conda-forge\n", - " nbclient-0.5.10 | pyhd8ed1ab_1 63 KB conda-forge\n", - " nbconvert-6.4.0 | py37h89c1867_0 551 KB conda-forge\n", - " nbformat-5.1.3 | pyhd8ed1ab_0 47 KB conda-forge\n", - " nest-asyncio-1.5.4 | pyhd8ed1ab_0 9 KB conda-forge\n", - " nglview-3.0.3 | pyh8a188c0_0 6.3 MB conda-forge\n", - " notebook-6.4.7 | pyha770c72_0 6.2 MB conda-forge\n", - " pandoc-2.17 | h7f98852_0 12.7 MB conda-forge\n", - " pandocfilters-1.5.0 | pyhd8ed1ab_0 11 KB conda-forge\n", - " parso-0.8.3 | pyhd8ed1ab_0 69 KB conda-forge\n", - " pexpect-4.8.0 | py37_1 77 KB\n", - " pickleshare-0.7.5 |py37hc8dfbb8_1002 13 KB conda-forge\n", - " prometheus_client-0.12.0 | pyhd8ed1ab_0 47 KB conda-forge\n", - " prompt-toolkit-3.0.24 | pyha770c72_0 249 KB conda-forge\n", - " ptyprocess-0.7.0 | pyhd3eb1b0_2 17 KB\n", - " pygments-2.11.2 | pyhd8ed1ab_0 796 KB conda-forge\n", - " pyrsistent-0.18.0 | py37heee7806_0 95 KB\n", - " pyzmq-22.3.0 | py37h295c915_2 465 KB\n", - " send2trash-1.8.0 | pyhd3eb1b0_1 19 KB\n", - " terminado-0.12.1 | py37h89c1867_1 28 KB conda-forge\n", - " testpath-0.5.0 | pyhd8ed1ab_0 86 KB conda-forge\n", - " tornado-6.1 | py37h5e8e339_2 642 KB conda-forge\n", - " traitlets-5.1.1 | pyhd8ed1ab_0 82 KB conda-forge\n", - " wcwidth-0.2.5 | pyh9f0ad1d_2 33 KB conda-forge\n", - " webencodings-0.5.1 | py37_1 19 KB\n", - " widgetsnbextension-3.5.2 | py37h89c1867_1 1.3 MB conda-forge\n", - " zeromq-4.3.4 | h9c3ff4c_1 351 KB conda-forge\n", - " ------------------------------------------------------------\n", - " Total: 35.8 MB\n", - "\n", - "The following NEW packages will be INSTALLED:\n", - "\n", - " argon2-cffi conda-forge/noarch::argon2-cffi-21.3.0-pyhd8ed1ab_0\n", - " argon2-cffi-bindi~ conda-forge/linux-64::argon2-cffi-bindings-21.2.0-py37h5e8e339_1\n", - " backcall pkgs/main/noarch::backcall-0.2.0-pyhd3eb1b0_0\n", - " backports pkgs/main/linux-64::backports-1.0-py37_1\n", - " backports.functoo~ conda-forge/noarch::backports.functools_lru_cache-1.6.4-pyhd8ed1ab_0\n", - " bleach conda-forge/noarch::bleach-4.1.0-pyhd8ed1ab_0\n", - " debugpy conda-forge/linux-64::debugpy-1.5.1-py37hcd2ae1e_0\n", - " decorator conda-forge/noarch::decorator-5.1.1-pyhd8ed1ab_0\n", - " defusedxml conda-forge/noarch::defusedxml-0.7.1-pyhd8ed1ab_0\n", - " entrypoints conda-forge/linux-64::entrypoints-0.3-py37hc8dfbb8_1002\n", - " flit-core conda-forge/noarch::flit-core-3.6.0-pyhd8ed1ab_0\n", - " importlib_resourc~ conda-forge/noarch::importlib_resources-5.4.0-pyhd8ed1ab_0\n", - " ipykernel conda-forge/linux-64::ipykernel-6.7.0-py37h6531663_0\n", - " ipython conda-forge/linux-64::ipython-7.31.0-py37h89c1867_0\n", - " ipython_genutils pkgs/main/linux-64::ipython_genutils-0.2.0-py37_0\n", - " ipywidgets pkgs/main/noarch::ipywidgets-7.6.5-pyhd3eb1b0_1\n", - " jedi conda-forge/linux-64::jedi-0.18.1-py37h89c1867_0\n", - " jinja2 conda-forge/noarch::jinja2-3.0.3-pyhd8ed1ab_0\n", - " jsonschema conda-forge/noarch::jsonschema-4.4.0-pyhd8ed1ab_0\n", - " jupyter_client conda-forge/noarch::jupyter_client-7.1.1-pyhd8ed1ab_0\n", - " jupyter_core conda-forge/linux-64::jupyter_core-4.9.1-py37h89c1867_1\n", - " jupyterlab_pygmen~ conda-forge/noarch::jupyterlab_pygments-0.1.2-pyh9f0ad1d_0\n", - " jupyterlab_widgets conda-forge/noarch::jupyterlab_widgets-1.0.2-pyhd8ed1ab_0\n", - " libsodium conda-forge/linux-64::libsodium-1.0.18-h516909a_1\n", - " markupsafe conda-forge/linux-64::markupsafe-2.0.1-py37h5e8e339_1\n", - " matplotlib-inline conda-forge/noarch::matplotlib-inline-0.1.3-pyhd8ed1ab_0\n", - " mistune conda-forge/linux-64::mistune-0.8.4-py37h5e8e339_1005\n", - " nbclient conda-forge/noarch::nbclient-0.5.10-pyhd8ed1ab_1\n", - " nbconvert conda-forge/linux-64::nbconvert-6.4.0-py37h89c1867_0\n", - " nbformat conda-forge/noarch::nbformat-5.1.3-pyhd8ed1ab_0\n", - " nest-asyncio conda-forge/noarch::nest-asyncio-1.5.4-pyhd8ed1ab_0\n", - " nglview conda-forge/noarch::nglview-3.0.3-pyh8a188c0_0\n", - " notebook conda-forge/noarch::notebook-6.4.7-pyha770c72_0\n", - " pandoc conda-forge/linux-64::pandoc-2.17-h7f98852_0\n", - " pandocfilters conda-forge/noarch::pandocfilters-1.5.0-pyhd8ed1ab_0\n", - " parso conda-forge/noarch::parso-0.8.3-pyhd8ed1ab_0\n", - " pexpect pkgs/main/linux-64::pexpect-4.8.0-py37_1\n", - " pickleshare conda-forge/linux-64::pickleshare-0.7.5-py37hc8dfbb8_1002\n", - " prometheus_client conda-forge/noarch::prometheus_client-0.12.0-pyhd8ed1ab_0\n", - " prompt-toolkit conda-forge/noarch::prompt-toolkit-3.0.24-pyha770c72_0\n", - " ptyprocess pkgs/main/noarch::ptyprocess-0.7.0-pyhd3eb1b0_2\n", - " pygments conda-forge/noarch::pygments-2.11.2-pyhd8ed1ab_0\n", - " pyrsistent pkgs/main/linux-64::pyrsistent-0.18.0-py37heee7806_0\n", - " pyzmq pkgs/main/linux-64::pyzmq-22.3.0-py37h295c915_2\n", - " send2trash pkgs/main/noarch::send2trash-1.8.0-pyhd3eb1b0_1\n", - " terminado conda-forge/linux-64::terminado-0.12.1-py37h89c1867_1\n", - " testpath conda-forge/noarch::testpath-0.5.0-pyhd8ed1ab_0\n", - " tornado conda-forge/linux-64::tornado-6.1-py37h5e8e339_2\n", - " traitlets conda-forge/noarch::traitlets-5.1.1-pyhd8ed1ab_0\n", - " wcwidth conda-forge/noarch::wcwidth-0.2.5-pyh9f0ad1d_2\n", - " webencodings pkgs/main/linux-64::webencodings-0.5.1-py37_1\n", - " widgetsnbextension conda-forge/linux-64::widgetsnbextension-3.5.2-py37h89c1867_1\n", - " zeromq conda-forge/linux-64::zeromq-4.3.4-h9c3ff4c_1\n", - "\n", - "\n", - "\n", - "Downloading and Extracting Packages\n", - "tornado-6.1 | 642 KB | : 100% 1.0/1 [00:00<00:00, 2.66it/s]\n", - "jinja2-3.0.3 | 99 KB | : 100% 1.0/1 [00:00<00:00, 13.90it/s]\n", - "pickleshare-0.7.5 | 13 KB | : 100% 1.0/1 [00:00<00:00, 22.77it/s]\n", - "defusedxml-0.7.1 | 23 KB | : 100% 1.0/1 [00:00<00:00, 20.54it/s]\n", - "webencodings-0.5.1 | 19 KB | : 100% 1.0/1 [00:00<00:00, 4.97it/s] \n", - "entrypoints-0.3 | 13 KB | : 100% 1.0/1 [00:00<00:00, 7.46it/s]\n", - "jedi-0.18.1 | 997 KB | : 100% 1.0/1 [00:00<00:00, 2.29it/s]\n", - "nbformat-5.1.3 | 47 KB | : 100% 1.0/1 [00:00<00:00, 12.54it/s]\n", - "pandocfilters-1.5.0 | 11 KB | : 100% 1.0/1 [00:00<00:00, 19.35it/s]\n", - "pyrsistent-0.18.0 | 95 KB | : 100% 1.0/1 [00:00<00:00, 9.47it/s]\n", - "testpath-0.5.0 | 86 KB | : 100% 1.0/1 [00:00<00:00, 16.62it/s]\n", - "traitlets-5.1.1 | 82 KB | : 100% 1.0/1 [00:00<00:00, 14.79it/s]\n", - "nbconvert-6.4.0 | 551 KB | : 100% 1.0/1 [00:00<00:00, 5.96it/s]\n", - "decorator-5.1.1 | 12 KB | : 100% 1.0/1 [00:00<00:00, 22.39it/s]\n", - "terminado-0.12.1 | 28 KB | : 100% 1.0/1 [00:00<00:00, 11.96it/s]\n", - "bleach-4.1.0 | 121 KB | : 100% 1.0/1 [00:00<00:00, 12.47it/s]\n", - "backports.functools_ | 9 KB | : 100% 1.0/1 [00:00<00:00, 22.97it/s]\n", - "widgetsnbextension-3 | 1.3 MB | : 100% 1.0/1 [00:00<00:00, 3.04it/s]\n", - "argon2-cffi-21.3.0 | 15 KB | : 100% 1.0/1 [00:00<00:00, 21.98it/s]\n", - "pyzmq-22.3.0 | 465 KB | : 100% 1.0/1 [00:00<00:00, 7.63it/s]\n", - "pandoc-2.17 | 12.7 MB | : 100% 1.0/1 [00:02<00:00, 2.84s/it]\n", - "mistune-0.8.4 | 54 KB | : 100% 1.0/1 [00:00<00:00, 12.68it/s]\n", - "jupyter_client-7.1.1 | 90 KB | : 100% 1.0/1 [00:00<00:00, 14.72it/s]\n", - "ipywidgets-7.6.5 | 105 KB | : 100% 1.0/1 [00:00<00:00, 11.74it/s]\n", - "ipython_genutils-0.2 | 39 KB | : 100% 1.0/1 [00:00<00:00, 11.64it/s]\n", - "ipython-7.31.0 | 1.1 MB | : 100% 1.0/1 [00:00<00:00, 3.30it/s]\n", - "backports-1.0 | 4 KB | : 100% 1.0/1 [00:00<00:00, 11.90it/s]\n", - "jsonschema-4.4.0 | 57 KB | : 100% 1.0/1 [00:00<00:00, 17.35it/s]\n", - "notebook-6.4.7 | 6.2 MB | : 100% 1.0/1 [00:01<00:00, 1.28s/it]\n", - "nest-asyncio-1.5.4 | 9 KB | : 100% 1.0/1 [00:00<00:00, 14.87it/s]\n", - "debugpy-1.5.1 | 2.0 MB | : 100% 1.0/1 [00:00<00:00, 1.88it/s]\n", - "prometheus_client-0. | 47 KB | : 100% 1.0/1 [00:00<00:00, 15.98it/s]\n", - "parso-0.8.3 | 69 KB | : 100% 1.0/1 [00:00<00:00, 17.95it/s]\n", - "backcall-0.2.0 | 13 KB | : 100% 1.0/1 [00:00<00:00, 26.19it/s]\n", - "flit-core-3.6.0 | 42 KB | : 100% 1.0/1 [00:00<00:00, 17.76it/s]\n", - "matplotlib-inline-0. | 11 KB | : 100% 1.0/1 [00:00<00:00, 23.11it/s]\n", - "wcwidth-0.2.5 | 33 KB | : 100% 1.0/1 [00:00<00:00, 16.48it/s]\n", - "importlib_resources- | 21 KB | : 100% 1.0/1 [00:00<00:00, 19.23it/s]\n", - "prompt-toolkit-3.0.2 | 249 KB | : 100% 1.0/1 [00:00<00:00, 9.49it/s]\n", - "libsodium-1.0.18 | 366 KB | : 100% 1.0/1 [00:00<00:00, 8.08it/s]\n", - "pexpect-4.8.0 | 77 KB | : 100% 1.0/1 [00:00<00:00, 5.99it/s] \n", - "ptyprocess-0.7.0 | 17 KB | : 100% 1.0/1 [00:00<00:00, 11.52it/s]\n", - "pygments-2.11.2 | 796 KB | : 100% 1.0/1 [00:00<00:00, 4.38it/s]\n", - "ipykernel-6.7.0 | 179 KB | : 100% 1.0/1 [00:00<00:00, 11.30it/s]\n", - "send2trash-1.8.0 | 19 KB | : 100% 1.0/1 [00:00<00:00, 11.25it/s]\n", - "nbclient-0.5.10 | 63 KB | : 100% 1.0/1 [00:00<00:00, 15.61it/s]\n", - "nglview-3.0.3 | 6.3 MB | : 100% 1.0/1 [00:01<00:00, 1.18s/it] \n", - "jupyterlab_pygments- | 8 KB | : 100% 1.0/1 [00:00<00:00, 22.59it/s]\n", - "jupyterlab_widgets-1 | 130 KB | : 100% 1.0/1 [00:00<00:00, 12.47it/s]\n", - "argon2-cffi-bindings | 34 KB | : 100% 1.0/1 [00:00<00:00, 17.36it/s]\n", - "markupsafe-2.0.1 | 22 KB | : 100% 1.0/1 [00:00<00:00, 19.46it/s]\n", - "zeromq-4.3.4 | 351 KB | : 100% 1.0/1 [00:00<00:00, 7.88it/s]\n", - "jupyter_core-4.9.1 | 80 KB | : 100% 1.0/1 [00:00<00:00, 15.37it/s]\n", - "Preparing transaction: - \b\b\\ \b\b| \b\bdone\n", - "Verifying transaction: - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n", - "Executing transaction: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- Enabling notebook extension jupyter-js-widgets/extension...\n", - "Paths used for configuration of notebook: \n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.d/nglview-js-widgets.json\n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.d/plotlywidget.json\n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.json\n", - "Paths used for configuration of notebook: \n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.d/nglview-js-widgets.json\n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.d/plotlywidget.json\n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\n", - " - Validating: \u001b[32mOK\u001b[0m\n", - "Paths used for configuration of notebook: \n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.d/nglview-js-widgets.json\n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.d/plotlywidget.json\n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\n", - " \t/usr/local/etc/jupyter/nbconfig/notebook.json\n", - "\n", - "\b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n" - ] - } - ], - "source": [ - "!mamba install -c conda-forge openff-toolkit openmm nglview --yes" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Was your OpenEye license correctly installed (True/False)? True\n" + ] + } + ], + "source": [ + "license_filename = '/content/drive/MyDrive/oe_license.txt'\n", + "import openeye\n", + "\n", + "import os\n", + "if os.path.isfile(license_filename):\n", + " license_file = open(license_filename, 'r')\n", + " openeye.OEAddLicenseData(license_file.read())\n", + " license_file.close()\n", + "else:\n", + " print(\"Error: Your OpenEye license is not readable; please check your filename and that you have mounted your Google Drive\")\n", + "\n", + "licensed = openeye.oechem.OEChemIsLicensed()\n", + "print(\"Was your OpenEye license correctly installed (True/False)? \" + str(licensed))\n", + "if not licensed:\n", + " print(\"Error: Your OpenEye license is not correctly installed.\")\n", + " raise Exception(\"Error: Your OpenEye license is not correctly installed.\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hJ9oc_3AqGZv" + }, + "source": [ + "#### Now(for Colab) we conda install the other prerequisites" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "vU4oSJrbqLDb", + "outputId": "e1bbbb21-afbd-4af8-ba82-ec525e9c6f85" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "_zprREAOorKO" - }, - "source": [ - "### Preparation for local use\n", - "\n", - "For local use, you need to ensure you are operating in a Python environment with the OpenEye toolkits (and license), the `openff-toolkit`, and `openmm`, the latter two of which are available from conda-forge." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + " __ __ __ __\n", + " / \\ / \\ / \\ / \\\n", + " / \\/ \\/ \\/ \\\n", + "███████████████/ /██/ /██/ /██/ /████████████████████████\n", + " / / \\ / \\ / \\ / \\ \\____\n", + " / / \\_/ \\_/ \\_/ \\ o \\__,\n", + " / _/ \\_____/ `\n", + " |/\n", + " ███╗ ███╗ █████╗ ███╗ ███╗██████╗ █████╗\n", + " ████╗ ████║██╔══██╗████╗ ████║██╔══██╗██╔══██╗\n", + " ██╔████╔██║███████║██╔████╔██║██████╔╝███████║\n", + " ██║╚██╔╝██║██╔══██║██║╚██╔╝██║██╔══██╗██╔══██║\n", + " ██║ ╚═╝ ██║██║ ██║██║ ╚═╝ ██║██████╔╝██║ ██║\n", + " ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝╚═════╝ ╚═╝ ╚═╝\n", + "\n", + " Supported by @QuantStack\n", + "\n", + " GitHub: https://github.com/QuantStack/mamba\n", + " Twitter: https://twitter.com/QuantStack\n", + "\n", + "█████████████████████████████████████████████████████████████\n", + "\n", + "Getting conda-forge linux-64\n", + "Getting conda-forge noarch\n", + "Getting pkgs/main linux-64\n", + "Getting pkgs/main noarch\n", + "Getting pkgs/r linux-64\n", + "Getting pkgs/r noarch\n", + "\n", + "Looking for: ['openff-toolkit', 'openmm', 'nglview', 'python 3.7.10']\n", + "\n", + "235541 packages in https://conda.anaconda.org/conda-forge/linux-64\n", + "79365 packages in https://conda.anaconda.org/conda-forge/noarch\n", + "24262 packages in https://repo.anaconda.com/pkgs/main/linux-64\n", + "4551 packages in https://repo.anaconda.com/pkgs/main/noarch\n", + "6637 packages in https://repo.anaconda.com/pkgs/r/linux-64\n", + "5025 packages in https://repo.anaconda.com/pkgs/r/noarch\n", + "\n", + "## Package Plan ##\n", + "\n", + " environment location: /usr/local\n", + "\n", + " added / updated specs:\n", + " - nglview\n", + " - openff-toolkit\n", + " - openmm\n", + " - python==3.7.10\n", + "\n", + "\n", + "The following packages will be downloaded:\n", + "\n", + " package | build\n", + " ---------------------------|-----------------\n", + " argon2-cffi-21.3.0 | pyhd8ed1ab_0 15 KB conda-forge\n", + " argon2-cffi-bindings-21.2.0| py37h5e8e339_1 34 KB conda-forge\n", + " backcall-0.2.0 | pyhd3eb1b0_0 13 KB\n", + " backports-1.0 | py37_1 4 KB\n", + " backports.functools_lru_cache-1.6.4| pyhd8ed1ab_0 9 KB conda-forge\n", + " bleach-4.1.0 | pyhd8ed1ab_0 121 KB conda-forge\n", + " debugpy-1.5.1 | py37hcd2ae1e_0 2.0 MB conda-forge\n", + " decorator-5.1.1 | pyhd8ed1ab_0 12 KB conda-forge\n", + " defusedxml-0.7.1 | pyhd8ed1ab_0 23 KB conda-forge\n", + " entrypoints-0.3 |py37hc8dfbb8_1002 13 KB conda-forge\n", + " flit-core-3.6.0 | pyhd8ed1ab_0 42 KB conda-forge\n", + " importlib_resources-5.4.0 | pyhd8ed1ab_0 21 KB conda-forge\n", + " ipykernel-6.7.0 | py37h6531663_0 179 KB conda-forge\n", + " ipython-7.31.0 | py37h89c1867_0 1.1 MB conda-forge\n", + " ipython_genutils-0.2.0 | py37_0 39 KB\n", + " ipywidgets-7.6.5 | pyhd3eb1b0_1 105 KB\n", + " jedi-0.18.1 | py37h89c1867_0 997 KB conda-forge\n", + " jinja2-3.0.3 | pyhd8ed1ab_0 99 KB conda-forge\n", + " jsonschema-4.4.0 | pyhd8ed1ab_0 57 KB conda-forge\n", + " jupyter_client-7.1.1 | pyhd8ed1ab_0 90 KB conda-forge\n", + " jupyter_core-4.9.1 | py37h89c1867_1 80 KB conda-forge\n", + " jupyterlab_pygments-0.1.2 | pyh9f0ad1d_0 8 KB conda-forge\n", + " jupyterlab_widgets-1.0.2 | pyhd8ed1ab_0 130 KB conda-forge\n", + " libsodium-1.0.18 | h516909a_1 366 KB conda-forge\n", + " markupsafe-2.0.1 | py37h5e8e339_1 22 KB conda-forge\n", + " matplotlib-inline-0.1.3 | pyhd8ed1ab_0 11 KB conda-forge\n", + " mistune-0.8.4 |py37h5e8e339_1005 54 KB conda-forge\n", + " nbclient-0.5.10 | pyhd8ed1ab_1 63 KB conda-forge\n", + " nbconvert-6.4.0 | py37h89c1867_0 551 KB conda-forge\n", + " nbformat-5.1.3 | pyhd8ed1ab_0 47 KB conda-forge\n", + " nest-asyncio-1.5.4 | pyhd8ed1ab_0 9 KB conda-forge\n", + " nglview-3.0.3 | pyh8a188c0_0 6.3 MB conda-forge\n", + " notebook-6.4.7 | pyha770c72_0 6.2 MB conda-forge\n", + " pandoc-2.17 | h7f98852_0 12.7 MB conda-forge\n", + " pandocfilters-1.5.0 | pyhd8ed1ab_0 11 KB conda-forge\n", + " parso-0.8.3 | pyhd8ed1ab_0 69 KB conda-forge\n", + " pexpect-4.8.0 | py37_1 77 KB\n", + " pickleshare-0.7.5 |py37hc8dfbb8_1002 13 KB conda-forge\n", + " prometheus_client-0.12.0 | pyhd8ed1ab_0 47 KB conda-forge\n", + " prompt-toolkit-3.0.24 | pyha770c72_0 249 KB conda-forge\n", + " ptyprocess-0.7.0 | pyhd3eb1b0_2 17 KB\n", + " pygments-2.11.2 | pyhd8ed1ab_0 796 KB conda-forge\n", + " pyrsistent-0.18.0 | py37heee7806_0 95 KB\n", + " pyzmq-22.3.0 | py37h295c915_2 465 KB\n", + " send2trash-1.8.0 | pyhd3eb1b0_1 19 KB\n", + " terminado-0.12.1 | py37h89c1867_1 28 KB conda-forge\n", + " testpath-0.5.0 | pyhd8ed1ab_0 86 KB conda-forge\n", + " tornado-6.1 | py37h5e8e339_2 642 KB conda-forge\n", + " traitlets-5.1.1 | pyhd8ed1ab_0 82 KB conda-forge\n", + " wcwidth-0.2.5 | pyh9f0ad1d_2 33 KB conda-forge\n", + " webencodings-0.5.1 | py37_1 19 KB\n", + " widgetsnbextension-3.5.2 | py37h89c1867_1 1.3 MB conda-forge\n", + " zeromq-4.3.4 | h9c3ff4c_1 351 KB conda-forge\n", + " ------------------------------------------------------------\n", + " Total: 35.8 MB\n", + "\n", + "The following NEW packages will be INSTALLED:\n", + "\n", + " argon2-cffi conda-forge/noarch::argon2-cffi-21.3.0-pyhd8ed1ab_0\n", + " argon2-cffi-bindi~ conda-forge/linux-64::argon2-cffi-bindings-21.2.0-py37h5e8e339_1\n", + " backcall pkgs/main/noarch::backcall-0.2.0-pyhd3eb1b0_0\n", + " backports pkgs/main/linux-64::backports-1.0-py37_1\n", + " backports.functoo~ conda-forge/noarch::backports.functools_lru_cache-1.6.4-pyhd8ed1ab_0\n", + " bleach conda-forge/noarch::bleach-4.1.0-pyhd8ed1ab_0\n", + " debugpy conda-forge/linux-64::debugpy-1.5.1-py37hcd2ae1e_0\n", + " decorator conda-forge/noarch::decorator-5.1.1-pyhd8ed1ab_0\n", + " defusedxml conda-forge/noarch::defusedxml-0.7.1-pyhd8ed1ab_0\n", + " entrypoints conda-forge/linux-64::entrypoints-0.3-py37hc8dfbb8_1002\n", + " flit-core conda-forge/noarch::flit-core-3.6.0-pyhd8ed1ab_0\n", + " importlib_resourc~ conda-forge/noarch::importlib_resources-5.4.0-pyhd8ed1ab_0\n", + " ipykernel conda-forge/linux-64::ipykernel-6.7.0-py37h6531663_0\n", + " ipython conda-forge/linux-64::ipython-7.31.0-py37h89c1867_0\n", + " ipython_genutils pkgs/main/linux-64::ipython_genutils-0.2.0-py37_0\n", + " ipywidgets pkgs/main/noarch::ipywidgets-7.6.5-pyhd3eb1b0_1\n", + " jedi conda-forge/linux-64::jedi-0.18.1-py37h89c1867_0\n", + " jinja2 conda-forge/noarch::jinja2-3.0.3-pyhd8ed1ab_0\n", + " jsonschema conda-forge/noarch::jsonschema-4.4.0-pyhd8ed1ab_0\n", + " jupyter_client conda-forge/noarch::jupyter_client-7.1.1-pyhd8ed1ab_0\n", + " jupyter_core conda-forge/linux-64::jupyter_core-4.9.1-py37h89c1867_1\n", + " jupyterlab_pygmen~ conda-forge/noarch::jupyterlab_pygments-0.1.2-pyh9f0ad1d_0\n", + " jupyterlab_widgets conda-forge/noarch::jupyterlab_widgets-1.0.2-pyhd8ed1ab_0\n", + " libsodium conda-forge/linux-64::libsodium-1.0.18-h516909a_1\n", + " markupsafe conda-forge/linux-64::markupsafe-2.0.1-py37h5e8e339_1\n", + " matplotlib-inline conda-forge/noarch::matplotlib-inline-0.1.3-pyhd8ed1ab_0\n", + " mistune conda-forge/linux-64::mistune-0.8.4-py37h5e8e339_1005\n", + " nbclient conda-forge/noarch::nbclient-0.5.10-pyhd8ed1ab_1\n", + " nbconvert conda-forge/linux-64::nbconvert-6.4.0-py37h89c1867_0\n", + " nbformat conda-forge/noarch::nbformat-5.1.3-pyhd8ed1ab_0\n", + " nest-asyncio conda-forge/noarch::nest-asyncio-1.5.4-pyhd8ed1ab_0\n", + " nglview conda-forge/noarch::nglview-3.0.3-pyh8a188c0_0\n", + " notebook conda-forge/noarch::notebook-6.4.7-pyha770c72_0\n", + " pandoc conda-forge/linux-64::pandoc-2.17-h7f98852_0\n", + " pandocfilters conda-forge/noarch::pandocfilters-1.5.0-pyhd8ed1ab_0\n", + " parso conda-forge/noarch::parso-0.8.3-pyhd8ed1ab_0\n", + " pexpect pkgs/main/linux-64::pexpect-4.8.0-py37_1\n", + " pickleshare conda-forge/linux-64::pickleshare-0.7.5-py37hc8dfbb8_1002\n", + " prometheus_client conda-forge/noarch::prometheus_client-0.12.0-pyhd8ed1ab_0\n", + " prompt-toolkit conda-forge/noarch::prompt-toolkit-3.0.24-pyha770c72_0\n", + " ptyprocess pkgs/main/noarch::ptyprocess-0.7.0-pyhd3eb1b0_2\n", + " pygments conda-forge/noarch::pygments-2.11.2-pyhd8ed1ab_0\n", + " pyrsistent pkgs/main/linux-64::pyrsistent-0.18.0-py37heee7806_0\n", + " pyzmq pkgs/main/linux-64::pyzmq-22.3.0-py37h295c915_2\n", + " send2trash pkgs/main/noarch::send2trash-1.8.0-pyhd3eb1b0_1\n", + " terminado conda-forge/linux-64::terminado-0.12.1-py37h89c1867_1\n", + " testpath conda-forge/noarch::testpath-0.5.0-pyhd8ed1ab_0\n", + " tornado conda-forge/linux-64::tornado-6.1-py37h5e8e339_2\n", + " traitlets conda-forge/noarch::traitlets-5.1.1-pyhd8ed1ab_0\n", + " wcwidth conda-forge/noarch::wcwidth-0.2.5-pyh9f0ad1d_2\n", + " webencodings pkgs/main/linux-64::webencodings-0.5.1-py37_1\n", + " widgetsnbextension conda-forge/linux-64::widgetsnbextension-3.5.2-py37h89c1867_1\n", + " zeromq conda-forge/linux-64::zeromq-4.3.4-h9c3ff4c_1\n", + "\n", + "\n", + "\n", + "Downloading and Extracting Packages\n", + "tornado-6.1 | 642 KB | : 100% 1.0/1 [00:00<00:00, 2.66it/s]\n", + "jinja2-3.0.3 | 99 KB | : 100% 1.0/1 [00:00<00:00, 13.90it/s]\n", + "pickleshare-0.7.5 | 13 KB | : 100% 1.0/1 [00:00<00:00, 22.77it/s]\n", + "defusedxml-0.7.1 | 23 KB | : 100% 1.0/1 [00:00<00:00, 20.54it/s]\n", + "webencodings-0.5.1 | 19 KB | : 100% 1.0/1 [00:00<00:00, 4.97it/s] \n", + "entrypoints-0.3 | 13 KB | : 100% 1.0/1 [00:00<00:00, 7.46it/s]\n", + "jedi-0.18.1 | 997 KB | : 100% 1.0/1 [00:00<00:00, 2.29it/s]\n", + "nbformat-5.1.3 | 47 KB | : 100% 1.0/1 [00:00<00:00, 12.54it/s]\n", + "pandocfilters-1.5.0 | 11 KB | : 100% 1.0/1 [00:00<00:00, 19.35it/s]\n", + "pyrsistent-0.18.0 | 95 KB | : 100% 1.0/1 [00:00<00:00, 9.47it/s]\n", + "testpath-0.5.0 | 86 KB | : 100% 1.0/1 [00:00<00:00, 16.62it/s]\n", + "traitlets-5.1.1 | 82 KB | : 100% 1.0/1 [00:00<00:00, 14.79it/s]\n", + "nbconvert-6.4.0 | 551 KB | : 100% 1.0/1 [00:00<00:00, 5.96it/s]\n", + "decorator-5.1.1 | 12 KB | : 100% 1.0/1 [00:00<00:00, 22.39it/s]\n", + "terminado-0.12.1 | 28 KB | : 100% 1.0/1 [00:00<00:00, 11.96it/s]\n", + "bleach-4.1.0 | 121 KB | : 100% 1.0/1 [00:00<00:00, 12.47it/s]\n", + "backports.functools_ | 9 KB | : 100% 1.0/1 [00:00<00:00, 22.97it/s]\n", + "widgetsnbextension-3 | 1.3 MB | : 100% 1.0/1 [00:00<00:00, 3.04it/s]\n", + "argon2-cffi-21.3.0 | 15 KB | : 100% 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\t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\n", + " \t/usr/local/etc/jupyter/nbconfig/notebook.json\n", + "Paths used for configuration of notebook: \n", + " \t/usr/local/etc/jupyter/nbconfig/notebook.d/nglview-js-widgets.json\n", + " \t/usr/local/etc/jupyter/nbconfig/notebook.d/plotlywidget.json\n", + " \t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\n", + " - Validating: \u001b[32mOK\u001b[0m\n", + "Paths used for configuration of notebook: \n", + " \t/usr/local/etc/jupyter/nbconfig/notebook.d/nglview-js-widgets.json\n", + " \t/usr/local/etc/jupyter/nbconfig/notebook.d/plotlywidget.json\n", + " \t/usr/local/etc/jupyter/nbconfig/notebook.d/widgetsnbextension.json\n", + " \t/usr/local/etc/jupyter/nbconfig/notebook.json\n", + "\n", + "\b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n" + ] + } + ], + "source": [ + "!mamba install -c conda-forge openff-toolkit openmm nglview --yes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_zprREAOorKO" + }, + "source": [ + "### Preparation for local use\n", + "\n", + "For local use, you need to ensure you are operating in a Python environment with the OpenEye toolkits (and license), the `openff-toolkit`, and `openmm`, the latter two of which are available from conda-forge." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "id": "OdeYv0O4-E6Q" + }, + "source": [ + "# Now let's do MD on a molecule!\n", + "\n", + "In the other sandbox associated with this lecture, we did some very simple MD on a 1D Lennard-Jones system. Now let's head towards the opposite extreme and do our first molecular system.\n", + "\n", + "Here, we'll draw on OpenMM, a molecular mechanics toolkit we'll see again later in the class, for running some simple energy minimizations and molecular dynamics. And we'll use the `openforcefield` tools for assigning a force field for a molecule, because they make this very simple (and you have them already installed).\n", + "\n", + "## First, we generate a molecule" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 182 }, + "id": "xETKwpnk-E6Q", + "outputId": "91968216-8582-4f29-84ad-1d65a73f1fb3" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "collapsed": true, - "id": "OdeYv0O4-E6Q" - }, - "source": [ - "# Now let's do MD on a molecule!\n", - "\n", - "In the other sandbox associated with this lecture, we did some very simple MD on a 1D Lennard-Jones system. Now let's head towards the opposite extreme and do our first molecular system.\n", - "\n", - "Here, we'll draw on OpenMM, a molecular mechanics toolkit we'll see again later in the class, for running some simple energy minimizations and molecular dynamics. And we'll use the `openforcefield` tools for assigning a force field for a molecule, because they make this very simple (and you have them already installed).\n", - "\n", - "## First, we generate a molecule" + "data": { + "image/png": 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", + "text/plain": [ + "" ] + }, + "execution_count": 1, + "metadata": { + "image/png": { + "width": 200 + } + }, + "output_type": "execute_result" + } + ], + "source": [ + "#@title\n", + "# What SMILES string for the guest? Should be isomeric SMILES\n", + "mol_smiles = 'OC(CC1CCCC1)=O'\n", + "\n", + "# Import stuff\n", + "from openeye.oechem import *\n", + "from openeye.oeomega import * # conformer generation\n", + "from openeye.oequacpac import * #for partial charge assignment\n", + "\n", + "# Create empty OEMol\n", + "mol = OEMol()\n", + "# Convert SMILES\n", + "OESmilesToMol(mol, mol_smiles)\n", + "\n", + "# Draw to make sure that's what we want\n", + "from IPython.display import Image\n", + "import openeye.oedepict as oedepict # Use OpenEye depiction toolkit\n", + "oedepict.OEPrepareDepiction(mol)\n", + "oedepict.OERenderMolecule(\"DepictMolSimple.png\", mol)\n", + "Image('DepictMolSimple.png',width = 200)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true, + "id": "BN4yBmxf-E6R" + }, + "source": [ + "## For some use cases, we would begin with a structure of our molecule\n", + "\n", + "We're about to jump into the OpenFF toolkit and prep our molecule for simulation. We could either begin with the molecule in a format which had coordinates (like .mol2 or .sdf) if we were starting from a conformer we wanted to use, or we could let the toolkit generate a conformer for us. Here, since we aren't starting with a conformer we want to use, we will let the toolkit generate an initial conformer. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ikHZZBQo-E6R" + }, + "source": [ + "## We apply a force field to assign parameters to the system\n", + "\n", + "This uses the `openff-toolkit` from Open Force Field, along with the `openff-2.0.0` Parsley force field." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "S53dPRDd-E6S" + }, + "outputs": [], + "source": [ + "from openff.toolkit.topology import Topology, Molecule\n", + "from openff.toolkit.typing.engines.smirnoff import ForceField\n", + "from openff.units import unit\n", + "\n", + "# Get force field ready\n", + "ff = ForceField('openff-2.0.0.offxml')\n", + "\n", + "# Generate an OpenFF molecule from the SMILES\n", + "offmol = Molecule.from_smiles(mol_smiles)\n", + "\n", + "# Generate a conformer, using whatever cheminformatics \"backend\" the toolkit is using (OpenEye or RDKit in general; here OpenEye)\n", + "offmol.generate_conformers(n_conformers=1)\n", + "\n", + "# Draw the OpenFF molecule to make sure it looks OK\n", + "offmol.visualize()\n", + "\n", + "# Create an empty OpenFF Topology \n", + "topology = Topology()\n", + "\n", + "# Add our molecule to the Topology (a Topology can contain multiple molecules)\n", + "molecule_topology_indices = topology.add_molecule(offmol)\n", + "\n", + "#Place in 4 nm box\n", + "import numpy as np\n", + "topology.box_vectors = np.eye(3) * 4 * unit.nanometer\n", + "\n", + "# Parametrize the topology and create an OpenMM System.\n", + "system = ff.create_openmm_system(topology)\n", + "\n", + "# Generate OpenMM topology for use setting up simulation\n", + "omm_topology = topology.to_openmm()\n", + "\n", + "# Write to a file for convenience\n", + "offmol.to_file('molecule.mol2', file_format='mol2')\n", + "offmol.to_file('molecule.pdb', file_format='pdb')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zjnHs_D9-E6S" + }, + "source": [ + "## Next we'll start off by doing an energy minimization\n", + "\n", + "We first prepare the system, but then the actual energy minimization is very simple. I believe OpenMM is using an L-BFGS minimizer." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "_iGv7Og6-E6S", + "outputId": "c76b46b7-58f8-4d1c-af1b-54aee98c9496" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 182 - }, - "id": "xETKwpnk-E6Q", - "outputId": "91968216-8582-4f29-84ad-1d65a73f1fb3" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": { - "image/png": { - "width": 200 - } - }, - "execution_count": 5 - } - ], - "source": [ - "#@title\n", - "# What SMILES string for the guest? Should be isomeric SMILES\n", - "mol_smiles = 'OC(CC1CCCC1)=O'\n", - "\n", - "# Import stuff\n", - "from openeye.oechem import *\n", - "from openeye.oeomega import * # conformer generation\n", - "from openeye.oequacpac import * #for partial charge assignment\n", - "\n", - "# Create empty OEMol\n", - "mol = OEMol()\n", - "# Convert SMILES\n", - "OESmilesToMol(mol, mol_smiles)\n", - "\n", - "# Draw to make sure that's what we want\n", - "from IPython.display import Image\n", - "import openeye.oedepict as oedepict # Use OpenEye depiction toolkit\n", - "oedepict.OEPrepareDepiction(mol)\n", - "oedepict.OERenderMolecule(\"DepictMolSimple.png\", mol)\n", - "Image('DepictMolSimple.png',width = 200)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Energy before minimization (kcal/mol): 9.1\n", + "Energy after minimization (kcal/mol): 5.3\n" + ] + } + ], + "source": [ + "# Import openmm stuff\n", + "import openmm\n", + "try:\n", + " import openmm\n", + " from openmm import unit as omm_unit\n", + "except ImportError:\n", + " from simtk import openmm\n", + " from simtk import unit as omm_unit\n", + "\n", + "# Even though we're just going to minimize, we still have to set up an integrator, since a Simulation needs one\n", + "integrator = openmm.VerletIntegrator(2.0*omm_unit.femtoseconds)\n", + "# Prep the Simulation using the parameterized system, the integrator, and the topology\n", + "simulation = openmm.app.Simulation(omm_topology, system, integrator)\n", + "\n", + "# Copy in the positions - here we use those from the OpenFF molecule we generated, but they could come from elsewhere\n", + "positions = offmol.conformers[0]\n", + "simulation.context.setPositions( positions.to_openmm()) \n", + "\n", + "# Get initial state and energy; print\n", + "state = simulation.context.getState(getEnergy = True)\n", + "energy = state.getPotentialEnergy() / omm_unit.kilocalories_per_mole\n", + "print(\"Energy before minimization (kcal/mol): %.2g\" % energy)\n", + "\n", + "# Minimize, get final state and energy and print\n", + "simulation.minimizeEnergy()\n", + "state = simulation.context.getState(getEnergy=True, getPositions=True)\n", + "energy = state.getPotentialEnergy() / omm_unit.kilocalories_per_mole\n", + "print(\"Energy after minimization (kcal/mol): %.2g\" % energy)\n", + "newpositions = state.getPositions()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JrOQsC_I-E6T" + }, + "source": [ + "## Now we do some bookkeeping to run MD\n", + "\n", + "Most of this is just setup, file bookkeeping, temperature selection, etc. The key part which actually runs it is the second-to-last block of code which says `simulation.step(1000)` which runs 1000 steps of dynamics. The timestep for this is set near the top, and is 2 femtoseconds per timestep, so the total time is 2000 fs, or 2 ps.\n", + "\n", + "Simulation snapshots are stored to a NetCDF file for later visualization, every 100 frames (so we'll end up with 10 of them). You can adjust these settings if you like." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "9bAyNURu-E6T", + "outputId": "bc24ce08-d73a-4011-d1bf-dbe086820195" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "collapsed": true, - "id": "BN4yBmxf-E6R" - }, - "source": [ - "## For some use cases, we would begin with a structure of our molecule\n", - "\n", - "We're about to jump into the OpenFF toolkit and prep our molecule for simulation. We could either begin with the molecule in a format which had coordinates (like .mol2 or .sdf) if we were starting from a conformer we wanted to use, or we could let the toolkit generate a conformer for us. Here, since we aren't starting with a conformer we want to use, we will let the toolkit generate an initial conformer. " - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/dmobley/mambaforge/envs/drugcomp25/lib/python3.12/site-packages/mdtraj/formats/netcdf.py:154: UserWarning: Warning: The 'netCDF4' Python package is not installed. MDTraj is using the 'scipy' implementation to read and write netCDF files,which can be significantly slower.\n", + "For improved performance, consider installing netCDF4. See installation instructions at:\n", + "https://unidata.github.io/netcdf4-python/#quick-install\n", + " warnings.warn(warning_message)\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "id": "ikHZZBQo-E6R" + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting simulation\n", + "Elapsed time 0.49 seconds\n", + "Done!\n" + ] + } + ], + "source": [ + "import os\n", + "from datetime import datetime\n", + "from mdtraj import reporters\n", + "\n", + "# Propagate the System with Langevin dynamics.\n", + "time_step = 2 * omm_unit.femtoseconds # simulation timestep\n", + "temperature = 300 * omm_unit.kelvin # simulation temperature\n", + "friction = 1 / omm_unit.picosecond # collision rate\n", + "integrator = openmm.LangevinIntegrator(temperature, friction, time_step)\n", + "\n", + "# Length of the simulation.\n", + "num_steps = 1000 # number of integration steps to run\n", + "\n", + "# Prep Simulation\n", + "simulation = openmm.app.Simulation(omm_topology, system, integrator)\n", + "# Copy in minimized positions\n", + "simulation.context.setPositions(newpositions)\n", + "\n", + "# Initialize velocities to correct temperature\n", + "simulation.context.setVelocitiesToTemperature(temperature)\n", + "\n", + "# Initialize reporters, including a CSV file to store certain stats every 100 frames\n", + "simulation.reporters.append(openmm.app.StateDataReporter(os.path.join('.', 'data.csv'), 100, step=True, potentialEnergy=True, temperature=True, density=True))\n", + "netcdf_reporter = reporters.NetCDFReporter(os.path.join('.', 'trajectory.nc'), 100) #Store every 100 frames\n", + "simulation.reporters.append(netcdf_reporter)\n", + "# Also store to a PDB for convenience\n", + "pdb_reporter = openmm.app.PDBReporter(\"trajectory.pdb\", 100) #store every 100 steps\n", + "simulation.reporters.append(pdb_reporter)\n", + "\n", + "# Run the simulation and print start info; store timing\n", + "print(\"Starting simulation\")\n", + "start = datetime.now()\n", + "simulation.step(num_steps) \n", + "end = datetime.now()\n", + "\n", + "# Print elapsed time info, finalize trajectory file\n", + "print(\"Elapsed time %.2f seconds\" % (end-start).total_seconds())\n", + "netcdf_reporter.close()\n", + "print(\"Done!\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xQohKeS_-E6T" + }, + "source": [ + "## Now we can visualize the results with `nglview`\n", + "\n", + "If you don't have `nglview`, you can also load the PDB file, `molecule.pdb`, with a standard viewer like VMD or Chimera, and then load the PDB trajectory file (`trajectory.pdb`) in afterwards. If `nglview` is not working on your platform, you can use `py3dmol` to load a static view of structures. (nglview can also load mol2 files, but I'm getting glitchy performance with the visualization beginning from a mol2).\n", + "\n", + "Currently nglview seems to be RUNNING on Colab but doesn't result in trajectories which look correct." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "IkIu5dE5zIO5" + }, + "outputs": [], + "source": [ + "#COLAB ONLY: First enable third party widgets\n", + "from google.colab import output\n", + "output.enable_custom_widget_manager()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 317, + "referenced_widgets": [ + "d0ff3f7e625448ef91c5bb962c746c2d", + "e7251f7ea94c4c95a1a023a590cd2d60", + "50af28a4d71f4a5a883fdc95a9467915", + "d6c61c09d9a744cf95345ef3f4576ccb", + "a0f47c4f6a8f4ef7b557a36d8c472c14", + "f82095b287894f9cbf9588711a86f3e6", + "18f37547860b4bcc87a77efefcb0e797", + "34c55a41b0e045d1a7fdce2a3f347ac4", + "9d499eae9c6f48eab85029147de2a510", + "818e1c3e5b8046f78dac1fb0cb394aae", + "885b7daaebd3496a950f572462cde31c", + "59f73309c61146ba98222ad05cddef47", + "3c00a6c3a8dc421794a0b683a3943c64" + ], + "resources": { + "http://localhost:8080/fonts/fontawesome-webfont.ttf?v=4.7.0": { + "data": "CjwhRE9DVFlQRSBodG1sPgo8aHRtbCBsYW5nPWVuPgogIDxtZXRhIGNoYXJzZXQ9dXRmLTg+CiAgPG1ldGEgbmFtZT12aWV3cG9ydCBjb250ZW50PSJpbml0aWFsLXNjYWxlPTEsIG1pbmltdW0tc2NhbGU9MSwgd2lkdGg9ZGV2aWNlLXdpZHRoIj4KICA8dGl0bGU+RXJyb3IgNDA0IChOb3QgRm91bmQpISExPC90aXRsZT4KICA8c3R5bGU+CiAgICAqe21hcmdpbjowO3BhZGRpbmc6MH1odG1sLGNvZGV7Zm9udDoxNXB4LzIycHggYXJpYWwsc2Fucy1zZXJpZn1odG1se2JhY2tncm91bmQ6I2ZmZjtjb2xvcjojMjIyO3BhZGRpbmc6MTVweH1ib2R5e21hcmdpbjo3JSBhdXRvIDA7bWF4LXdpZHRoOjM5MHB4O21pbi1oZWlnaHQ6MTgwcHg7cGFkZGluZzozMHB4IDAgMTVweH0qID4gYm9keXtiYWNrZ3JvdW5kOnVybCgvL3d3dy5nb29nbGUuY29tL2ltYWdlcy9lcnJvcnMvcm9ib3QucG5nKSAxMDAlIDVweCBuby1yZXBlYXQ7cGFkZGluZy1yaWdodDoyMDVweH1we21hcmdpbjoxMXB4IDAgMjJweDtvdmVyZmxvdzpoaWRkZW59aW5ze2NvbG9yOiM3Nzc7dGV4dC1kZWNvcmF0aW9uOm5vbmV9YSBpbWd7Ym9yZGVyOjB9QG1lZGlhIHNjcmVlbiBhbmQgKG1heC13aWR0aDo3NzJweCl7Ym9keXtiYWNrZ3JvdW5kOm5vbmU7bWFyZ2luLXRvcDowO21heC13aWR0aDpub25lO3BhZGRpbmctcmlnaHQ6MH19I2xvZ297YmFja2dyb3VuZDp1cmwoLy93d3cuZ29vZ2xlLmNvbS9pbWFnZXMvbG9nb3MvZXJyb3JwYWdlL2Vycm9yX2xvZ28tMTUweDU0LnBuZykgbm8tcmVwZWF0O21hcmdpbi1sZWZ0Oi01cHh9QG1lZGlhIG9ubHkgc2NyZWVuIGFuZCAobWluLXJlc29sdXRpb246MTkyZHBpKXsjbG9nb3tiYWNrZ3JvdW5kOnVybCgvL3d3dy5nb29nbGUuY29tL2ltYWdlcy9sb2dvcy9lcnJvcnBhZ2UvZXJyb3JfbG9nby0xNTB4NTQtMngucG5nKSBuby1yZXBlYXQgMCUgMCUvMTAwJSAxMDAlOy1tb3otYm9yZGVyLWltYWdlOnVybCgvL3d3dy5nb29nbGUuY29tL2ltYWdlcy9sb2dvcy9lcnJvcnBhZ2UvZXJyb3JfbG9nby0xNTB4NTQtMngucG5nKSAwfX1AbWVkaWEgb25seSBzY3JlZW4gYW5kICgtd2Via2l0LW1pbi1kZXZpY2UtcGl4ZWwtcmF0aW86Mil7I2xvZ297YmFja2dyb3VuZDp1cmwoLy93d3cuZ29vZ2xlLmNvbS9pbWFnZXMvbG9nb3MvZXJyb3JwYWdlL2Vycm9yX2xvZ28tMTUweDU0LTJ4LnBuZykgbm8tcmVwZWF0Oy13ZWJraXQtYmFja2dyb3VuZC1zaXplOjEwMCUgMTAwJX19I2xvZ297ZGlzcGxheTppbmxpbmUtYmxvY2s7aGVpZ2h0OjU0cHg7d2lkdGg6MTUwcHh9CiAgPC9zdHlsZT4KICA8YSBocmVmPS8vd3d3Lmdvb2dsZS5jb20vPjxzcGFuIGlkPWxvZ28gYXJpYS1sYWJlbD1Hb29nbGU+PC9zcGFuPjwvYT4KICA8cD48Yj40MDQuPC9iPiA8aW5zPlRoYXTigJlzIGFuIGVycm9yLjwvaW5zPgogIDxwPiAgPGlucz5UaGF04oCZcyBhbGwgd2Uga25vdy48L2lucz4K", + "headers": [ + [ + "content-length", + "1449" + ], + [ + "content-type", + "text/html; charset=utf-8" + ] + ], + "ok": false, + "status": 404, + "status_text": "" }, - "source": [ - "## We apply a force field to assign parameters to the system\n", - "\n", - "This uses the `openff-toolkit` from Open Force Field, along with the `openff-2.0.0` Parsley force field." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "S53dPRDd-E6S" + "http://localhost:8080/fonts/fontawesome-webfont.woff2?v=4.7.0": { + "data": "CjwhRE9DVFlQRSBodG1sPgo8aHRtbCBsYW5nPWVuPgogIDxtZXRhIGNoYXJzZXQ9dXRmLTg+CiAgPG1ldGEgbmFtZT12aWV3cG9ydCBjb250ZW50PSJpbml0aWFsLXNjYWxlPTEsIG1pbmltdW0tc2NhbGU9MSwgd2lkdGg9ZGV2aWNlLXdpZHRoIj4KICA8dGl0bGU+RXJyb3IgNDA0IChOb3QgRm91bmQpISExPC90aXRsZT4KICA8c3R5bGU+CiAgICAqe21hcmdpbjowO3BhZGRpbmc6MH1odG1sLGNvZGV7Zm9udDoxNXB4LzIycHggYXJpYWwsc2Fucy1zZXJpZn1odG1se2JhY2tncm91bmQ6I2ZmZjtjb2xvcjojMjIyO3BhZGRpbmc6MTVweH1ib2R5e21hcmdpbjo3JSBhdXRvIDA7bWF4LXdpZHRoOjM5MHB4O21pbi1oZWlnaHQ6MTgwcHg7cGFkZGluZzozMHB4IDAgMTVweH0qID4gYm9keXtiYWNrZ3JvdW5kOnVybCgvL3d3dy5nb29nbGUuY29tL2ltYWdlcy9lcnJvcnMvcm9ib3QucG5nKSAxMDAlIDVweCBuby1yZXBlYXQ7cGFkZGluZy1yaWdodDoyMDVweH1we21hcmdpbjoxMXB4IDAgMjJweDtvdmVyZmxvdzpoaWRkZW59aW5ze2NvbG9yOiM3Nzc7dGV4dC1kZWNvcmF0aW9uOm5vbmV9YSBpbWd7Ym9yZGVyOjB9QG1lZGlhIHNjcmVlbiBhbmQgKG1heC13aWR0aDo3NzJweCl7Ym9keXtiYWNrZ3JvdW5kOm5vbmU7bWFyZ2luLXRvcDowO21heC13aWR0aDpub25lO3BhZGRpbmctcmlnaHQ6MH19I2xvZ297YmFja2dyb3VuZDp1cmwoLy93d3cuZ29vZ2xlLmNvbS9pbWFnZXMvbG9nb3MvZXJyb3JwYWdlL2Vycm9yX2xvZ28tMTUweDU0LnBuZykgbm8tcmVwZWF0O21hcmdpbi1sZWZ0Oi01cHh9QG1lZGlhIG9ubHkgc2NyZWVuIGFuZCAobWluLXJlc29sdXRpb246MTkyZHBpKXsjbG9nb3tiYWNrZ3JvdW5kOnVybCgvL3d3dy5nb29nbGUuY29tL2ltYWdlcy9sb2dvcy9lcnJvcnBhZ2UvZXJyb3JfbG9nby0xNTB4NTQtMngucG5nKSBuby1yZXBlYXQgMCUgMCUvMTAwJSAxMDAlOy1tb3otYm9yZGVyLWltYWdlOnVybCgvL3d3dy5nb29nbGUuY29tL2ltYWdlcy9sb2dvcy9lcnJvcnBhZ2UvZXJyb3JfbG9nby0xNTB4NTQtMngucG5nKSAwfX1AbWVkaWEgb25seSBzY3JlZW4gYW5kICgtd2Via2l0LW1pbi1kZXZpY2UtcGl4ZWwtcmF0aW86Mil7I2xvZ297YmFja2dyb3VuZDp1cmwoLy93d3cuZ29vZ2xlLmNvbS9pbWFnZXMvbG9nb3MvZXJyb3JwYWdlL2Vycm9yX2xvZ28tMTUweDU0LTJ4LnBuZykgbm8tcmVwZWF0Oy13ZWJraXQtYmFja2dyb3VuZC1zaXplOjEwMCUgMTAwJX19I2xvZ297ZGlzcGxheTppbmxpbmUtYmxvY2s7aGVpZ2h0OjU0cHg7d2lkdGg6MTUwcHh9CiAgPC9zdHlsZT4KICA8YSBocmVmPS8vd3d3Lmdvb2dsZS5jb20vPjxzcGFuIGlkPWxvZ28gYXJpYS1sYWJlbD1Hb29nbGU+PC9zcGFuPjwvYT4KICA8cD48Yj40MDQuPC9iPiA8aW5zPlRoYXTigJlzIGFuIGVycm9yLjwvaW5zPgogIDxwPiAgPGlucz5UaGF04oCZcyBhbGwgd2Uga25vdy48L2lucz4K", + "headers": [ + [ + "content-length", + "1449" + ], + [ + "content-type", + "text/html; charset=utf-8" + ] + ], + "ok": false, + "status": 404, + "status_text": "" }, - "outputs": [], - "source": [ - "from openff.toolkit.topology import Topology, Molecule\n", - "from openff.toolkit.typing.engines.smirnoff import ForceField\n", - "from openmm import unit\n", - "\n", - "# Get force field ready\n", - "ff = ForceField('openff-2.0.0.offxml')\n", - "\n", - "# Generate an OpenFF molecule from the SMILES\n", - "offmol = Molecule.from_smiles(mol_smiles)\n", - "\n", - "# Generate a conformer, using whatever cheminformatics \"backend\" the toolkit is using (OpenEye or RDKit in general; here OpenEye)\n", - "offmol.generate_conformers(n_conformers=1)\n", - "\n", - "# Draw the OpenFF molecule to make sure it looks OK\n", - "offmol.visualize()\n", - "\n", - "# Create an empty OpenFF Topology \n", - "topology = Topology()\n", - "\n", - "# Add our molecule to the Topology (a Topology can contain multiple molecules)\n", - "molecule_topology_indices = topology.add_molecule(offmol)\n", - "\n", - "#Place in 4 nm box\n", - "import numpy as np\n", - "topology.box_vectors = np.eye(3) * 4 * unit.nanometer\n", - "\n", - "# Parametrize the topology and create an OpenMM System.\n", - "system = ff.create_openmm_system(topology)\n", - "\n", - "# Generate OpenMM topology for use setting up simulation\n", - "omm_topology = topology.to_openmm()\n", - "\n", - "# Write to a file for convenience\n", - "offmol.to_file('molecule.mol2', file_format='mol2')" - ] + "http://localhost:8080/fonts/fontawesome-webfont.woff?v=4.7.0": { + "data": "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", + "headers": [ + [ + "content-length", + "1449" + ], + [ + "content-type", + "text/html; charset=utf-8" + ] + ], + "ok": false, + "status": 404, + "status_text": "" + } + } }, + "id": "JRusCopR-E6U", + "outputId": "95fc59f0-e6ef-46ee-d1e1-ccfae448538c" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "zjnHs_D9-E6S" + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "389b0d2bbc374975b639c50b4ffe6831", + "version_major": 2, + "version_minor": 0 }, - "source": [ - "## Next we'll start off by doing an energy minimization\n", - "\n", - "We first prepare the system, but then the actual energy minimization is very simple. I believe OpenMM is using an L-BFGS minimizer." + "text/plain": [ + "NGLWidget(max_frame=9)" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Load stored trajectory using MDTraj; the trajectory doesn't contain chemistry info so we also load a PDB\n", + "import mdtraj\n", + "import nglview\n", + "traj= mdtraj.load(os.path.join('.', 'trajectory.nc'), top=os.path.join('.', 'molecule.pdb'))\n", + "\n", + "# View the trajectory\n", + "view = nglview.show_mdtraj(traj)\n", + "view" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mCezrgQq-E6U" + }, + "source": [ + "## Exercises\n", + "\n", + "- Try your own molecule. What about one which has the potential for internal hydrogen bonding?\n", + "- Run the dynamics longer\n", + "- What is different if you lower the temperature? Raise the temperature? " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "_PBHRj09zFLj" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "colab": { + "collapsed_sections": [], + "name": "Copy of MD_Sandbox_Molecule.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.8" + }, + "toc": { + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "toc_cell": false, + "toc_position": {}, + "toc_section_display": "block", + "toc_window_display": false + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "18f37547860b4bcc87a77efefcb0e797": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "_iGv7Og6-E6S", - "outputId": "c76b46b7-58f8-4d1c-af1b-54aee98c9496", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Energy before minimization (kcal/mol): 19\n", - "Energy after minimization (kcal/mol): 16\n" - ] - } - ], - "source": [ - "# Import openmm stuff\n", - "import openmm\n", - "try:\n", - " import openmm\n", - " from openmm import unit\n", - "except ImportError:\n", - " from simtk import openmm, unit\n", - "\n", - "# Even though we're just going to minimize, we still have to set up an integrator, since a Simulation needs one\n", - "integrator = openmm.VerletIntegrator(2.0*unit.femtoseconds)\n", - "# Prep the Simulation using the parameterized system, the integrator, and the topology\n", - "simulation = openmm.app.Simulation(omm_topology, system, integrator)\n", - "\n", - "# Copy in the positions - here we use those from the OpenFF molecule we generated, but they could come from elsewhere\n", - "positions = offmol.conformers[0]\n", - "simulation.context.setPositions( positions) \n", - "\n", - "# Get initial state and energy; print\n", - "state = simulation.context.getState(getEnergy = True)\n", - "energy = state.getPotentialEnergy() / unit.kilocalories_per_mole\n", - "print(\"Energy before minimization (kcal/mol): %.2g\" % energy)\n", - "\n", - "# Minimize, get final state and energy and print\n", - "simulation.minimizeEnergy()\n", - "state = simulation.context.getState(getEnergy=True, getPositions=True)\n", - "energy = state.getPotentialEnergy() / unit.kilocalories_per_mole\n", - "print(\"Energy after minimization (kcal/mol): %.2g\" % energy)\n", - "newpositions = state.getPositions()" - ] + "34c55a41b0e045d1a7fdce2a3f347ac4": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "JrOQsC_I-E6T" - }, - "source": [ - "## Now we do some bookkeeping to run MD\n", - "\n", - "Most of this is just setup, file bookkeeping, temperature selection, etc. The key part which actually runs it is the second-to-last block of code which says `simulation.step(1000)` which runs 1000 steps of dynamics. The timestep for this is set near the top, and is 2 femtoseconds per timestep, so the total time is 2000 fs, or 2 ps.\n", - "\n", - "Simulation snapshots are stored to a NetCDF file for later visualization, every 100 frames (so we'll end up with 10 of them). You can adjust these settings if you like." - ] + "3c00a6c3a8dc421794a0b683a3943c64": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "id": "9bAyNURu-E6T", - "outputId": "bc24ce08-d73a-4011-d1bf-dbe086820195", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Starting simulation\n", - "Elapsed time 2.85 seconds\n", - "Done!\n" - ] - } + "50af28a4d71f4a5a883fdc95a9467915": { + "model_module": "nglview-js-widgets", + "model_module_version": "3.0.1", + "model_name": "NGLModel", + "state": { + "_camera_orientation": [ + 22.182761659099427, + 0, + 0, + 0, + 0, + 22.182761659099427, + 0, + 0, + 0, + 0, + 22.182761659099427, + 0, + -39.49099922180176, + -38.68149948120117, + -36.91849899291992, + 1 ], - "source": [ - "import os\n", - "from datetime import datetime\n", - "from mdtraj import reporters\n", - "\n", - "# Propagate the System with Langevin dynamics.\n", - "time_step = 2 * unit.femtoseconds # simulation timestep\n", - "temperature = 300 * unit.kelvin # simulation temperature\n", - "friction = 1 / unit.picosecond # collision rate\n", - "integrator = openmm.LangevinIntegrator(temperature, friction, time_step)\n", - "\n", - "# Length of the simulation.\n", - "num_steps = 1000 # number of integration steps to run\n", - "\n", - "# Prep Simulation\n", - "simulation = openmm.app.Simulation(omm_topology, system, integrator)\n", - "# Copy in minimized positions\n", - "simulation.context.setPositions(newpositions)\n", - "\n", - "# Initialize velocities to correct temperature\n", - "simulation.context.setVelocitiesToTemperature(temperature)\n", - "\n", - "# Initialize reporters, including a CSV file to store certain stats every 100 frames\n", - "simulation.reporters.append(openmm.app.StateDataReporter(os.path.join('.', 'data.csv'), 100, step=True, potentialEnergy=True, temperature=True, density=True))\n", - "netcdf_reporter = reporters.NetCDFReporter(os.path.join('.', 'trajectory.nc'), 100) #Store every 100 frames\n", - "simulation.reporters.append(netcdf_reporter)\n", - "# Also store to a PDB for convenience\n", - "pdb_reporter = openmm.app.PDBReporter(\"trajectory.pdb\", 100) #store every 100 steps\n", - "simulation.reporters.append(pdb_reporter)\n", - "\n", - "# Run the simulation and print start info; store timing\n", - "print(\"Starting simulation\")\n", - "start = datetime.now()\n", - "simulation.step(num_steps) \n", - "end = datetime.now()\n", - "\n", - "# Print elapsed time info, finalize trajectory file\n", - "print(\"Elapsed time %.2f seconds\" % (end-start).total_seconds())\n", - "netcdf_reporter.close()\n", - "print(\"Done!\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "xQohKeS_-E6T" + "_camera_str": "orthographic", + "_dom_classes": [], + "_gui_theme": null, + "_ibtn_fullscreen": "IPY_MODEL_9d499eae9c6f48eab85029147de2a510", + "_igui": null, + "_iplayer": "IPY_MODEL_818e1c3e5b8046f78dac1fb0cb394aae", + "_model_module": "nglview-js-widgets", + "_model_module_version": "3.0.1", + "_model_name": "NGLModel", + "_ngl_color_dict": {}, + "_ngl_coordinate_resource": {}, + "_ngl_full_stage_parameters": { + "ambientColor": 14540253, + "ambientIntensity": 0.2, + "backgroundColor": "white", + "cameraEyeSep": 0.3, + "cameraFov": 40, + "cameraType": "perspective", + "clipDist": 10, + "clipFar": 100, + "clipNear": 0, + "fogFar": 100, + "fogNear": 50, + "hoverTimeout": 0, + "impostor": true, + "lightColor": 14540253, + "lightIntensity": 1, + "mousePreset": "default", + "panSpeed": 1, + "quality": "medium", + "rotateSpeed": 2, + "sampleLevel": 0, + "tooltip": true, + "workerDefault": true, + "zoomSpeed": 1.2 }, - "source": [ - "## Now we can visualize the results with `nglview`\n", - "\n", - "If you don't have `nglview`, you can also load the PDB file, `molecule.mol2`, with a standard viewer like VMD or Chimera, and then load the PDB trajectory file (`trajectory.pdb`) in afterwards. If `nglview` is not working on your platform, you can use `py3dmol` to load a static view of structures.\n", - "\n", - "Currently nglview seems to be RUNNING on Colab but doesn't result in trajectories which look correct." - ] - }, - { - "cell_type": "code", - "source": [ - "#COLAB ONLY: First enable third party widgets\n", - "from google.colab import output\n", - "output.enable_custom_widget_manager()" + "_ngl_msg_archive": [ + { + "args": [ + { + "binary": false, + "data": "REMARK 1 CREATED WITH MDTraj 1.9.7, 2022-01-17\nCRYST1 40.000 40.000 40.000 90.00 90.00 90.00 P 1 1 \nMODEL 0\nATOM 0 O1 <0> A 1 41.356 37.706 34.048 1.00 0.00 O \nATOM 1 C1 <0> A 1 41.038 37.613 35.389 1.00 0.00 C \nATOM 2 C2 <0> A 1 40.915 38.926 36.205 1.00 0.00 C \nATOM 3 C3 <0> A 1 40.086 38.763 37.490 1.00 0.00 C \nATOM 4 C4 <0> A 1 40.309 39.915 38.448 1.00 0.00 C \nATOM 5 C5 <0> A 1 39.084 39.883 39.328 1.00 0.00 C \nATOM 6 C6 <0> A 1 37.945 39.658 38.312 1.00 0.00 C \nATOM 7 C7 <0> A 1 38.568 38.702 37.255 1.00 0.00 C \nATOM 8 O2 <0> A 1 40.945 36.537 35.946 1.00 0.00 O \nATOM 9 H1 <0> A 1 41.252 38.636 33.788 1.00 0.00 H \nATOM 10 H2 <0> A 1 40.399 39.643 35.559 1.00 0.00 H \nATOM 11 H3 <0> A 1 41.938 39.259 36.407 1.00 0.00 H \nATOM 12 H4 <0> A 1 40.380 37.848 38.012 1.00 0.00 H \nATOM 13 H5 <0> A 1 41.246 39.789 38.998 1.00 0.00 H \nATOM 14 H6 <0> A 1 40.387 40.826 37.846 1.00 0.00 H \nATOM 15 H7 <0> A 1 39.116 39.060 40.049 1.00 0.00 H \nATOM 16 H8 <0> A 1 38.914 40.814 39.876 1.00 0.00 H \nATOM 17 H9 <0> A 1 37.044 39.278 38.802 1.00 0.00 H \nATOM 18 H10 <0> A 1 37.797 40.645 37.863 1.00 0.00 H \nATOM 19 H11 <0> A 1 38.250 39.083 36.280 1.00 0.00 H \nATOM 20 H12 <0> A 1 38.197 37.676 37.328 1.00 0.00 H \nTER 21 <0> A 1\nENDMDL\nCONECT 1 2 10\nCONECT 2 1 3 9\nCONECT 3 2 4 11 12\nCONECT 4 3 8 5 13\nCONECT 5 4 6 14 15\nCONECT 6 5 7 16 17\nCONECT 7 6 8 18 19\nCONECT 8 4 7 20 21\nCONECT 9 2\nCONECT 10 1\nCONECT 11 3\nCONECT 12 3\nCONECT 13 4\nCONECT 14 5\nCONECT 15 5\nCONECT 16 6\nCONECT 17 6\nCONECT 18 7\nCONECT 19 7\nCONECT 20 8\nCONECT 21 8\nEND\n", + "type": "blob" + } + ], + "kwargs": { + "defaultRepresentation": true, + "ext": "pdb", + "name": "nglview.adaptor.MDTrajTrajectory" + }, + "methodName": "loadFile", + "reconstruc_color_scheme": false, + "target": "Stage", + "type": "call_method" + } ], - 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}, - "outputs": [ - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "50af28a4d71f4a5a883fdc95a9467915", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "NGLWidget(max_frame=9)" - ] + }, + "1": { + "0": { + "params": { + "aspectRatio": 1.5, + "assembly": "default", + "bondScale": 0.3, + "bondSpacing": 0.75, + "clipCenter": { + "x": 0, + "y": 0, + "z": 0 + }, + "clipNear": 0, + "clipRadius": 0, + "colorMode": "hcl", + "colorReverse": false, + "colorScale": "", + "colorScheme": "element", + "colorValue": 9474192, + "cylinderOnly": false, + "defaultAssembly": "", + "depthWrite": true, + "diffuse": 16777215, + "diffuseInterior": false, + "disableImpostor": false, + "disablePicking": false, + "flatShaded": false, + "interiorColor": 2236962, + "interiorDarkening": 0, + "lazy": false, + "lineOnly": false, + "linewidth": 2, + "matrix": { + "elements": [ + 1, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 1, + 0, + 0, + 0, + 0, + 1 + ] }, - "metadata": { - "application/vnd.jupyter.widget-view+json": { - "colab": { - "custom_widget_manager": { - "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/a8874ba6619b6106/manager.min.js" - } - } - } - } + "metalness": 0, + "multipleBond": "off", + "opacity": 1, + "openEnded": true, + "quality": "high", + "radialSegments": 20, + "radiusData": {}, + "radiusScale": 2, + "radiusSize": 0.15, + "radiusType": "size", + "roughness": 0.4, + "sele": "", + "side": "double", + "sphereDetail": 2, + "useInteriorColor": true, + "visible": true, + "wireframe": false + }, + "type": "ball+stick" } + } + }, + "_ngl_serialize": false, + "_ngl_version": "2.0.0-dev.36", + "_ngl_view_id": [ + "80BA9328-398F-44E4-97B2-F5E415EBAE05" ], - "source": [ - "# Load stored trajectory using MDTraj; the trajectory doesn't contain chemistry info so we also load a PDB\n", - "import mdtraj\n", - "import nglview\n", - "traj= mdtraj.load(os.path.join('.', 'trajectory.nc'), top=os.path.join('.', 'molecule.mol2'))\n", - "\n", - "# View the trajectory\n", - "view = nglview.show_mdtraj(traj)\n", - "view" - ] + "_player_dict": {}, + "_scene_position": {}, + "_scene_rotation": {}, + "_synced_model_ids": [], + "_synced_repr_model_ids": [], + "_view_count": null, + "_view_height": "", + "_view_module": "nglview-js-widgets", + "_view_module_version": "3.0.1", + "_view_name": "NGLView", + "_view_width": "", + "background": "white", + "frame": 0, + "gui_style": null, + "layout": "IPY_MODEL_34c55a41b0e045d1a7fdce2a3f347ac4", + "max_frame": 9, + "n_components": 2, + "picked": {} + } }, - { - "cell_type": "markdown", - "metadata": { - "id": "mCezrgQq-E6U" - }, - "source": [ - "## Exercises\n", - "\n", - "- Try your own molecule. What about one which has the potential for internal hydrogen bonding?\n", - "- Run the dynamics longer\n", - "- What is different if you lower the temperature? Raise the temperature? 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"This material is a set of slides intended for presentation with RISE as detailed [in the course materials on GitHub](https://github.com/MobleyLab/drug-computing/tree/master/uci-pharmsci/lectures/Python_Linux_Vi). While it may be useful without RISE, it will also likely appear somewhat less verbose than it would if it were intended for use in written form. If you wish to install rise, it is conda-installable via conda-forge, e.g. \"conda install rise\" should work. Refer to the [RISE docs for more details](https://rise.readthedocs.io/en/maint-5.6/usage.html).\n", + "This material is a set of slides intended for presentation with RISE as detailed [in the course materials on GitHub](https://github.com/MobleyLab/drug-computing/tree/master/uci-pharmsci/lectures/Python_Linux_Vi). While it may be useful without RISE, it will also likely appear somewhat less verbose than it would if it were intended for use in written form. If you wish to install rise, it is conda-installable via conda-forge, e.g. \"conda install rise\" should work. Refer to the [RISE docs for more details](https://rise.readthedocs.io/en/stable/usage.html).\n", "\n", "This material is also presently formatted as a Jupyter notebook and can be run locally on a computer with Python, Jupyter, etc., if your platform is suitable. It can also be run via Google Colab, which removes most platform requirements. This notebook is set up for either running locally or on Colab, but some commands should be skipped if you are running locally." ] @@ -218,7 +218,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -249,7 +249,7 @@ "" ] }, - "execution_count": 4, + "execution_count": 1, "metadata": { "image/png": { "width": 1000 @@ -441,7 +441,7 @@ "- Jupyter notebooks: Great for introducing and prototyping\n", "- These slides are themselves a Jupyter notebook you can try out\n", "\n", - "You may also want to refer to the Volkamer lab's intro to [Python and Jupyter notebooks](https://github.com/volkamerlab/TeachOpenCADD/blob/master/introductory_notebooks/1_IntroToPythonAndJupyter.ipynb)" + "You may also want to refer to the Volkamer lab's resources introducing [Python and Jupyter notebooks](https://projects.volkamerlab.org/teachopencadd/external_tutorials_collections.html)" ] }, { @@ -3944,9 +3944,9 @@ "provenance": [] }, "kernelspec": { - "display_name": "drugcomp", + "display_name": "drugcomp23", "language": "python", - "name": "drugcomp" + "name": "drugcomp23" }, "language_info": { "codemirror_mode": { @@ -3958,7 +3958,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.15" }, "livereveal": { "height": 900, diff --git a/uci-pharmsci/lectures/QM/QM_slides.key b/uci-pharmsci/lectures/QM/QM_slides.key index efa6371..501f585 100644 Binary files a/uci-pharmsci/lectures/QM/QM_slides.key and b/uci-pharmsci/lectures/QM/QM_slides.key differ diff --git a/uci-pharmsci/lectures/QM/psi4_example.ipynb b/uci-pharmsci/lectures/QM/psi4_example.ipynb index 3d552e3..af42600 100644 --- a/uci-pharmsci/lectures/QM/psi4_example.ipynb +++ b/uci-pharmsci/lectures/QM/psi4_example.ipynb @@ -2,17 +2,21 @@ "cells": [ { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "mKK1Lzhl6qet" + }, "source": [ "# PharmSci 175/275 (UCI)\n", - "## What is this?? \n", - "The material below is a supplement to the quantum mechanics (QM) lecture from Drug Discovery Computing Techniques, PharmSci 175/275 at UC Irvine. \n", + "## What is this??\n", + "The material below is a supplement to the quantum mechanics (QM) lecture from Drug Discovery Computing Techniques, PharmSci 175/275 at UC Irvine.\n", "Extensive materials for this course, as well as extensive background and related materials, are available on the course GitHub repository: [github.com/mobleylab/drug-computing](https://github.com/mobleylab/drug-computing)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "PFeXvnvW6qeu" + }, "source": [ "# Using QM in Python\n", "\n", @@ -27,16 +31,20 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "FbKSFyOv6qev" + }, "source": [ "## Choose whether to run under Google Colab or locally\n", "\n", - "You need to do different preparation to run this notebook locally vs Google Colab; skip to the appropriate section following depending on which you choose." + "You need to do different preparation to run this notebook locally vs Google Colab; skip to the appropriate section following depending on which you choose.\n" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "p0iJPdeX6qev" + }, "source": [ "## Preparation for using Google Colab (SKIP IF RUNNING LOCALLY))\n", "\n", @@ -44,93 +52,1089 @@ "\n", "If you are running this on Google Colab, you need to take a couple additional steps of preparation. **Note that these steps may take 5-10 minutes to complete.**\n", "\n", - "Psi4 installs via `conda`, not pip, so you will need to get conda set up on Colab: " + "Psi4 installs via `mamba`:" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oNZZEHq46qev", + "outputId": 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keyutils-1.6.1-h166bdaf_0.tar.bz2\n", + "Extracting libev-4.33-hd590300_2.conda\n", + "Extracting libffi-3.4.2-h7f98852_5.tar.bz2\n", + "Extracting libiconv-1.17-hd590300_2.conda\n", + "Extracting libnsl-2.0.1-hd590300_0.conda\n", + "Extracting libuuid-2.38.1-h0b41bf4_0.conda\n", + "Extracting libzlib-1.2.13-hd590300_5.conda\n", + "Extracting lz4-c-1.9.4-hcb278e6_0.conda\n", + "Extracting lzo-2.10-h516909a_1000.tar.bz2\n", + "Extracting ncurses-6.4-h59595ed_2.conda\n", + "Extracting openssl-3.2.0-hd590300_1.conda\n", + "Extracting reproc-14.2.4.post0-hd590300_1.conda\n", + "Extracting xz-5.2.6-h166bdaf_0.tar.bz2\n", + "Extracting yaml-cpp-0.8.0-h59595ed_0.conda\n", + "Extracting libedit-3.1.20191231-he28a2e2_2.tar.bz2\n", + "Extracting libnghttp2-1.58.0-h47da74e_1.conda\n", + "Extracting libsolv-0.7.27-hfc55251_0.conda\n", + "Extracting libsqlite-3.44.2-h2797004_0.conda\n", + "Extracting libssh2-1.11.0-h0841786_0.conda\n", + "Extracting libxml2-2.12.3-h232c23b_0.conda\n", + "Extracting readline-8.2-h8228510_1.conda\n", + "Extracting reproc-cpp-14.2.4.post0-h59595ed_1.conda\n", + "Extracting tk-8.6.13-noxft_h4845f30_101.conda\n", + "Extracting zstd-1.5.5-hfc55251_0.conda\n", + "Extracting krb5-1.21.2-h659d440_0.conda\n", + "Extracting libarchive-3.7.2-h2aa1ff5_1.conda\n", + "Extracting python-3.10.13-hd12c33a_0_cpython.conda\n", + "Extracting libcurl-8.5.0-hca28451_0.conda\n", + "Extracting menuinst-2.0.1-py310hff52083_0.conda\n", + "Extracting archspec-0.2.2-pyhd8ed1ab_0.conda\n", + "Extracting boltons-23.1.1-pyhd8ed1ab_0.conda\n", + "Extracting brotli-python-1.1.0-py310hc6cd4ac_1.conda\n", + "Extracting certifi-2023.11.17-pyhd8ed1ab_0.conda\n", + "Extracting charset-normalizer-3.3.2-pyhd8ed1ab_0.conda\n", + "Extracting colorama-0.4.6-pyhd8ed1ab_0.tar.bz2\n", + "Extracting distro-1.8.0-pyhd8ed1ab_0.conda\n", + "Extracting idna-3.6-pyhd8ed1ab_0.conda\n", + "Extracting jsonpointer-2.4-py310hff52083_3.conda\n", + "Extracting libmamba-1.5.5-had39da4_0.conda\n", + "Extracting packaging-23.2-pyhd8ed1ab_0.conda\n", + "Extracting platformdirs-4.1.0-pyhd8ed1ab_0.conda\n", + "Extracting pluggy-1.3.0-pyhd8ed1ab_0.conda\n", + "Extracting pycosat-0.6.6-py310h2372a71_0.conda\n", + "Extracting pycparser-2.21-pyhd8ed1ab_0.tar.bz2\n", + "Extracting pysocks-1.7.1-pyha2e5f31_6.tar.bz2\n", + "Extracting ruamel.yaml.clib-0.2.7-py310h2372a71_2.conda\n", + "Extracting setuptools-68.2.2-pyhd8ed1ab_0.conda\n", + "Extracting truststore-0.8.0-pyhd8ed1ab_0.conda\n", + "Extracting wheel-0.42.0-pyhd8ed1ab_0.conda\n", + "Extracting cffi-1.16.0-py310h2fee648_0.conda\n", + "Extracting jsonpatch-1.33-pyhd8ed1ab_0.conda\n", + "Extracting libmambapy-1.5.5-py310h39ff949_0.conda\n", + "Extracting pip-23.3.2-pyhd8ed1ab_0.conda\n", + "Extracting ruamel.yaml-0.18.5-py310h2372a71_0.conda\n", + "Extracting tqdm-4.66.1-pyhd8ed1ab_0.conda\n", + "Extracting urllib3-2.1.0-pyhd8ed1ab_0.conda\n", + "Extracting requests-2.31.0-pyhd8ed1ab_0.conda\n", + "Extracting zstandard-0.22.0-py310h1275a96_0.conda\n", + "Extracting conda-package-streaming-0.9.0-pyhd8ed1ab_0.conda\n", + "Extracting conda-package-handling-2.2.0-pyh38be061_0.conda\n", + "Extracting conda-23.11.0-py310hff52083_1.conda\n", + "Extracting conda-libmamba-solver-23.12.0-pyhd8ed1ab_0.conda\n", + "Extracting mamba-1.5.5-py310h51d5547_0.conda\n", + "\n", + "Installing base environment...\n", + "\n", + "\n", + " __\n", + " __ ______ ___ ____ _____ ___ / /_ ____ _\n", + " / / / / __ `__ \\/ __ `/ __ `__ \\/ __ \\/ __ `/\n", + " / /_/ / / / / / / /_/ / / / / / / /_/ / /_/ /\n", + " / .___/_/ /_/ /_/\\__,_/_/ /_/ /_/_.___/\\__,_/\n", + " /_/\n", + "\n", + "Transaction\n", + "\n", + " Prefix: /usr/local\n", + "\n", + " Updating specs:\n", + "\n", + " - conda-forge/linux-64::_libgcc_mutex==0.1=conda_forge[md5=d7c89558ba9fa0495403155b64376d81]\n", + " - conda-forge/linux-64::ca-certificates==2023.11.17=hbcca054_0[md5=01ffc8d36f9eba0ce0b3c1955fa780ee]\n", + " - conda-forge/linux-64::ld_impl_linux-64==2.40=h41732ed_0[md5=7aca3059a1729aa76c597603f10b0dd3]\n", + " - conda-forge/linux-64::libstdcxx-ng==13.2.0=h7e041cc_3[md5=937eaed008f6bf2191c5fe76f87755e9]\n", + " - conda-forge/noarch::pybind11-abi==4=hd8ed1ab_3[md5=878f923dd6acc8aeb47a75da6c4098be]\n", + " - conda-forge/linux-64::python_abi==3.10=4_cp310[md5=26322ec5d7712c3ded99dd656142b8ce]\n", + " - conda-forge/noarch::tzdata==2023c=h71feb2d_0[md5=939e3e74d8be4dac89ce83b20de2492a]\n", + " - conda-forge/linux-64::libgomp==13.2.0=h807b86a_3[md5=7124cbb46b13d395bdde68f2d215c989]\n", + " - conda-forge/linux-64::_openmp_mutex==4.5=2_gnu[md5=73aaf86a425cc6e73fcf236a5a46396d]\n", + " - conda-forge/linux-64::libgcc-ng==13.2.0=h807b86a_3[md5=23fdf1fef05baeb7eadc2aed5fb0011f]\n", + " - conda-forge/linux-64::bzip2==1.0.8=hd590300_5[md5=69b8b6202a07720f448be700e300ccf4]\n", + " - conda-forge/linux-64::c-ares==1.24.0=hd590300_0[md5=f5842b88e9cbfa177abfaeacd457a45d]\n", + " - conda-forge/linux-64::fmt==10.1.1=h00ab1b0_1[md5=50a08d74ca45785f3334b175da23fd82]\n", + " - conda-forge/linux-64::icu==73.2=h59595ed_0[md5=cc47e1facc155f91abd89b11e48e72ff]\n", + " - conda-forge/linux-64::keyutils==1.6.1=h166bdaf_0[md5=30186d27e2c9fa62b45fb1476b7200e3]\n", + " - conda-forge/linux-64::libev==4.33=hd590300_2[md5=172bf1cd1ff8629f2b1179945ed45055]\n", + " - conda-forge/linux-64::libffi==3.4.2=h7f98852_5[md5=d645c6d2ac96843a2bfaccd2d62b3ac3]\n", + " - conda-forge/linux-64::libiconv==1.17=hd590300_2[md5=d66573916ffcf376178462f1b61c941e]\n", + " - conda-forge/linux-64::libnsl==2.0.1=hd590300_0[md5=30fd6e37fe21f86f4bd26d6ee73eeec7]\n", + " - conda-forge/linux-64::libuuid==2.38.1=h0b41bf4_0[md5=40b61aab5c7ba9ff276c41cfffe6b80b]\n", + " - conda-forge/linux-64::libzlib==1.2.13=hd590300_5[md5=f36c115f1ee199da648e0597ec2047ad]\n", + " - conda-forge/linux-64::lz4-c==1.9.4=hcb278e6_0[md5=318b08df404f9c9be5712aaa5a6f0bb0]\n", + " - conda-forge/linux-64::lzo==2.10=h516909a_1000[md5=bb14fcb13341b81d5eb386423b9d2bac]\n", + " - conda-forge/linux-64::ncurses==6.4=h59595ed_2[md5=7dbaa197d7ba6032caf7ae7f32c1efa0]\n", + " - conda-forge/linux-64::openssl==3.2.0=hd590300_1[md5=603827b39ea2b835268adb8c821b8570]\n", + " - conda-forge/linux-64::reproc==14.2.4.post0=hd590300_1[md5=82ca53502dfd5a64a80dee76dae14685]\n", + " - conda-forge/linux-64::xz==5.2.6=h166bdaf_0[md5=2161070d867d1b1204ea749c8eec4ef0]\n", + " - conda-forge/linux-64::yaml-cpp==0.8.0=h59595ed_0[md5=965eaacd7c18eb8361fd12bb9e7a57d7]\n", + " - conda-forge/linux-64::libedit==3.1.20191231=he28a2e2_2[md5=4d331e44109e3f0e19b4cb8f9b82f3e1]\n", + " - conda-forge/linux-64::libnghttp2==1.58.0=h47da74e_1[md5=700ac6ea6d53d5510591c4344d5c989a]\n", + " - conda-forge/linux-64::libsolv==0.7.27=hfc55251_0[md5=7dc82edcd617c399ca7fdb51d993a0c2]\n", + " - conda-forge/linux-64::libsqlite==3.44.2=h2797004_0[md5=3b6a9f225c3dbe0d24f4fedd4625c5bf]\n", + " - conda-forge/linux-64::libssh2==1.11.0=h0841786_0[md5=1f5a58e686b13bcfde88b93f547d23fe]\n", + " - conda-forge/linux-64::libxml2==2.12.3=h232c23b_0[md5=bc6ac4c0cea148d924f621985bc3892b]\n", + " - conda-forge/linux-64::readline==8.2=h8228510_1[md5=47d31b792659ce70f470b5c82fdfb7a4]\n", + " - conda-forge/linux-64::reproc-cpp==14.2.4.post0=h59595ed_1[md5=715e1d720ec1a03715bebd237972fca5]\n", + " - conda-forge/linux-64::tk==8.6.13=noxft_h4845f30_101[md5=d453b98d9c83e71da0741bb0ff4d76bc]\n", + " - conda-forge/linux-64::zstd==1.5.5=hfc55251_0[md5=04b88013080254850d6c01ed54810589]\n", + " - conda-forge/linux-64::krb5==1.21.2=h659d440_0[md5=cd95826dbd331ed1be26bdf401432844]\n", + " - conda-forge/linux-64::libarchive==3.7.2=h2aa1ff5_1[md5=3bf887827d1968275978361a6e405e4f]\n", + " - conda-forge/linux-64::python==3.10.13=hd12c33a_0_cpython[md5=f3a8c32aa764c3e7188b4b810fc9d6ce]\n", + " - conda-forge/linux-64::libcurl==8.5.0=hca28451_0[md5=7144d5a828e2cae218e0e3c98d8a0aeb]\n", + " - conda-forge/linux-64::menuinst==2.0.1=py310hff52083_0[md5=471a72a1214ff4910ab6095936aecdfd]\n", + " - conda-forge/noarch::archspec==0.2.2=pyhd8ed1ab_0[md5=0dc2fce00a160271714647c019e3a8a8]\n", + " - conda-forge/noarch::boltons==23.1.1=pyhd8ed1ab_0[md5=56febe65315cc388a5d20adf2b39a74d]\n", + " - conda-forge/linux-64::brotli-python==1.1.0=py310hc6cd4ac_1[md5=1f95722c94f00b69af69a066c7433714]\n", + " - conda-forge/noarch::certifi==2023.11.17=pyhd8ed1ab_0[md5=2011bcf45376341dd1d690263fdbc789]\n", + " - conda-forge/noarch::charset-normalizer==3.3.2=pyhd8ed1ab_0[md5=7f4a9e3fcff3f6356ae99244a014da6a]\n", + " - conda-forge/noarch::colorama==0.4.6=pyhd8ed1ab_0[md5=3faab06a954c2a04039983f2c4a50d99]\n", + " - conda-forge/noarch::distro==1.8.0=pyhd8ed1ab_0[md5=67999c5465064480fa8016d00ac768f6]\n", + " - conda-forge/noarch::idna==3.6=pyhd8ed1ab_0[md5=1a76f09108576397c41c0b0c5bd84134]\n", + " - conda-forge/linux-64::jsonpointer==2.4=py310hff52083_3[md5=08ec1463dbc5c806a32fc431874032ca]\n", + " - conda-forge/linux-64::libmamba==1.5.5=had39da4_0[md5=3a99159f63c866711e67955885087700]\n", + " - conda-forge/noarch::packaging==23.2=pyhd8ed1ab_0[md5=79002079284aa895f883c6b7f3f88fd6]\n", + " - conda-forge/noarch::platformdirs==4.1.0=pyhd8ed1ab_0[md5=45a5065664da0d1dfa8f8cd2eaf05ab9]\n", + " - conda-forge/noarch::pluggy==1.3.0=pyhd8ed1ab_0[md5=2390bd10bed1f3fdc7a537fb5a447d8d]\n", + " - conda-forge/linux-64::pycosat==0.6.6=py310h2372a71_0[md5=0adaac9a86d59adae2bc86b3cdef2df1]\n", + " - conda-forge/noarch::pycparser==2.21=pyhd8ed1ab_0[md5=076becd9e05608f8dc72757d5f3a91ff]\n", + " - conda-forge/noarch::pysocks==1.7.1=pyha2e5f31_6[md5=2a7de29fb590ca14b5243c4c812c8025]\n", + " - conda-forge/linux-64::ruamel.yaml.clib==0.2.7=py310h2372a71_2[md5=7c9da9721ee545d57ad759f020172853]\n", + " - conda-forge/noarch::setuptools==68.2.2=pyhd8ed1ab_0[md5=fc2166155db840c634a1291a5c35a709]\n", + " - conda-forge/noarch::truststore==0.8.0=pyhd8ed1ab_0[md5=08316d001eca8854392cf2837828ea11]\n", + " - conda-forge/noarch::wheel==0.42.0=pyhd8ed1ab_0[md5=1cdea58981c5cbc17b51973bcaddcea7]\n", + " - conda-forge/linux-64::cffi==1.16.0=py310h2fee648_0[md5=45846a970e71ac98fd327da5d40a0a2c]\n", + " - conda-forge/noarch::jsonpatch==1.33=pyhd8ed1ab_0[md5=bfdb7c5c6ad1077c82a69a8642c87aff]\n", + " - conda-forge/linux-64::libmambapy==1.5.5=py310h39ff949_0[md5=718fe2d638023e9eaaa2f8e05e1a8185]\n", + " - conda-forge/noarch::pip==23.3.2=pyhd8ed1ab_0[md5=8591c748f98dcc02253003533bc2e4b1]\n", + " - conda-forge/linux-64::ruamel.yaml==0.18.5=py310h2372a71_0[md5=14fd49048b91c96a8fbf1113a8cc4f49]\n", + " - conda-forge/noarch::tqdm==4.66.1=pyhd8ed1ab_0[md5=03c97908b976498dcae97eb4e4f3149c]\n", + " - conda-forge/noarch::urllib3==2.1.0=pyhd8ed1ab_0[md5=f8ced8ee63830dec7ecc1be048d1470a]\n", + " - conda-forge/noarch::requests==2.31.0=pyhd8ed1ab_0[md5=a30144e4156cdbb236f99ebb49828f8b]\n", + " - conda-forge/linux-64::zstandard==0.22.0=py310h1275a96_0[md5=54698ba13cd3494547b289cd86a2176a]\n", + " - conda-forge/noarch::conda-package-streaming==0.9.0=pyhd8ed1ab_0[md5=38253361efb303deead3eab39ae9269b]\n", + " - conda-forge/noarch::conda-package-handling==2.2.0=pyh38be061_0[md5=8a3ae7f6318376aa08ea753367bb7dd6]\n", + " - conda-forge/linux-64::conda==23.11.0=py310hff52083_1[md5=d5278ac990c67d5ed51584998aa16c5a]\n", + " - conda-forge/noarch::conda-libmamba-solver==23.12.0=pyhd8ed1ab_0[md5=e877d5150e73a0844ea2939be110c3b1]\n", + " - conda-forge/linux-64::mamba==1.5.5=py310h51d5547_0[md5=7e9379ab9a9540195193e3c00b48465b]\n", + "\n", + "\n", + " Package Version Build Channel Size\n", + "───────────────────────────────────────────────────────────────────────────────────────\n", + " Install:\n", + "───────────────────────────────────────────────────────────────────────────────────────\n", + "\n", + " \u001b[32m+ _libgcc_mutex \u001b[0m 0.1 conda_forge conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ _openmp_mutex \u001b[0m 4.5 2_gnu conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ archspec \u001b[0m 0.2.2 pyhd8ed1ab_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ boltons \u001b[0m 23.1.1 pyhd8ed1ab_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ brotli-python \u001b[0m 1.1.0 py310hc6cd4ac_1 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ bzip2 \u001b[0m 1.0.8 hd590300_5 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ c-ares \u001b[0m 1.24.0 hd590300_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ ca-certificates \u001b[0m 2023.11.17 hbcca054_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ certifi \u001b[0m 2023.11.17 pyhd8ed1ab_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ cffi \u001b[0m 1.16.0 py310h2fee648_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ charset-normalizer \u001b[0m 3.3.2 pyhd8ed1ab_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ colorama \u001b[0m 0.4.6 pyhd8ed1ab_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ conda \u001b[0m 23.11.0 py310hff52083_1 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ conda-libmamba-solver \u001b[0m 23.12.0 pyhd8ed1ab_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ conda-package-handling \u001b[0m 2.2.0 pyh38be061_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ conda-package-streaming\u001b[0m 0.9.0 pyhd8ed1ab_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ distro \u001b[0m 1.8.0 pyhd8ed1ab_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ fmt \u001b[0m 10.1.1 h00ab1b0_1 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ icu \u001b[0m 73.2 h59595ed_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ idna \u001b[0m 3.6 pyhd8ed1ab_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ jsonpatch \u001b[0m 1.33 pyhd8ed1ab_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ jsonpointer \u001b[0m 2.4 py310hff52083_3 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ keyutils \u001b[0m 1.6.1 h166bdaf_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ krb5 \u001b[0m 1.21.2 h659d440_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ ld_impl_linux-64 \u001b[0m 2.40 h41732ed_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ libarchive \u001b[0m 3.7.2 h2aa1ff5_1 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ libcurl \u001b[0m 8.5.0 hca28451_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ libedit \u001b[0m 3.1.20191231 he28a2e2_2 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ libev \u001b[0m 4.33 hd590300_2 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ libffi \u001b[0m 3.4.2 h7f98852_5 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ libgcc-ng \u001b[0m 13.2.0 h807b86a_3 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ libgomp \u001b[0m 13.2.0 h807b86a_3 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ libiconv \u001b[0m 1.17 hd590300_2 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ libmamba \u001b[0m 1.5.5 had39da4_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ libmambapy \u001b[0m 1.5.5 py310h39ff949_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ libnghttp2 \u001b[0m 1.58.0 h47da74e_1 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ libnsl \u001b[0m 2.0.1 hd590300_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ libsolv \u001b[0m 0.7.27 hfc55251_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ libsqlite \u001b[0m 3.44.2 h2797004_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ libssh2 \u001b[0m 1.11.0 h0841786_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ libstdcxx-ng \u001b[0m 13.2.0 h7e041cc_3 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ libuuid \u001b[0m 2.38.1 h0b41bf4_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ libxml2 \u001b[0m 2.12.3 h232c23b_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ libzlib \u001b[0m 1.2.13 hd590300_5 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ lz4-c \u001b[0m 1.9.4 hcb278e6_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ lzo \u001b[0m 2.10 h516909a_1000 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ mamba \u001b[0m 1.5.5 py310h51d5547_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ menuinst \u001b[0m 2.0.1 py310hff52083_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ ncurses \u001b[0m 6.4 h59595ed_2 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ openssl \u001b[0m 3.2.0 hd590300_1 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ packaging \u001b[0m 23.2 pyhd8ed1ab_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ pip \u001b[0m 23.3.2 pyhd8ed1ab_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ platformdirs \u001b[0m 4.1.0 pyhd8ed1ab_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ pluggy \u001b[0m 1.3.0 pyhd8ed1ab_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ pybind11-abi \u001b[0m 4 hd8ed1ab_3 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ pycosat \u001b[0m 0.6.6 py310h2372a71_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ pycparser \u001b[0m 2.21 pyhd8ed1ab_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ pysocks \u001b[0m 1.7.1 pyha2e5f31_6 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ python \u001b[0m 3.10.13 hd12c33a_0_cpython conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ python_abi \u001b[0m 3.10 4_cp310 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ readline \u001b[0m 8.2 h8228510_1 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ reproc \u001b[0m 14.2.4.post0 hd590300_1 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ reproc-cpp \u001b[0m 14.2.4.post0 h59595ed_1 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ requests \u001b[0m 2.31.0 pyhd8ed1ab_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ ruamel.yaml \u001b[0m 0.18.5 py310h2372a71_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ ruamel.yaml.clib \u001b[0m 0.2.7 py310h2372a71_2 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ setuptools \u001b[0m 68.2.2 pyhd8ed1ab_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ tk \u001b[0m 8.6.13 noxft_h4845f30_101 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ tqdm \u001b[0m 4.66.1 pyhd8ed1ab_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ truststore \u001b[0m 0.8.0 pyhd8ed1ab_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ tzdata \u001b[0m 2023c h71feb2d_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ urllib3 \u001b[0m 2.1.0 pyhd8ed1ab_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ wheel \u001b[0m 0.42.0 pyhd8ed1ab_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ xz \u001b[0m 5.2.6 h166bdaf_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ yaml-cpp \u001b[0m 0.8.0 h59595ed_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ zstandard \u001b[0m 0.22.0 py310h1275a96_0 conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ zstd \u001b[0m 1.5.5 hfc55251_0 conda-forge\u001b[32m Cached\u001b[0m\n", + "\n", + " Summary:\n", + "\n", + " Install: 77 packages\n", + "\n", + " Total download: 0 B\n", + "\n", + "───────────────────────────────────────────────────────────────────────────────────────\n", + "\n", + "\n", + "\n", + "Transaction starting\n", + "\u001b[?25l\u001b[2K\u001b[0G\u001b[?25hLinking _libgcc_mutex-0.1-conda_forge\n", + "Linking ca-certificates-2023.11.17-hbcca054_0\n", + "Linking ld_impl_linux-64-2.40-h41732ed_0\n", + "Linking libstdcxx-ng-13.2.0-h7e041cc_3\n", + "Linking pybind11-abi-4-hd8ed1ab_3\n", + "Linking python_abi-3.10-4_cp310\n", + "Linking tzdata-2023c-h71feb2d_0\n", + "Linking libgomp-13.2.0-h807b86a_3\n", + "Linking _openmp_mutex-4.5-2_gnu\n", + "Linking libgcc-ng-13.2.0-h807b86a_3\n", + "Linking bzip2-1.0.8-hd590300_5\n", + "Linking c-ares-1.24.0-hd590300_0\n", + "Linking fmt-10.1.1-h00ab1b0_1\n", + "Linking icu-73.2-h59595ed_0\n", + "Linking keyutils-1.6.1-h166bdaf_0\n", + "Linking libev-4.33-hd590300_2\n", + "Linking libffi-3.4.2-h7f98852_5\n", + "Linking libiconv-1.17-hd590300_2\n", + "Linking libnsl-2.0.1-hd590300_0\n", + "Linking libuuid-2.38.1-h0b41bf4_0\n", + "Linking libzlib-1.2.13-hd590300_5\n", + "Linking lz4-c-1.9.4-hcb278e6_0\n", + "Linking lzo-2.10-h516909a_1000\n", + "Linking ncurses-6.4-h59595ed_2\n", + "Linking openssl-3.2.0-hd590300_1\n", + "Linking reproc-14.2.4.post0-hd590300_1\n", + "Linking xz-5.2.6-h166bdaf_0\n", + "Linking yaml-cpp-0.8.0-h59595ed_0\n", + "Linking libedit-3.1.20191231-he28a2e2_2\n", + "Linking libnghttp2-1.58.0-h47da74e_1\n", + "Linking libsolv-0.7.27-hfc55251_0\n", + "Linking libsqlite-3.44.2-h2797004_0\n", + "Linking libssh2-1.11.0-h0841786_0\n", + "Linking libxml2-2.12.3-h232c23b_0\n", + "Linking readline-8.2-h8228510_1\n", + "Linking reproc-cpp-14.2.4.post0-h59595ed_1\n", + "Linking tk-8.6.13-noxft_h4845f30_101\n", + "Linking zstd-1.5.5-hfc55251_0\n", + "Linking krb5-1.21.2-h659d440_0\n", + "Linking libarchive-3.7.2-h2aa1ff5_1\n", + "Linking python-3.10.13-hd12c33a_0_cpython\n", + "\u001b[33m\u001b[1mwarning libmamba\u001b[m [python-3.10.13-hd12c33a_0_cpython] The following files were already present in the environment:\n", + " - bin/python\n", + "Linking libcurl-8.5.0-hca28451_0\n", + "Linking menuinst-2.0.1-py310hff52083_0\n", + "Linking archspec-0.2.2-pyhd8ed1ab_0\n", + "Linking boltons-23.1.1-pyhd8ed1ab_0\n", + "Linking brotli-python-1.1.0-py310hc6cd4ac_1\n", + "Linking certifi-2023.11.17-pyhd8ed1ab_0\n", + "Linking charset-normalizer-3.3.2-pyhd8ed1ab_0\n", + "\u001b[33m\u001b[1mwarning libmamba\u001b[m [charset-normalizer-3.3.2-pyhd8ed1ab_0] The following files were already present in the environment:\n", + " - bin/normalizer\n", + "Linking colorama-0.4.6-pyhd8ed1ab_0\n", + "Linking distro-1.8.0-pyhd8ed1ab_0\n", + "Linking idna-3.6-pyhd8ed1ab_0\n", + "Linking jsonpointer-2.4-py310hff52083_3\n", + "Linking libmamba-1.5.5-had39da4_0\n", + "Linking packaging-23.2-pyhd8ed1ab_0\n", + "Linking platformdirs-4.1.0-pyhd8ed1ab_0\n", + "Linking pluggy-1.3.0-pyhd8ed1ab_0\n", + "Linking pycosat-0.6.6-py310h2372a71_0\n", + "Linking pycparser-2.21-pyhd8ed1ab_0\n", + "Linking pysocks-1.7.1-pyha2e5f31_6\n", + "Linking ruamel.yaml.clib-0.2.7-py310h2372a71_2\n", + "Linking setuptools-68.2.2-pyhd8ed1ab_0\n", + "Linking truststore-0.8.0-pyhd8ed1ab_0\n", + "Linking wheel-0.42.0-pyhd8ed1ab_0\n", + "\u001b[33m\u001b[1mwarning libmamba\u001b[m [wheel-0.42.0-pyhd8ed1ab_0] The following files were already present in the environment:\n", + " - bin/wheel\n", + "Linking cffi-1.16.0-py310h2fee648_0\n", + "Linking jsonpatch-1.33-pyhd8ed1ab_0\n", + "Linking libmambapy-1.5.5-py310h39ff949_0\n", + "Linking pip-23.3.2-pyhd8ed1ab_0\n", + "\u001b[33m\u001b[1mwarning libmamba\u001b[m [pip-23.3.2-pyhd8ed1ab_0] The following files were already present in the environment:\n", + " - bin/pip\n", + " - bin/pip3\n", + "Linking ruamel.yaml-0.18.5-py310h2372a71_0\n", + "Linking tqdm-4.66.1-pyhd8ed1ab_0\n", + "\u001b[33m\u001b[1mwarning libmamba\u001b[m [tqdm-4.66.1-pyhd8ed1ab_0] The following files were already present in the environment:\n", + " - bin/tqdm\n", + "Linking urllib3-2.1.0-pyhd8ed1ab_0\n", + "Linking requests-2.31.0-pyhd8ed1ab_0\n", + "Linking zstandard-0.22.0-py310h1275a96_0\n", + "Linking conda-package-streaming-0.9.0-pyhd8ed1ab_0\n", + "Linking conda-package-handling-2.2.0-pyh38be061_0\n", + "Linking conda-23.11.0-py310hff52083_1\n", + "Linking conda-libmamba-solver-23.12.0-pyhd8ed1ab_0\n", + "Linking mamba-1.5.5-py310h51d5547_0\n", + "Transaction finished\n", + "installation finished.\n" + ] + } + ], "source": [ - "! wget https://repo.anaconda.com/miniconda/Miniconda3-py37_4.10.3-Linux-x86_64.sh\n", - "! chmod +x Miniconda3-py37_4.10.3-Linux-x86_64.sh\n", - "! bash ./Miniconda3-py37_4.10.3-Linux-x86_64.sh -b -f -p /usr/local\n", + "%env PYTHONPATH=\n", + "! wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh\n", + "! chmod +x Miniforge3-Linux-x86_64.sh\n", + "! bash ./Miniforge3-Linux-x86_64.sh -b -f -p /usr/local\n", "import sys\n", - "sys.path.append('/usr/local/lib/python3.7/site-packages/')" + "sys.path.append('/usr/local/lib/python3.10/site-packages/')" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "qsnThDjT6qew" + }, "source": [ - "Then `conda`-install psi4:" + "Then `mamba`-install psi4:" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "riwb9Wyz6qew", + "outputId": "f4a192fc-d442-42c0-8426-e5f63bcaf3ad" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Looking for: ['psi4']\n", + "\n", + "\u001b[?25l\u001b[2K\u001b[0G[+] 0.0s\n", + "\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.1s\n", + "conda-forge/linux-64 ⣾ \n", + "conda-forge/noarch ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.2s\n", + "conda-forge/linux-64 11%\n", + "conda-forge/noarch 1%\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.3s\n", + "conda-forge/linux-64 22%\n", + "conda-forge/noarch 12%\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.4s\n", + "conda-forge/linux-64 32%\n", + "conda-forge/noarch 24%\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.5s\n", + "conda-forge/linux-64 37%\n", + "conda-forge/noarch 36%\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.6s\n", + "conda-forge/linux-64 42%\n", + "conda-forge/noarch 48%\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.7s\n", + "conda-forge/linux-64 47%\n", + "conda-forge/noarch 60%\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.8s\n", + "conda-forge/linux-64 52%\n", + "conda-forge/noarch 73%\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 0.9s\n", + "conda-forge/linux-64 57%\n", + "conda-forge/noarch 85%\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gconda-forge/noarch \n", + "[+] 1.0s\n", + "conda-forge/linux-64 63%\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.1s\n", + "conda-forge/linux-64 68%\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.2s\n", + "conda-forge/linux-64 83%\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.3s\n", + "conda-forge/linux-64 88%\u001b[2K\u001b[1A\u001b[2K\u001b[0Gconda-forge/linux-64 \n", + "\u001b[?25h\n", + "Pinned packages:\n", + " - python 3.10.*\n", + "\n", + "\n", + "Transaction\n", + "\n", + " Prefix: /usr/local\n", + "\n", + " Updating specs:\n", + "\n", + " - psi4\n", + " - ca-certificates\n", + " - certifi\n", + " - openssl\n", + "\n", + "\n", + " Package Version Build Channel Size\n", + "─────────────────────────────────────────────────────────────────────────────────\n", + " Install:\n", + "─────────────────────────────────────────────────────────────────────────────────\n", + "\n", + " \u001b[32m+ llvm-openmp \u001b[0m 17.0.6 h4dfa4b3_0 conda-forge 41MB\n", + " \u001b[32m+ psutil \u001b[0m 5.9.8 py310h2372a71_0 conda-forge 368kB\n", + " \u001b[32m+ msgpack-python \u001b[0m 1.0.7 py310hd41b1e2_0 conda-forge 197kB\n", + " \u001b[32m+ yaml \u001b[0m 0.2.5 h7f98852_2 conda-forge 89kB\n", + " \u001b[32m+ gau2grid \u001b[0m 2.0.7 h0b41bf4_2 conda-forge 387kB\n", + " \u001b[32m+ pugixml \u001b[0m 1.14 h59595ed_0 conda-forge 115kB\n", + " \u001b[32m+ libint \u001b[0m 2.8.1 h9bbc0ff_1 conda-forge 67MB\n", + " \u001b[32m+ libxc-c \u001b[0m 6.2.2 cpu_h1b64f48_4 conda-forge 52MB\n", + " \u001b[32m+ libgfortran5 \u001b[0m 13.2.0 ha4646dd_3 conda-forge 1MB\n", + " \u001b[32m+ gtest \u001b[0m 1.14.0 h00ab1b0_1 conda-forge 405kB\n", + " \u001b[32m+ libhwloc \u001b[0m 2.9.3 default_h554bfaf_1009 conda-forge 3MB\n", + " \u001b[32m+ pyyaml \u001b[0m 6.0.1 py310h2372a71_1 conda-forge 171kB\n", + " \u001b[32m+ libgfortran-ng \u001b[0m 13.2.0 h69a702a_3 conda-forge 24kB\n", + " \u001b[32m+ libecpint \u001b[0m 1.0.7 h3ecfda7_10 conda-forge 352kB\n", + " \u001b[32m+ tbb \u001b[0m 2021.11.0 h00ab1b0_0 conda-forge 194kB\n", + " \u001b[32m+ libpcm \u001b[0m 1.2.3 h4175798_8 conda-forge 559kB\n", + " \u001b[32m+ mkl \u001b[0m 2023.2.0 h84fe81f_50496 conda-forge 164MB\n", + " \u001b[32m+ libblas \u001b[0m 3.9.0 20_linux64_mkl conda-forge 15kB\n", + " \u001b[32m+ libcblas \u001b[0m 3.9.0 20_linux64_mkl conda-forge 14kB\n", + " \u001b[32m+ liblapack \u001b[0m 3.9.0 20_linux64_mkl conda-forge 14kB\n", + " \u001b[32m+ dkh \u001b[0m 1.2 hd59d2e7_0 conda-forge 64kB\n", + " \u001b[32m+ numpy \u001b[0m 1.26.3 py310hb13e2d6_0 conda-forge 7MB\n", + " \u001b[32m+ scipy \u001b[0m 1.12.0 py310hb13e2d6_2 conda-forge 16MB\n", + " \u001b[32m+ py-cpuinfo \u001b[0m 9.0.0 pyhd8ed1ab_0 conda-forge 25kB\n", + " \u001b[32m+ typing_extensions\u001b[0m 4.9.0 pyha770c72_0 conda-forge 36kB\n", + " \u001b[32m+ networkx \u001b[0m 3.2.1 pyhd8ed1ab_0 conda-forge 1MB\n", + " \u001b[32m+ pcmsolver \u001b[0m 1.2.3 py_8 conda-forge 95kB\n", + " \u001b[32m+ typing-extensions\u001b[0m 4.9.0 hd8ed1ab_0 conda-forge 10kB\n", + " \u001b[32m+ pint \u001b[0m 0.23 pyhd8ed1ab_0 conda-forge 237kB\n", + " \u001b[32m+ annotated-types \u001b[0m 0.6.0 pyhd8ed1ab_0 conda-forge 17kB\n", + " \u001b[32m+ pydantic-core \u001b[0m 2.14.6 py310hcb5633a_1 conda-forge 2MB\n", + " \u001b[32m+ pydantic \u001b[0m 2.5.3 pyhd8ed1ab_0 conda-forge 262kB\n", + " \u001b[32m+ qcelemental \u001b[0m 0.27.1 pyhd8ed1ab_0 conda-forge 251kB\n", + " \u001b[32m+ qcengine \u001b[0m 0.29.0 pyhd8ed1ab_0 conda-forge 237kB\n", + " \u001b[32m+ optking \u001b[0m 0.2.1 pyhd8ed1ab_0 conda-forge 130kB\n", + " \u001b[32m+ psi4 \u001b[0m 1.9 py310hb14b25f_1 conda-forge 25MB\n", + "\n", + " Change:\n", + "─────────────────────────────────────────────────────────────────────────────────\n", + "\n", + " \u001b[31m- _openmp_mutex \u001b[0m 4.5 2_gnu conda-forge\u001b[32m Cached\u001b[0m\n", + " \u001b[32m+ _openmp_mutex \u001b[0m 4.5 2_kmp_llvm conda-forge 6kB\n", + "\n", + " Summary:\n", + "\n", + " Install: 36 packages\n", + " Change: 1 packages\n", + "\n", + " Total download: 385MB\n", + "\n", + "─────────────────────────────────────────────────────────────────────────────────\n", + "\n", + "\n", + "\u001b[?25l\u001b[2K\u001b[0G[+] 0.0s\n", + "Downloading 0%\n", + "Extracting 0%\u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gyaml 89.1kB @ 1.0MB/s 0.1s\n", + "msgpack-python 196.9kB @ 2.2MB/s 0.1s\n", + "[+] 0.1s\n", + "Downloading (5) 1%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G_openmp_mutex 5.7kB @ 42.4kB/s 0.0s\n", + "psutil 368.3kB @ 2.3MB/s 0.2s\n", + "gau2grid 386.9kB @ 2.3MB/s 0.2s\n", + "libhwloc 2.6MB @ 13.4MB/s 0.1s\n", + "[+] 0.2s\n", + "Downloading (5) 3%\n", + "Extracting (3) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gliblapack 14.4kB @ 62.4kB/s 0.1s\n", + "typing-extensions 10.2kB @ 37.6kB/s 0.0s\n", + "[+] 0.3s\n", + "Downloading (5) 4%\n", + "Extracting (4) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibpcm 559.1kB @ 2.0MB/s 0.2s\n", + "libcblas 14.4kB @ 50.8kB/s 0.1s\n", + "pint 237.3kB @ 710.9kB/s 0.1s\n", + "qcelemental 251.3kB @ 669.9kB/s 0.1s\n", + "[+] 0.4s\n", + "Downloading (5) 7%\n", + "Extracting (6) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gqcengine 236.6kB @ 526.2kB/s 0.2s\n", + "libgfortran-ng 23.8kB @ 52.6kB/s 0.1s\n", + "[+] 0.5s\n", + "Downloading (5) 10%\n", + "Extracting (6) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibblas 14.9kB @ 27.1kB/s 0.1s\n", + "[+] 0.6s\n", + "Downloading (4) 13%\n", + "Extracting (6) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gscipy 16.5MB @ 25.6MB/s 0.5s\n", + "networkx 1.1MB @ 1.7MB/s 0.1s\n", + "[+] 0.7s\n", + "Downloading (5) 15%\n", + "Extracting (6) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpydantic-core 1.6MB @ 2.1MB/s 0.1s\n", + "pydantic 262.4kB @ 340.8kB/s 0.1s\n", + "[+] 0.8s\n", + "Downloading (5) 18%\n", + "Extracting (6) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpugixml 114.9kB @ 139.4kB/s 0.1s\n", + "tbb 193.9kB @ 217.9kB/s 0.1s\n", + "[+] 0.9s\n", + "Downloading (5) 19%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.0s\n", + "Downloading (5) 21%\n", + "Extracting (6) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gtyping_extensions 36.1kB @ 35.8kB/s 0.1s\n", + "[+] 1.1s\n", + "Downloading (5) 22%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.2s\n", + "Downloading (5) 25%\n", + "Extracting (6) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Goptking 129.6kB @ 108.1kB/s 0.2s\n", + "[+] 1.3s\n", + "Downloading (5) 27%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpyyaml 170.6kB @ 127.1kB/s 0.1s\n", + "[+] 1.4s\n", + "Downloading (4) 30%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.5s\n", + "Downloading (5) 32%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 1.6s\n", + "Downloading (5) 34%\n", + "Extracting (4) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gllvm-openmp 41.3MB @ 25.7MB/s 1.6s\n", + "[+] 1.7s\n", + "Downloading (5) 37%\n", + "Extracting (4) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gannotated-types 17.0kB @ 10.0kB/s 0.1s\n", + "[+] 1.8s\n", + "Downloading (5) 39%\n", + "Extracting (3) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gnumpy 7.0MB @ 3.9MB/s 0.4s\n", + "dkh 64.1kB @ 34.0kB/s 0.1s\n", + "[+] 1.9s\n", + "Downloading (5) 42%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibgfortran5 1.4MB @ 730.2kB/s 0.1s\n", + "[+] 2.0s\n", + "Downloading (4) 45%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.1s\n", + "Downloading (5) 49%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpcmsolver 95.4kB @ 44.1kB/s 0.1s\n", + "[+] 2.2s\n", + "Downloading (4) 53%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpy-cpuinfo 24.9kB @ 11.0kB/s 0.1s\n", + "[+] 2.3s\n", + "Downloading (4) 56%\n", + "Extracting (6) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.4s\n", + "Downloading (5) 57%\n", + "Extracting (6) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Ggtest 404.7kB @ 162.3kB/s 0.1s\n", + "[+] 2.5s\n", + "Downloading (4) 59%\n", + "Extracting (6) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.6s\n", + "Downloading (4) 60%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.7s\n", + "Downloading (5) 62%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 2.8s\n", + "Downloading (5) 63%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibecpint 352.5kB @ 124.9kB/s 0.2s\n", + "[+] 2.9s\n", + "Downloading (4) 65%\n", + "Extracting (8) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gpsi4 25.3MB @ 8.7MB/s 1.2s\n", + "[+] 3.0s\n", + "Downloading (3) 66%\n", + "Extracting (9) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.1s\n", + "Downloading (3) 69%\n", + "Extracting (9) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.2s\n", + "Downloading (3) 69%\n", + "Extracting (9) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.3s\n", + "Downloading (3) 69%\n", + "Extracting (9) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.4s\n", + "Downloading (3) 69%\n", + "Extracting (9) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.5s\n", + "Downloading (3) 69%\n", + "Extracting (9) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.6s\n", + "Downloading (3) 69%\n", + "Extracting (9) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.7s\n", + "Downloading (3) 69%\n", + "Extracting (9) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.8s\n", + "Downloading (3) 69%\n", + "Extracting (9) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 3.9s\n", + "Downloading (3) 69%\n", + "Extracting (9) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.0s\n", + "Downloading (3) 69%\n", + "Extracting (9) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.1s\n", + "Downloading (3) 69%\n", + "Extracting (9) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.2s\n", + "Downloading (3) 69%\n", + "Extracting (9) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.3s\n", + "Downloading (3) 69%\n", + "Extracting (9) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.4s\n", + "Downloading (3) 69%\n", + "Extracting (9) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.5s\n", + "Downloading (3) 69%\n", + "Extracting (9) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.6s\n", + "Downloading (3) 70%\n", + "Extracting (9) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.7s\n", + "Downloading (3) 70%\n", + "Extracting (9) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.8s\n", + "Downloading (3) 70%\n", + "Extracting (9) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 4.9s\n", + "Downloading (3) 70%\n", + "Extracting (9) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.0s\n", + "Downloading (3) 70%\n", + "Extracting (9) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.1s\n", + "Downloading (3) 70%\n", + "Extracting (9) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.2s\n", + "Downloading (3) 72%\n", + "Extracting (8) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Glibxc-c 52.5MB @ 10.0MB/s 4.5s\n", + "libint 66.9MB @ 12.8MB/s 4.9s\n", + "[+] 5.3s\n", + "Downloading (1) 75%\n", + "Extracting (10) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.4s\n", + "Downloading (1) 76%\n", + "Extracting (8) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.5s\n", + "Downloading (1) 76%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.6s\n", + "Downloading (1) 78%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.7s\n", + "Downloading (1) 79%\n", + "Extracting (6) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.8s\n", + "Downloading (1) 82%\n", + "Extracting (6) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 5.9s\n", + "Downloading (1) 84%\n", + "Extracting (6) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.0s\n", + "Downloading (1) 86%\n", + "Extracting (6) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.1s\n", + "Downloading (1) 87%\n", + "Extracting (6) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.2s\n", + "Downloading (1) 89%\n", + "Extracting (6) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.3s\n", + "Downloading (1) 90%\n", + "Extracting (6) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.4s\n", + "Downloading (1) 94%\n", + "Extracting (6) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.5s\n", + "Downloading (1) 95%\n", + "Extracting (6) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.6s\n", + "Downloading (1) 98%\n", + "Extracting (6) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0Gmkl 164.4MB @ 24.7MB/s 6.2s\n", + "[+] 6.7s\n", + "Downloading 100%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.8s\n", + "Downloading 100%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 6.9s\n", + "Downloading 100%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.0s\n", + "Downloading 100%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.1s\n", + "Downloading 100%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.2s\n", + "Downloading 100%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.3s\n", + "Downloading 100%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.4s\n", + "Downloading 100%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.5s\n", + "Downloading 100%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.6s\n", + "Downloading 100%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.7s\n", + "Downloading 100%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.8s\n", + "Downloading 100%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 7.9s\n", + "Downloading 100%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.0s\n", + "Downloading 100%\n", + "Extracting (7) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.1s\n", + "Downloading 100%\n", + "Extracting (6) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.2s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.3s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.4s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.5s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.6s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.7s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.8s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 8.9s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.0s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.1s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.2s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.3s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.4s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.5s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.6s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.7s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.8s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 9.9s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.0s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.1s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.2s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.3s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.4s\n", + "Downloading 100%\n", + "Extracting (5) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.5s\n", + "Downloading 100%\n", + "Extracting (4) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.6s\n", + "Downloading 100%\n", + "Extracting (3) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.7s\n", + "Downloading 100%\n", + "Extracting (3) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.8s\n", + "Downloading 100%\n", + "Extracting (3) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 10.9s\n", + "Downloading 100%\n", + "Extracting (3) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.0s\n", + "Downloading 100%\n", + "Extracting (3) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.1s\n", + "Downloading 100%\n", + "Extracting (3) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.2s\n", + "Downloading 100%\n", + "Extracting (3) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.3s\n", + "Downloading 100%\n", + "Extracting (3) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.4s\n", + "Downloading 100%\n", + "Extracting (3) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.5s\n", + "Downloading 100%\n", + "Extracting (3) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.6s\n", + "Downloading 100%\n", + "Extracting (3) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.7s\n", + "Downloading 100%\n", + "Extracting (3) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.8s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 11.9s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.0s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.1s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.2s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.3s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.4s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.5s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.6s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.7s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.8s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 12.9s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.0s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.1s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.2s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.3s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.4s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.5s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.6s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.7s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.8s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 13.9s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.0s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.1s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.2s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.3s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.4s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.5s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.6s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.7s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.8s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ \u001b[2K\u001b[1A\u001b[2K\u001b[1A\u001b[2K\u001b[0G[+] 14.9s\n", + "Downloading 100%\n", + "Extracting (2) ⣾ 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\b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\bdone\n" + ] + } + ], "source": [ - "!conda install -c psi4 psi4 --yes" + "!mamba install psi4 --yes" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "DlTqd6Nn6qew" + }, "source": [ "## Preparation for running locally (SKIP IF RUNNING USING COLAB)" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "gDvh8jIO6qew" + }, "source": [ - "For today's activity we will use the package of program psi4, so we will need to install it first. Assuming you already have anaconda/miniconda installed, you can install as follows (in a new conda environment, `psi4`:\n", + "For today's activity we will use the package of program psi4, so we will need to install it first. Assuming you already have mamba installed, you can install as follows (in a new mamba environment, `psi4`:\n", "\n", "\n", - "**conda create -n psi4 psi4 psi4-rt jupyter matplotlib -c psi4/label/dev -c psi4**\n", + "**mamba create -n psi4 psi4 jupyter matplotlib**\n", "\n", - "(Note psi4 seems to be somewhat incompatible with the other software we are using in this course, so a separate environment is needed.)\n", + "(Previously, psi4 was somewhat incompatible with the other software we are using in this course, so a separate environment was needed; this may or may not still be the case.)\n", "\n", - "This will install all the psi4 binaries and a python module which can be imported from the notebook. Then activate the environment via `conda activate psi4`.\n", + "This will install all the psi4 binaries and a python module which can be imported from the notebook. Then activate the environment via `mamba activate psi4`.\n", "\n", "You also need to ensure it works in your jupyter notebook, which you can do via (in the terminal, with your `psi4` environment active):\n", "```\n", - "conda install ipykernel --name psi4\n", + "mamba install ipykernel --name psi4\n", "python -m ipykernel install --user\n", + "ipython kernel install --name psi4 --user\n", "```\n", "\n", - "To finish the installation you need to provide a scratch directory in your `~/.bash_profile`, for example (assuming you want your scratch directory in this space):\n", + "To finish the installation you need to provide a scratch directory in your environment (e.g. your `~/.zshrc` or `~/.bash_profile`), for example (assuming you want your scratch directory in this space):\n", "\n", "**export PSI_SCRATCH=/home/user_name/scratch/psi4**\n", "\n", - "Then type `source ~/.bash_profile`. Now you may open jupyter-notebook and install psi4.\n", + "Then type `source ~/.zshrc` or similar. Now you may open jupyter-notebook and install psi4.\n", "\n", - "Every time you wish to use psi4 you will need to `conda activate psi4`." + "Every time you wish to use psi4 you will need to `mamba activate psi4`." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "dk5jKcNp6qew" + }, "source": [ "# Exploring molecular interactions using electronic structure methods." ] }, { "cell_type": "code", - "execution_count": 1, - "metadata": {}, + "execution_count": 3, + "metadata": { + "id": "xgC9vL2V6qex" + }, "outputs": [], "source": [ - "import psi4 \n", + "import psi4\n", "import numpy as np" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "ZNb5VzpU6qex" + }, "source": [ "# 1. Compute the energy of a diatomic molecule\n", "\n", @@ -140,16 +1144,20 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": {}, + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "W4rtd2dC6qex", + "outputId": "06793b45-8ad4-407b-80d6-93864aeffaca" + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - " Memory set to 476.837 MiB by Python driver.\n", - "-100.34290633086249\n" + "-100.34290633086532\n" ] } ], @@ -175,37 +1183,43 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "i8JFwQXu6qex" + }, "source": [ "This corresponds to the MP2/cc-pVTZ energy for this system (HF). Input coordinates are given in [Z matrix format](https://en.wikipedia.org/wiki/Z-matrix_(chemistry)), using internal coordinates (in this case just a bond distance).\n", "\n", "\n", "### Exercise: Compute the energy of other diatomic molecules using similar methods\n", - "You might consider computing the energy of of F$_2$ and N$_2$ using the cc-pVDZ and cc-pVTZ methods. " + "You might consider computing the energy of of F$_2$ and N$_2$ using the cc-pVDZ and cc-pVTZ methods." ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "MP8j-7MB6qex" + }, "source": [ - "# 2. Compute the dipole and quadrupole moment of diatomic molecules" + "# 2. Compute the dipole moment of diatomic molecules" ] }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "ExmozmJl6qex" + }, "source": [ - "Since we are intrested in studying long range molecular interactions using classical elctrodynamics, it is necessary \n", + "Since we are intrested in studying long range molecular interactions using classical elctrodynamics, it is necessary\n", "to compute the dipole and quadrupole moments. Quantum mechanically the dipole can be computed using the one electron dipole operator:\n", "\n", "\\begin{equation}\n", - "\\hat{\\mu} = \\sum_i q_i r_i \n", + "\\hat{\\mu} = \\sum_i q_i r_i\n", "\\end{equation}\n", "\n", "where $q_i$ is the charge of the particle and $r_i$ is the position vector of the particle. The dipole moment can be computed using the wavefunction through the expectation value of the operator $\\mu$.\n", "\n", "\\begin{equation}\n", - "\\mu = <\\psi|\\hat{\\mu}|\\psi> \n", + "\\mu = <\\psi|\\hat{\\mu}|\\psi>\n", "\\end{equation}\n", "\n", "In psi4 we can obtain the dipole moment from the wafefunction object that was defined above" @@ -213,55 +1227,39 @@ }, { "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/ipykernel_58639/3045530770.py:3: FutureWarning: Using `psi4.core.get_variable` instead of `psi4.core.variable` (or `psi4.core.scalar_variable` for scalar variables only) is deprecated, and in 1.4 it will stop working\n", - "\n", - " mux = psi4.core.get_variable('HF SCF DIPOLE X') # in debye\n", - "/var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/ipykernel_58639/3045530770.py:4: FutureWarning: Using `psi4.core.get_variable` instead of `psi4.core.variable` (or `psi4.core.scalar_variable` for scalar variables only) is deprecated, and in 1.4 it will stop working\n", - "\n", - " muy = psi4.core.get_variable('HF SCF DIPOLE Y')\n", - "/var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/ipykernel_58639/3045530770.py:5: FutureWarning: Using `psi4.core.get_variable` instead of `psi4.core.variable` (or `psi4.core.scalar_variable` for scalar variables only) is deprecated, and in 1.4 it will stop working\n", - "\n", - " muz = psi4.core.get_variable('HF SCF DIPOLE Z')\n", - "/var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/ipykernel_58639/3045530770.py:6: FutureWarning: Using `psi4.core.get_variable` instead of `psi4.core.variable` (or `psi4.core.scalar_variable` for scalar variables only) is deprecated, and in 1.4 it will stop working\n", - "\n", - " quad_zz = psi4.core.get_variable('HF SCF QUADRUPOLE ZZ')\n" - ] - } - ], + "execution_count": 5, + "metadata": { + "id": "4GNxWaG16qex" + }, + "outputs": [], "source": [ "psi4.oeprop(wfn_hf_mol, 'DIPOLE', 'QUADRUPOLE', title='HF SCF')\n", "\n", - "mux = psi4.core.get_variable('HF SCF DIPOLE X') # in debye\n", - "muy = psi4.core.get_variable('HF SCF DIPOLE Y')\n", - "muz = psi4.core.get_variable('HF SCF DIPOLE Z')\n", - "quad_zz = psi4.core.get_variable('HF SCF QUADRUPOLE ZZ')" + "mux,muy,muz = psi4.core.variable('SCF DIPOLE') # in au" ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "oFThKy1i6qex", + "outputId": "ad0f34f4-fb79-475a-f27a-918fb600f193" + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "-1.9411550055413282\n", - "-3.2710162959870983\n", - "1.9411550055413282\n" + "0.0 -0.7637091437243927\n", + "0.7637091437243927\n" ] } ], "source": [ - "print(muz)\n", - "print(quad_zz)\n", + "print(mux,muz)\n", "mu = (np.sqrt(mux**2 + muy**2 + muz**2))\n", "print(mu)" ] @@ -269,22 +1267,25 @@ { "cell_type": "markdown", "metadata": { - "collapsed": true + "collapsed": true, + "id": "ce_sK5z-6qex" }, "source": [ "# 3. Compute a potential energy surface of HF dimer.\n", "\n", - "In order to study the physical interactions between two molecules it is convinient to draw \n", - "a potential energy surface along the interaction coordinate. In this section we will \n", - "obtain a potential energy profile for the most favorable dipole-dipole interaction, which is the \n", - "horizontal orientation with oposing dipole vectors, HF---FH. First we need to define a list containing \n", + "In order to study the physical interactions between two molecules it is convinient to draw\n", + "a potential energy surface along the interaction coordinate. In this section we will\n", + "obtain a potential energy profile for the most favorable dipole-dipole interaction, which is the\n", + "horizontal orientation with oposing dipole vectors, HF---FH. First we need to define a list containing\n", "the distances between both dimers for which the energy will be obtained." ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, + "execution_count": 7, + "metadata": { + "id": "HuyEarmV6qex" + }, "outputs": [], "source": [ "hf_dimer = psi4.geometry(\"\"\"\n", @@ -298,186 +1299,198 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "rstPo87D6qey" + }, "source": [ - "Next, we write a loop and in each step of the loop we compute the energy at the mp4 level of theory. " + "Next, we write a loop and in each step of the loop we compute the energy at the mp4 level of theory." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "uy57_Gxj6qey", + "outputId": "0f39afd1-5e2d-4608-b333-67844cdf106f" + }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "-200.11594007613158\n", + "-200.11594007612948\n", "1.5\n", - "-200.11908196505507\n", + "-200.11908196505945\n", "1.6\n", - "-200.12084511099732\n", + "-200.12084511099758\n", "1.7000000000000002\n", - "-200.12174182364433\n", + "-200.1217418236408\n", "1.8000000000000003\n", - "-200.12210646361427\n", + "-200.1221064636195\n", "1.9000000000000004\n", - "-200.12214740636392\n", + "-200.12214740637464\n", "2.0000000000000004\n", - "-200.12199128717546\n", + "-200.12199128716566\n", "2.1000000000000005\n", - "-200.1217185553115\n", + "-200.12171855530406\n", "2.2000000000000006\n", - 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We will use the matplotlib python library for this \n", - "purpose. The function `ref_zero_kcal` transforms the energy which is in Hartee to kcal/mol and takes the \n", + "Now we are ready to plot the potential energy profile. We will use the matplotlib python library for this\n", + "purpose. The function `ref_zero_kcal` transforms the energy which is in Hartee to kcal/mol and takes the\n", "energy of the dimer with the farthest separation as the reference energy." ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 9, + "metadata": { + "id": "sq95VUWt6qey" + }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", @@ -518,9 +1535,37 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 467 + }, + "id": "CsY3u3-j6qey", + "outputId": "b7cc001e-1158-4baa-da43-f1dea1bc5802" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Energy (kcal/mol)')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "def ref_zero_kcal(en_list):\n", " energy_kcal = []\n", @@ -529,7 +1574,7 @@ " return energy_kcal\n", "\n", "\n", - "energy_kcal = ref_zero_kcal(energy) \n", + "energy_kcal = ref_zero_kcal(energy)\n", "plt.plot(dist,energy_kcal)\n", "plt.xlabel('Distance')\n", "plt.ylabel('Energy (kcal/mol)')" @@ -537,17 +1582,22 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "id": "ht2WkFix6qey" + }, "source": [ "There are many more examples/tutorials in the Psi4 GitHub repositories, especially see `Tutorials` under the (psi4numpy repository](https://github.com/psi4/psi4numpy) for many Jupyter notebooks." ] } ], "metadata": { + "colab": { + "provenance": [] + }, "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "psi4", "language": "python", - "name": "python3" + "name": "psi4" }, "language_info": { "codemirror_mode": { @@ -559,7 +1609,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.7" + "version": "3.9.15" }, "toc": { "nav_menu": {}, @@ -573,5 +1623,5 @@ } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 1 } diff --git a/uci-pharmsci/lectures/README.md b/uci-pharmsci/lectures/README.md index 5978e89..1f0f52f 100644 --- a/uci-pharmsci/lectures/README.md +++ b/uci-pharmsci/lectures/README.md @@ -22,7 +22,8 @@ Since the material will typically draw from external files, you are recommended - Supplementary material: [MD/MD_Sandbox.ipynb](MD/MD_Sandbox.ipynb) Jupyter notebook 6. MD (part 2) and Monte Carlo (MC): [MC](MC): Lecture is Keynote/PDF format - Supplementary material: [MC/MC_Sandbox.ipynb](MC/MC_Sandbox.ipynb) Jupyter notebook -7. Solvation models: [solvation](solvation): Lecture is a Jupyter notebook (with RISE format slides) +7. ChEMBL - database querying: Use [TeachOpenCADD talktorial 1](https://github.com/volkamerlab/teachopencadd/tree/master/teachopencadd/talktorials/T001_query_chembl); for this, install `chembl_webresource_client` which is conda-installable. +. 8. Docking, scoring, and pose prediction: [docking_scoring_pose](docking_scoring_pose): Lecture is a Jupyter notebook, `docking.ipynb`, plus a PDF of old slides (which need some updating and provide the latter part of the content because conversion to Jupyter notebook format is not yet complete). An asssociated `OEDocking.ipynb` provides a docking example. 9. Library searching: [library_searching](library_searching): Includes Keynote/PDF of lecture slides, and a sandbox on library and lingo searching (Jupyter notebook format). 10. Ligand based design: [ligand_based_design](ligand_based_design): Includes a keynote/PDF of slides and a supplementary `ligand_based_design.ipynb` Jupyter notebook with supplemental materials. @@ -36,4 +37,8 @@ Since the material will typically draw from external files, you are recommended 17. (b) Mixture simulations: [mixtures](mixtures) 18. Free energy calculations and the drug discovery process -Usually we also have one guest lecture/tour by Nathan Crawford, who discusses high performance computing and takes us on a tour of the server room. +Usually we also have one guest lecture/tour by a UCI computing leader, who discusses high performance computing and takes us on a tour of the server room. + +## Obsolete, removed, or suspended content +- Solvation models: [obsolete/solvation](obsolete/solvation): Lecture is a Jupyter notebook (with RISE format slides) +- diff --git a/uci-pharmsci/lectures/SMIRNOFF_simulations/OpenFF_effort_2022.pdf b/uci-pharmsci/lectures/SMIRNOFF_simulations/OpenFF_effort_2022.pdf new file mode 100644 index 0000000..fdc2977 Binary files /dev/null and b/uci-pharmsci/lectures/SMIRNOFF_simulations/OpenFF_effort_2022.pdf differ diff --git a/uci-pharmsci/lectures/SMIRNOFF_simulations/OpenFF_effort_2022.pptx b/uci-pharmsci/lectures/SMIRNOFF_simulations/OpenFF_effort_2022.pptx new file mode 100644 index 0000000..197c88e Binary files /dev/null and b/uci-pharmsci/lectures/SMIRNOFF_simulations/OpenFF_effort_2022.pptx differ diff --git a/uci-pharmsci/lectures/SMIRNOFF_simulations/OpenFF_effort_2023.pptx b/uci-pharmsci/lectures/SMIRNOFF_simulations/OpenFF_effort_2023.pptx new file mode 100644 index 0000000..61efd4d Binary files /dev/null and b/uci-pharmsci/lectures/SMIRNOFF_simulations/OpenFF_effort_2023.pptx differ diff --git a/uci-pharmsci/lectures/SMIRNOFF_simulations/README.md b/uci-pharmsci/lectures/SMIRNOFF_simulations/README.md index a2925b5..a86cc09 100644 --- a/uci-pharmsci/lectures/SMIRNOFF_simulations/README.md +++ b/uci-pharmsci/lectures/SMIRNOFF_simulations/README.md @@ -3,7 +3,7 @@ ## Manifest ### Core content -- `mixture_simulations.ipynb`: A Jupyter notebook for conducting simple mixture simulations, where setup is done with SolvationToolkit and simulations with OpenMM. Builds on/has some similarity to previous examples seen relating to density in the `fluctuations_correlations_error` lecture. +- `mixture_simulations.ipynb`: A Jupyter notebook for conducting simple mixture simulations, where setup is done with Evaluator and simulations with OpenMM. Builds on/has some similarity to previous examples seen relating to density in the `fluctuations_correlations_error` lecture. - `smirnoff_host_guest.ipynb`: Sets up a simulation of a guest in a host (a miniature version of ligand-receptor binding) using the SMIRNOFF force field and minimizes/runs some dynamics in OpenMM. Adapted from an example I prepared in the `openforcefield` repo at https://github.com/openforcefield/openforcefield/tree/master/examples/host_guest_simulation . - `OpenFF_effort`: PDF and Keynote of slides that I'll show part of to introduce the format and force field. diff --git a/uci-pharmsci/lectures/SMIRNOFF_simulations/mixture_simulations.ipynb b/uci-pharmsci/lectures/SMIRNOFF_simulations/mixture_simulations.ipynb index 8150085..b820998 100644 --- a/uci-pharmsci/lectures/SMIRNOFF_simulations/mixture_simulations.ipynb +++ b/uci-pharmsci/lectures/SMIRNOFF_simulations/mixture_simulations.ipynb @@ -13,226 +13,94 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning: importing 'simtk.openmm' is deprecated. Import 'openmm' instead.\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "# Mixture\n", - "\n", - "tolerance 2.000000\n", - "filetype pdb\n", - "output /var/folders/41/7d3rylg503n8nt_tn0hz6v8c0000gn/T/tmphbi98883/tmpmumb08xh.pdb\n", - "add_amber_ter\n", - "\n", - "\n", - "structure /var/folders/41/7d3rylg503n8nt_tn0hz6v8c0000gn/T/tmphbi98883/tmpm8xr8u7b.pdb\n", - " number 3\n", - " inside box 0. 0. 0. 24.788816 24.788816 24.788816\n", - "end structure\n", - "\n", - "structure /var/folders/41/7d3rylg503n8nt_tn0hz6v8c0000gn/T/tmphbi98883/tmpudv5wj6r.pdb\n", - " number 100\n", - " inside box 0. 0. 0. 24.788816 24.788816 24.788816\n", - "end structure\n", - "\n", - "\n", - "source leaprc.gaff\n", - "source oldff/leaprc.ff99SB\n", - "ZPW = loadmol2 in0.mol2\n", - "ZEQ = loadmol2 in1.mol2\n", - "box = loadPdb tbox.pdb\n", - "loadamberparams in0.frcmod\n", - "loadamberparams in1.frcmod\n", - "setbox box centers\n", - "saveAmberParm box out.prmtop out.inpcrd\n", - "quit\n", - "\n", - "\n", - "# Mixture\n", - "\n", - "tolerance 2.000000\n", - "filetype pdb\n", - "output /var/folders/41/7d3rylg503n8nt_tn0hz6v8c0000gn/T/tmp0b4h7bdz/tmpe6jdw1qf.pdb\n", - "add_amber_ter\n", - "\n", - "\n", - "structure /var/folders/41/7d3rylg503n8nt_tn0hz6v8c0000gn/T/tmp0b4h7bdz/tmpaj_5foqf.pdb\n", - " number 3\n", - " inside box 0. 0. 0. 24.782739 24.782739 24.782739\n", - "end structure\n", - "\n", - "structure /var/folders/41/7d3rylg503n8nt_tn0hz6v8c0000gn/T/tmp0b4h7bdz/tmpea6yjyp0.pdb\n", - " number 100\n", - " inside box 0. 0. 0. 24.782739 24.782739 24.782739\n", - "end structure\n", - "\n", - "\n", - "source leaprc.gaff\n", - "source oldff/leaprc.ff99SB\n", - "ZNF = loadmol2 in0.mol2\n", - "ZET = loadmol2 in1.mol2\n", - "box = loadPdb tbox.pdb\n", - "loadamberparams in0.frcmod\n", - "loadamberparams in1.frcmod\n", - "setbox box centers\n", - "saveAmberParm box out.prmtop out.inpcrd\n", - "quit\n", - "\n", - "\n", - "# Mixture\n", - "\n", - "tolerance 2.000000\n", - "filetype pdb\n", - "output /var/folders/41/7d3rylg503n8nt_tn0hz6v8c0000gn/T/tmp8rpiukfj/tmp7tz0ckx5.pdb\n", - "add_amber_ter\n", - "\n", - "\n", - "structure /var/folders/41/7d3rylg503n8nt_tn0hz6v8c0000gn/T/tmp8rpiukfj/tmp_d6t22bx.pdb\n", - " number 3\n", - " inside box 0. 0. 0. 24.739450 24.739450 24.739450\n", - "end structure\n", - "\n", - "structure /var/folders/41/7d3rylg503n8nt_tn0hz6v8c0000gn/T/tmp8rpiukfj/tmpxykqzijy.pdb\n", - " number 100\n", - " inside box 0. 0. 0. 24.739450 24.739450 24.739450\n", - "end structure\n", - "\n", - "\n", - "source leaprc.gaff\n", - "source oldff/leaprc.ff99SB\n", - "ZHU = loadmol2 in0.mol2\n", - "ZKM = loadmol2 in1.mol2\n", - "box = loadPdb tbox.pdb\n", - "loadamberparams in0.frcmod\n", - "loadamberparams in1.frcmod\n", - "setbox box centers\n", - "saveAmberParm box out.prmtop out.inpcrd\n", - "quit\n", - "\n", - "\n", - "# Mixture\n", - "\n", - "tolerance 2.000000\n", - "filetype pdb\n", - "output /var/folders/41/7d3rylg503n8nt_tn0hz6v8c0000gn/T/tmprq3p55o3/tmpzn_q05nm.pdb\n", - "add_amber_ter\n", - "\n", - "\n", - "structure /var/folders/41/7d3rylg503n8nt_tn0hz6v8c0000gn/T/tmprq3p55o3/tmp32l0wicf.pdb\n", - " number 3\n", - " inside box 0. 0. 0. 28.683936 28.683936 28.683936\n", - "end structure\n", - "\n", - "structure /var/folders/41/7d3rylg503n8nt_tn0hz6v8c0000gn/T/tmprq3p55o3/tmpk8l4_nzq.pdb\n", - " number 100\n", - " inside box 0. 0. 0. 28.683936 28.683936 28.683936\n", - "end structure\n", - "\n", - "\n", - "source leaprc.gaff\n", - "source oldff/leaprc.ff99SB\n", - "ZMV = loadmol2 in0.mol2\n", - "ZMU = loadmol2 in1.mol2\n", - "box = loadPdb tbox.pdb\n", - "loadamberparams in0.frcmod\n", - "loadamberparams in1.frcmod\n", - "setbox box centers\n", - "saveAmberParm box out.prmtop out.inpcrd\n", - "quit\n", - "\n", - "\n", - "# Mixture\n", - "\n", - "tolerance 2.000000\n", - "filetype pdb\n", - "output /var/folders/41/7d3rylg503n8nt_tn0hz6v8c0000gn/T/tmp6tck4f29/tmpx3a4bbys.pdb\n", - "add_amber_ter\n", - "\n", - "\n", - "structure /var/folders/41/7d3rylg503n8nt_tn0hz6v8c0000gn/T/tmp6tck4f29/tmp2tiflzad.pdb\n", - " number 3\n", - " inside box 0. 0. 0. 28.754261 28.754261 28.754261\n", - "end structure\n", - "\n", - "structure /var/folders/41/7d3rylg503n8nt_tn0hz6v8c0000gn/T/tmp6tck4f29/tmp75aexnuw.pdb\n", - " number 100\n", - " inside box 0. 0. 0. 28.754261 28.754261 28.754261\n", - "end structure\n", - "\n", - "\n", - "source leaprc.gaff\n", - "source oldff/leaprc.ff99SB\n", - "ZGF = loadmol2 in0.mol2\n", - "ZOG = loadmol2 in1.mol2\n", - "box = loadPdb tbox.pdb\n", - "loadamberparams in0.frcmod\n", - "loadamberparams in1.frcmod\n", - "setbox box centers\n", - "saveAmberParm box out.prmtop out.inpcrd\n", - "quit\n", - "\n", - "\n", - "# Mixture\n", - "\n", - "tolerance 2.000000\n", - "filetype pdb\n", - "output /var/folders/41/7d3rylg503n8nt_tn0hz6v8c0000gn/T/tmp5cvrjoux/tmp9riwxls_.pdb\n", - "add_amber_ter\n", - "\n", - "\n", - "structure /var/folders/41/7d3rylg503n8nt_tn0hz6v8c0000gn/T/tmp5cvrjoux/tmp219uxyfv.pdb\n", - " number 3\n", - " inside box 0. 0. 0. 28.944930 28.944930 28.944930\n", - "end structure\n", - "\n", - "structure /var/folders/41/7d3rylg503n8nt_tn0hz6v8c0000gn/T/tmp5cvrjoux/tmp2hvhclw8.pdb\n", - " number 100\n", - " inside box 0. 0. 0. 28.944930 28.944930 28.944930\n", - "end structure\n", - "\n", - "\n", - "source leaprc.gaff\n", - "source oldff/leaprc.ff99SB\n", - "ZKV = loadmol2 in0.mol2\n", - "ZTE = loadmol2 in1.mol2\n", - "box = loadPdb tbox.pdb\n", - "loadamberparams in0.frcmod\n", - "loadamberparams in1.frcmod\n", - "setbox box centers\n", - "saveAmberParm box out.prmtop out.inpcrd\n", - "quit\n", - "\n" + "finished build 0\n", + "finished build 1\n", + "finished build 2\n", + "finished build 3\n", + "finished build 4\n", + "finished build 5\n", + "Finished building\n" ] } ], "source": [ - "from solvationtoolkit.solvated_mixtures import *\n", + "from openff.evaluator.protocols.coordinates import BuildCoordinatesPackmol\n", + "from openff.evaluator.substances import Substance, Component, MoleFraction, ExactAmount\n", + "from openeye import oeiupac\n", + "from openeye import oechem\n", + "import shutil, os\n", + "\n", + "#Number of solute/solvent molecules\n", + "Nsolu = 3\n", + "Nsolv = 100\n", "\n", - "#In this particular instance I'll just look at six solutes/solvent mixtures (not an all-by-all combination) which are pre-specified\n", "#solute names\n", "solutes = ['phenol', 'toluene', 'benzene', 'methane', 'ethanol', 'naphthalene']\n", "#Solvent names\n", "solvents = ['cyclohexane', 'cyclohexane', 'cyclohexane', 'octanol', 'octanol', 'octanol']\n", "\n", - "#Number of solute/solvent molecules\n", - "Nsolu = 3\n", - "Nsolv = 100\n", + "# Generate SMILES for solutes and solvents and store for use in building mixtures\n", + "solute_smiles = []\n", + "solvent_smiles = []\n", + "for name_pair in zip(solutes, solvents):\n", + " #print(name_pair)\n", + " solu_mol = oechem.OEMol()\n", + " oeiupac.OEParseIUPACName(solu_mol, name_pair[0])\n", + " solute_smiles.append(oechem.OECreateIsoSmiString(solu_mol))\n", + " solv_mol = oechem.OEMol()\n", + " oeiupac.OEParseIUPACName(solv_mol, name_pair[1])\n", + " solvent_smiles.append(oechem.OECreateIsoSmiString(solv_mol))\n", + " \n", "\n", - "#Construct systems\n", + "# Storage for the mixtures we've built\n", + "mixtures = []\n", + "outdir = 'coordinate_files'\n", + "if not os.path.isdir(outdir): os.mkdir(outdir)\n", + " \n", + "# Loop and build mixtures\n", "for idx in range( len( solutes) ):\n", " # Define new mixture\n", - " mixture = MixtureSystem()\n", + " mixture_build = BuildCoordinatesPackmol(\"\")\n", + " substance = Substance()\n", " # Add solute and solvent\n", - " mixture.addComponent(name=solutes[idx], number=Nsolu)\n", - " mixture.addComponent(name=solvents[idx], number=Nsolv)\n", + " substance.add_component(Component(solute_smiles[idx], role=Component.Role.Solute), ExactAmount(Nsolu))\n", + " substance.add_component(Component(solvent_smiles[idx], role=Component.Role.Solvent), ExactAmount(Nsolv))\n", + " #substance.add_component(Component(\"Cc1ccccc1\", role=Component.Role.Solvent), MoleFraction(0.1))\n", + " #substance.add_component(Component(\"C1CCCCC1\", role=Component.Role.Solvent), MoleFraction(0.9))\n", " # Note you can optionally specify mole fraction instead, or a mix of numbers/mole fractions, etc.\n", " \n", - " # Build system, including AMBER input files (but not GROMACS)\n", - " mixture.build(amber=True, gromacs=True)" + " #substance.max_molecules = 150\n", + " mixture_build.substance = substance\n", + "\n", + " #build\n", + " mixture_build.execute()\n", + " \n", + " # Do file bookkeeping so the output files don't overwrite one another\n", + " outfile = os.path.join(outdir, f'coordinate_file{idx}.pdb')\n", + " shutil.copy( mixture_build.coordinate_file_path, outfile )\n", + " mixture_build.coordinate_file_path = outfile\n", + " \n", + " #Store details\n", + " mixtures.append(mixture_build)\n", + "\n", + " print(f\"finished build {idx}\")\n", + " \n", + " \n", + "print(\"Finished building\")\n" ] }, { @@ -244,13 +112,25 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "fe5a970d2c274d4eaee634af877b7934", + "model_id": "7ccdbe76c21f44079fe6e5bd148429e2", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8e108f00aa524401bd7db74645456fb4", "version_major": 2, "version_minor": 0 }, @@ -266,9 +146,9 @@ "#Import MDTraj\n", "import mdtraj as md\n", "#Load \"trajectory\" (structures)\n", - "#You can load from either format (SolvationToolkit generates both)\n", + "#You can load from either format\n", "#traj = md.load( 'data/amber/phenol_cyclohexane_3_100.inpcrd', top = 'data/amber/phenol_cyclohexane_3_100.prmtop' )\n", - "traj = md.load( 'data/gromacs/phenol_cyclohexane_3_100.gro')\n", + "traj = md.load( os.path.join(outdir, 'coordinate_file0.pdb'))#'data/gromacs/phenol_cyclohexane_3_100.gro')\n", "\n", "#Input viewer\n", "import nglview\n", @@ -279,9 +159,10 @@ "#Try some of the following to modify representations\n", "view.clear_representations()\n", "view.add_licorice('all')\n", - "view.add_licorice('1-3', color = \"blue\") #For selection info, see http://arose.github.io/ngl/doc/#User_manual/Usage/Selection_language\n", - "view.add_surface('1', opacity=0.3)\n", - "view.add_surface('2, 3', color = 'red', opacity=0.3)\n", + "# Select the first three \"residues\" of chain A for special display\n", + "# (Evaluator seems to make the first substance to be chain A, and then individual molecules are residues within that chain)\n", + "view.add_licorice('1:A or 2:A or 3:A', color = \"blue\") #NGLview has a whole selection lanuage\n", + "view.add_surface('1:A or 2:A or 3:A', opacity=0.3)\n", "\n", "#Show the view. Note that this needs to be the last command used to manipulate the view, i.e. if you modify the\n", "#representation after this, your view will be empty.\n", @@ -306,21 +187,21 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Import the SMIRNOFF forcefield engine and some useful tools\n", - "from openforcefield.topology import Molecule, Topology\n", - "from openforcefield.typing.engines.smirnoff import ForceField\n", + "from openff.toolkit.topology import Molecule, Topology\n", + "from openff.toolkit.typing.engines.smirnoff import ForceField\n", "import openeye.oechem as oechem #Here we'll use OpenEye tookits, but RDKIt use is also possible\n", "\n", "# We use PDBFile to get OpenMM topologies from PDB files\n", - "from simtk.openmm.app import PDBFile\n", + "try:\n", + " from openmm.app import PDBFile\n", + "except:\n", + " from simtk.openmm.app import PDBFile\n", "\n", - "# We'll use OpenMM for simulations/minimization\n", - "from simtk import openmm, unit\n", - "from simtk.openmm import app\n", "# MDTraj for working with trajectories; time for timing\n", "import time\n", "import mdtraj" @@ -335,40 +216,31 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Specify names of molecules that are components of the system\n", - "mol_filenames = ['phenol', 'cyclohexane']\n", + "molnames = ['phenol', 'cyclohexane']\n", "\n", - "# Load OEMols of components of system - SMIRNOFF requires OEMols of the components\n", - "# and an OpenMM topology as input\n", + "# Get molecules for components - required as input for OpenFF\n", "oemols = []\n", - "flavor = oechem.OEIFlavor_Generic_Default | oechem.OEIFlavor_MOL2_Default | oechem.OEIFlavor_MOL2_Forcefield\n", - " #input flavor to use for reading mol2 files (so that it can understand GAFF atom names)\n", - "# Loop over molecule files and load oemols\n", - "for name in mol_filenames:\n", + "for name in molnames:\n", " mol = oechem.OEGraphMol()\n", - " filename = 'data/monomers/'+name+'.mol2'\n", - " ifs = oechem.oemolistream(filename)\n", - " ifs.SetFlavor( oechem.OEFormat_MOL2, flavor)\n", - " oechem.OEReadMolecule(ifs, mol )\n", - " oechem.OETriposAtomNames(mol) #Right now we have GAFF atom names, which OE doesn't like; reassign\n", + " oeiupac.OEParseIUPACName(mol, name)\n", " oemols.append(mol)\n", - " ifs.close()\n", "\n", "# Build set of OpenFF mols from OE molecules\n", "OFFmols = []\n", "for mol in oemols:\n", " OFFmols.append( Molecule.from_openeye(mol))\n", " \n", - "# Load OpenFF 1.0 force field, plus TIP3P water just in case we use it.\n", - "ff = ForceField('openff-1.0.0.offxml', 'test_forcefields/tip3p.offxml') \n", + "# Load OpenFF 2.0 force field\n", + "ff = ForceField('openff-2.0.0.offxml') \n", "\n", - "# Get OpenMM topology for mixture of phenol and cyclohexane from where SolvationToolkit created\n", - "# it on disk\n", - "pdbfile = PDBFile('data/packmol_boxes/phenol_cyclohexane_3_100.pdb')\n", + "# Get OpenMM topology for mixture of phenol and cyclohexane from where they were created\n", + "# it on disk (the first mixture we built)\n", + "pdbfile = PDBFile(mixtures[0].coordinate_file_path)\n", "\n", "# Create OpenFF Topology\n", "off_topology = Topology.from_openmm(openmm_topology = pdbfile.topology, unique_molecules = OFFmols)\n", @@ -386,40 +258,29 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Starting simulation\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/dmobley/anaconda3/envs/drugcomp2/lib/python3.7/site-packages/ipykernel_launcher.py:33: DeprecationWarning: time.clock has been deprecated in Python 3.3 and will be removed from Python 3.8: use time.perf_counter or time.process_time instead\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed time 9.78 seconds\n", + "Starting simulation\n", + "Elapsed time 14.19 seconds\n", "Done!\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/dmobley/anaconda3/envs/drugcomp2/lib/python3.7/site-packages/ipykernel_launcher.py:35: DeprecationWarning: time.clock has been deprecated in Python 3.3 and will be removed from Python 3.8: use time.perf_counter or time.process_time instead\n" - ] } ], "source": [ + "from datetime import datetime\n", + "try:\n", + " import openmm\n", + " from openmm import app, unit\n", + " from openmm.app import PDBFile\n", + "except ImportError:\n", + " from simtk import openmm, app, unit\n", + " from simtk.openmm.app import PDBFile\n", + "\n", "# Set how many steps we'll run and other run parameters\n", "num_steps=10000\n", "trj_freq = 100 #Trajectory output frequency\n", @@ -452,10 +313,11 @@ "\n", "# Run the dynamics\n", "print(\"Starting simulation\")\n", - "start = time.clock()\n", + "start = datetime.now()\n", "simulation.step(num_steps)\n", - "end = time.clock()\n", - "print(\"Elapsed time %.2f seconds\" % (end-start))\n", + "end = datetime.now()\n", + "\n", + "print(\"Elapsed time %.2f seconds\" % (end-start).total_seconds())\n", "#netcdf_reporter.close()\n", "reporter.close()\n", "print(\"Done!\")\n" @@ -470,18 +332,18 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "cc3d0cbc82df42959c172838388f6b02", + "model_id": "4dd45991187948169ce4df99f26acd42", "version_major": 2, "version_minor": 0 }, "text/plain": [ - "NGLWidget(count=100)" + "NGLWidget(max_frame=99)" ] }, "metadata": {}, @@ -496,9 +358,9 @@ "#Try some of the following to modify representations\n", "view.clear_representations()\n", "view.add_licorice('all')\n", - "view.add_licorice('1-3', color = \"blue\") #For selection info, see http://arose.github.io/ngl/doc/#User_manual/Usage/Selection_language\n", - "view.add_surface('1', opacity=0.3)\n", - "view.add_surface('2, 3', color = 'red', opacity=0.3)\n", + "view.add_licorice('1:A or 2:A or 3:A', color = \"blue\") \n", + "view.add_surface('1:A', opacity=0.3)\n", + "view.add_surface('2:A or 3:A', color = 'red', opacity=0.3)\n", "\n", "view #Note that if you view a movie and keep it playing, your notebook will run a hair slow..." ] @@ -508,10 +370,12 @@ "metadata": {}, "source": [ "## Other possibly interesting things to try:\n", - "* Find the average distance from phenol to phenol\n", - "* Calculate the density or volume of the system\n", + "* Find the average distance from phenol to phenol (would need longer simulation)\n", + "* Calculate the density or volume of the system (would need to add barostat)\n", "* etc.\n", "\n", + "Note: I would need to check that I've configured PBC properly above, and if I have, update the wrapping so the trajectory visualization correctly handles it.\n", + "\n", "(Drawing on MDTraj - see docs online)" ] }, @@ -528,9 +392,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [conda env:drugcomp2]", + "display_name": "drugcomp", "language": "python", - "name": "conda-env-drugcomp2-py" + "name": "drugcomp" }, "language_info": { "codemirror_mode": { @@ -542,7 +406,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.8.12" }, "toc": { "nav_menu": {}, diff --git a/uci-pharmsci/lectures/SMIRNOFF_simulations/smirnoff_host_guest.ipynb b/uci-pharmsci/lectures/SMIRNOFF_simulations/smirnoff_host_guest.ipynb index db376c3..3eed79a 100644 --- a/uci-pharmsci/lectures/SMIRNOFF_simulations/smirnoff_host_guest.ipynb +++ b/uci-pharmsci/lectures/SMIRNOFF_simulations/smirnoff_host_guest.ipynb @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -31,30 +31,30 @@ "text": [ "Is your OEChem licensed? True\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Warning: importing 'simtk.openmm' is deprecated. Import 'openmm' instead.\n" - ] } ], "source": [ "from openeye import oechem # OpenEye Python toolkits\n", - "import oenotebook as oenb\n", + "\n", "# Check license\n", "print(\"Is your OEChem licensed? \", oechem.OEChemIsLicensed())\n", "from openeye import oeomega # Omega toolkit\n", "from openeye import oequacpac #Charge toolkit\n", "from openeye import oedocking # Docking toolkit\n", - "from oeommtools import utils as oeommutils # Tools for OE/OpenMM\n", - "from simtk import unit #Unit handling for OpenMM\n", - "from simtk.openmm import app\n", - "from simtk.openmm.app import PDBFile\n", + "\n", + "try:\n", + " import openmm\n", + " from openmm import app, unit\n", + " from openmm.app import PDBFile\n", + "except ImportError:\n", + " from simtk import openmm, app, unit\n", + " from simtk.openmm.app import PDBFile\n", + "\n", "# import openff tools\n", "from openff.toolkit.typing.engines.smirnoff import *\n", "import os\n", + "\n", + "# If you get an error with this, you may need to conda-install pdbfixer:\n", "from pdbfixer import PDBFixer # for solvating" ] }, @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -95,23 +95,27 @@ "source": [ "## Quickly draw your guest and make sure it's what you intended\n", "\n", - "OENotebook is super useful and powerful; see https://www.eyesopen.com/notebooks-directory. Here we only use a very small amount of what's available, drawing on http://notebooks.eyesopen.com/introduction-to-oenb.html" + "We'll do this as we did earlier in the class." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "" ] }, - "execution_count": 3, - "metadata": {}, + "execution_count": 10, + "metadata": { + "image/png": { + "width": 200 + } + }, "output_type": "execute_result" } ], @@ -120,8 +124,13 @@ "mol = oechem.OEMol()\n", "# Convert SMILES\n", "oechem.OESmilesToMol(mol, guest_smiles)\n", + "\n", "# Draw\n", - "oenb.draw_mol(mol)" + "from IPython.display import Image\n", + "import openeye.oedepict as oedepict # Use OpenEye depiction toolkit\n", + "oedepict.OEPrepareDepiction(mol)\n", + "oedepict.OERenderMolecule(\"DepictGuest.png\", mol)\n", + "Image('DepictGuest.png',width = 200)\n" ] }, { @@ -136,7 +145,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -173,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -209,9 +218,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# Read in host file\n", "hostfile_charged = hostfile.replace('.mol2', '_charged.mol2')\n", @@ -238,7 +258,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -310,9 +330,58 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/3dmoljs_load.v0": "
\n

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n jupyter labextension install jupyterlab_3dmol

\n
\n", + "text/html": [ + "
\n", + "

You appear to be running in JupyterLab (or JavaScript failed to load for some other reason). You need to install the 3dmol extension:
\n", + " jupyter labextension install jupyterlab_3dmol

\n", + "
\n", + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import py3Dmol\n", "#First we assign the py3Dmol.view as viewer\n", @@ -330,9 +399,43 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ab3e83ae99ea45c894ebd2ddee05056f", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning: importing 'simtk.openmm' is deprecated. Import 'openmm' instead.\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c924f6791c09471f93f90bf9adf04529", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "NGLWidget()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Optional: can use nglview and mdtraj to load and view host and guest structures\n", "# To execute this you'll need `nglview` for visualization and `mdtraj` for working with trajectory files\n", @@ -371,9 +474,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Host+guest number of atoms 211\n", + "Number of atoms after applying PDBFixer: 4554\n" + ] + } + ], "source": [ "# Join OEMols into complex\n", "complex = host.CreateCopy()\n", @@ -417,7 +529,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -461,7 +573,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -469,12 +581,12 @@ "\n", "from openff.toolkit.topology import Molecule, Topology\n", "from openff.toolkit.typing.engines.smirnoff import ForceField\n", - "ff = ForceField('openff-1.0.0.offxml', 'test_forcefields/tip3p.offxml') \n", + "ff = ForceField('openff-2.0.0.offxml') \n", "\n", "\n", "# Build list of OpenFF Molecules for passing into topology creation\n", - "mols = []\n", - "charged_mols= []\n", + "mols = [] # These are all molecules\n", + "charged_mols= [] # These are just those moolecules for which we assigned charges - the ions and the host\n", "for mol in oemols:\n", " mols.append(Molecule.from_openeye(mol))\n", "for mol in charged_oemols:\n", @@ -505,9 +617,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Energy before minimization (kcal/mol): 5.1e+02\n", + "Energy after minimization (kcal/mol): -1.5e+04\n" + ] + } + ], "source": [ "import simtk.openmm as openmm\n", "\n", @@ -540,9 +661,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting simulation\n", + "Elapsed time 0.64 seconds\n", + "Done!\n" + ] + } + ], "source": [ "# Set up NetCDF reporter for storing trajectory; prep for Langevin dynamics\n", "import time\n", @@ -576,9 +707,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "23118fd378dc4f24abf71a7031a26de0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "NGLWidget(max_frame=9)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Load stored trajectory using MDTraj; the trajectory doesn't contain chemistry info so we also load a PDB\n", "traj= mdtraj.load(os.path.join(datadir, 'trajectory.nc'), top=os.path.join(datadir, 'complex_solvated.pdb'))\n", diff --git a/uci-pharmsci/lectures/cluster_and_visualize/Introduction_To_MSMs.key b/uci-pharmsci/lectures/cluster_and_visualize/Introduction_To_MSMs.key deleted file mode 100644 index 247e250..0000000 --- a/uci-pharmsci/lectures/cluster_and_visualize/Introduction_To_MSMs.key +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:637e740a7d523bf9d4302840ae7c18e6dee5eb2935711130e68d41d74369e89f -size 14797302 diff --git a/uci-pharmsci/lectures/cluster_and_visualize/Introduction_To_MSMs.pdf b/uci-pharmsci/lectures/cluster_and_visualize/Introduction_To_MSMs.pdf index 154367f..71d79e2 100644 Binary files a/uci-pharmsci/lectures/cluster_and_visualize/Introduction_To_MSMs.pdf and b/uci-pharmsci/lectures/cluster_and_visualize/Introduction_To_MSMs.pdf differ diff --git a/uci-pharmsci/lectures/cluster_and_visualize/Introduction_To_MSMs.pptx b/uci-pharmsci/lectures/cluster_and_visualize/Introduction_To_MSMs.pptx new file mode 100644 index 0000000..7a7cc8e Binary files /dev/null and b/uci-pharmsci/lectures/cluster_and_visualize/Introduction_To_MSMs.pptx differ diff --git a/uci-pharmsci/lectures/cluster_and_visualize/MDTraj_simulate_and_view.ipynb b/uci-pharmsci/lectures/cluster_and_visualize/MDTraj_simulate_and_view.ipynb index a9302d7..5efb95e 100644 --- a/uci-pharmsci/lectures/cluster_and_visualize/MDTraj_simulate_and_view.ipynb +++ b/uci-pharmsci/lectures/cluster_and_visualize/MDTraj_simulate_and_view.ipynb @@ -5,12 +5,12 @@ "metadata": {}, "source": [ "# MDTraj and OpenMM\n", - "This example builds on the MDTraj \"Simulation with OpenMM\" example (http://mdtraj.org/latest/examples/openmm.html) to set up a very simple simulation of a tryptophan zipper with the AMBER force fields in implicit solvent, run a few steps of dynamics, and visualize the result. " + "This example builds on the MDTraj \"Simulation with OpenMM\" example (https://mdtraj.org/1.8.0/examples/openmm.html) to set up a very simple simulation of a tryptophan zipper with the AMBER force fields in implicit solvent, run a few steps of dynamics, and visualize the result. " ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -18,24 +18,16 @@ "import mdtraj\n", "import mdtraj.reporters\n", "from simtk import unit #Units is an extremely useful module that allows numbers to carry around assigned units\n", - "import openmm\n", + "import openmm as mm\n", "#from simtk.openmm import app\n", "from openmm import app" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/dmobley/opt/anaconda3/envs/drugcomp/lib/python3.8/site-packages/mdtraj/formats/pdb/pdbfile.py:196: UserWarning: Unlikely unit cell vectors detected in PDB file likely resulting from a dummy CRYST1 record. Discarding unit cell vectors.\n", - " warnings.warn('Unlikely unit cell vectors detected in PDB file likely '\n" - ] - }, { "name": "stdout", "output_type": "stream", @@ -86,13 +78,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2d76f826b2534a358a1c456966b8e834", + "model_id": "ecc1988bd97a48c6a1467b8cbc55c354", "version_major": 2, "version_minor": 0 }, @@ -111,7 +103,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "75b219c99b39427babc926b2cf1943be", + "model_id": "4e7ef9af33a344b7822061f959bf6dfe", "version_major": 2, "version_minor": 0 }, @@ -198,9 +190,9 @@ "metadata": { "anaconda-cloud": {}, "kernelspec": { - "display_name": "Python [conda env:drugcomp] *", + "display_name": "drugcomp23", "language": "python", - "name": "conda-env-drugcomp-py" + "name": "drugcomp23" }, "language_info": { "codemirror_mode": { @@ -212,7 +204,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.16" }, "widgets": { "state": { diff --git a/uci-pharmsci/lectures/cluster_and_visualize/PyEMMA_Ligand_binding_mode_clustering.ipynb b/uci-pharmsci/lectures/cluster_and_visualize/PyEMMA_Ligand_binding_mode_clustering.ipynb index 4d814d7..118ab30 100644 --- a/uci-pharmsci/lectures/cluster_and_visualize/PyEMMA_Ligand_binding_mode_clustering.ipynb +++ b/uci-pharmsci/lectures/cluster_and_visualize/PyEMMA_Ligand_binding_mode_clustering.ipynb @@ -1,3383 +1,3450 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "WO2IxX1g_dZZ" - }, - "source": [ - "# Cluster ligand binding modes with an MSM\n", - "\n", - "In simulations, ligand binding modes often interconvert somewhat slowly. In this notebook we work through an example which takes stored simulation data and builds an MSM to cluster the ligand binding modes which were sampled. This is based on a research problem we encountered, but takes a slightly simplified form.\n", - "\n", - "Please see [the PyEMMA LiveCoMS article](https://doi.org/10.33011/livecoms.1.1.5965) and associated GitHub repository [https://github.com/markovmodel/pyemma_tutorials](https://github.com/markovmodel/pyemma_tutorials) for additional background and tutorials.\n", - "\n", - "## Preparation for using Google Colab (SKIP IF RUNNING LOCALLY)\n", - "\n", - "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MobleyLab/drug-computing/blob/master/uci-pharmsci/lectures/cluster_and_visualize/PyEMMA_Ligand_binding_mode_clustering.ipynb)\n", - "\n", - "If you are running this on Google Colab, you need to take a couple additional steps of preparation:\n", - "1) Mount your Google Drive to this notebook:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Cdy16DIV_dZb", - "outputId": "a06d8cc1-4a44-4464-a167-978add14efdf" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Mounted at /content/drive\n" - ] - } - ], - "source": [ - "# Run cell if using collab\n", - "\n", - "# Mount google drive to Colab Notebooks to access files\n", - "from google.colab import drive\n", - "drive.mount('/content/drive', force_remount = True)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "0NqzAb_Y_dZc" - }, - "source": [ - "2) **If you are running this on Google Colab, install dependencies**:\n", - "\n", - "(You will need to run this cell twice if you encounter an error the first time.)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "id": "DNUPudjHhxfb", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "9f8b2cfc-d30c-4155-861e-0e9110fc9bdd" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "✨🍰✨ Everything looks OK!\n", - "✨🍰✨ Everything looks OK!\n", - "CPU times: user 27.4 ms, sys: 11.3 ms, total: 38.7 ms\n", - "Wall time: 1.33 s\n" - ] - } - ], - "source": [ - "%%time \n", - "### run this block if running notebook on google collab\n", - "\n", - "# Import condacolab python library and install condacolab (~5-10 minutes). \n", - "\n", - "# Make sure to rerun this cell after it crashes\n", - "!pip install --target=$nb_path -q condacolab\n", - "import condacolab\n", - "condacolab.install()\n", - "\n", - "# Check condacolab to ensure that it works\n", - "condacolab.check()" - ] - }, - { - "cell_type": "code", - "source": [ - "# Install packages - MUST be in a separate cell from the above because of library issues with how condacolab works.\n", - "!conda config --add channels conda-forge\n", - "!conda install pyemma " - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "xDaN08l1AG-3", - "outputId": "7ae530b7-2352-4629-ec86-0aa429bceacb" - }, - "execution_count": 4, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Warning: 'conda-forge' already in 'channels' list, moving to the top\n", - "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", - "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", - "\n", - "# All requested packages already installed.\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_lekq2ti_dZd" - }, - "source": [ - "3) **Navigate to where you have the content for this lecture on Drive**; edit this path if needed" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "qvSB7KUs_dZe", - "outputId": "0822aa60-26fd-4abc-9841-2fa93c3263e2" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "/content/drive/MyDrive/drug-computing/uci-pharmsci/lectures/cluster_and_visualize\n", - "4XH_1-top.pdb lig-first.pdb test.h5\n" - ] - } - ], - "source": [ - "# Move into directory so that files can be accessed\n", - "%cd /content/drive/MyDrive/drug-computing/uci-pharmsci/lectures/cluster_and_visualize\n", - "# list files/folders in directory\n", - "%ls" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "eC55dSQD_dZe" - }, - "source": [ - "## Prep for running locally\n", - "\n", - "If running locally, you need a standard scientific Python installation with `openmm` installed (as is standard for this class), and then you additionally need to `conda install -c conda-forge pyemma`. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "0Gt4MUdJhuiY" - }, - "source": [ - "## Featurization\n", - "\n", - "Our first step is \"featurization\" -- to try and identify which features or coordinates in our simulations can separate the slow transitions between ligand binding modes.\n", - "\n", - "Import the [pyEMMA coordinates](http://emma-project.org/latest/api/index_coor.html) package: \n", - "> This package contains functions and classes for reading and writing trajectory files, extracting order parameters from them (such as distances or angles), as well as various methods for dimensionality reduction and clustering." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "i-eVcBVShuif" - }, - "outputs": [], - "source": [ - "import pyemma.coordinates as coor\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "UnU19tUthuih" - }, - "outputs": [], - "source": [ - "#Define our input MD files\n", - "md_nc = '4XH_1-md_subsamp.nc'\n", - "topfile = '4XH_1-top.pdb'" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "m5etBseChuii" - }, - "source": [ - "From the coordinates module, use the [featurizer](http://emma-project.org/latest/api/generated/pyemma.coordinates.featurizer.html#pyemma.coordinates.featurizer) to select the ligand heavy atoms." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "TT7NXb4Xhuii", - "outputId": "e625faee-7de0-4dad-8211-0a93f9187faa" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Selected 11 heavy atoms\n" - ] - } - ], - "source": [ - "feat = coor.featurizer(topfile)\n", - "lig_atoms = feat.select(\"resname LIG and not type H\")\n", - "feat.add_selection(lig_atoms)\n", - "print('Selected %s heavy atoms' % len(lig_atoms))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ljSgmabNhuij" - }, - "source": [ - "Load the coordinates from disc. Often, coordinates will not fit into memory, so we'll just create a loader by specifying the files as follows using the [source module](http://emma-project.org/latest/api/generated/pyemma.coordinates.source.html):" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "NDlk7Gnyhuik", - "outputId": "da5a0219-371f-4aaf-a54f-36499371ab63" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "number of trajectories = 1\n", - "trajectory length = 750\n", - "number of dimension = 33\n" - ] - } - ], - "source": [ - "inp = coor.source(md_nc, feat)\n", - "print('number of trajectories = ',inp.number_of_trajectories())\n", - "print('trajectory length = ',inp.trajectory_length(0))\n", - "print('number of dimension = ',inp.dimension())" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ZYX5dMsuhuil" - }, - "source": [ - "# Dimensionality Reduction\n", - "## Time-lagged independent component analysis (TICA)\n", - "[TICA theory](http://docs.markovmodel.org/lecture_tica.html)\n", - "\n", - "Run TICA for a series of lag times and select a suitable lag time for further analysis. In order to do so, have a look at the implied timescales generated by TICA. The [TICA object](http://emma-project.org/latest/api/generated/pyemma.coordinates.tica.html#pyemma.coordinates.tica) has an attribute timescales.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "id": "1MPLRhLqhuil" - }, - "outputs": [], - "source": [ - "#Frames in MD simulation were taken every 10ps \n", - "#Give a range of different lag times to try out\n", - "dt = 10\n", - "lag_list = np.arange(1, 20,5)\n", - "nlags = lag_list.shape[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "hqsQ7NiRhuim", - "outputId": "08958f4c-7fc2-4cfb-c073-aaa9b1ed09d9" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "type of Y: \n", - "length of Y: 1\n", - "shape of first element: (750, 33)\n" - ] - } - ], - "source": [ - "#Load the trajectory files\n", - "Y = inp.get_output()\n", - "print(\"type of Y:\", type(Y))\n", - "print(\"length of Y:\", len(Y))\n", - "print(\"shape of first element:\", Y[0].shape)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "id": "-zKfT3Mthuin" - }, - "outputs": [], - "source": [ - "#Try out different lag times fr \n", - "lag_times = []\n", - "for lag in lag_list:\n", - " data = coor.tica(np.vstack(Y),lag, kinetic_map=True, var_cutoff=0.95, reversible=True)\n", - " lag_times.append(data.timescales[:10])\n", - "lag_times = np.asarray(lag_times)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "id": "HRY63hpxhuin" - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 303 - }, - "id": "SiBK7ZnHhuio", - "outputId": "84fae0da-8c86-4341-fc7c-9f7f1574507b" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Text(0, 0.5, 'Timescales [$ps$]')" - ] - }, - "metadata": {}, - "execution_count": 15 - }, - { - "output_type": "display_data", - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - } - } - ], - "source": [ - "#Plot the different lag times\n", - "plt.plot(dt*lag_list, dt*lag_times, linewidth=2)\n", - "plt.xlabel(r\"Lag time [$ps$]\", fontsize=12)\n", - "plt.ylabel(r\"Timescales [$ps$]\", fontsize=12)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "QNuUMDuThuio", - "outputId": "b67efddd-ec50-4fe3-d6ca-124878ac6f24" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "TICA dimension 17\n" - ] - } - ], - "source": [ - "#Select a lag time where the lines start to level off\n", - "#Here, I'll use 80ps\n", - "lag = int(80/ dt)\n", - "\n", - "tica_obj = coor.tica(inp, lag=lag, kinetic_map=True, var_cutoff=0.95, reversible=True)\n", - "print('TICA dimension ', tica_obj.dimension())" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UGuXMp8uhuip" - }, - "source": [ - "Plot the first two TICA components. The TICA object has a get_output() method." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "kbTE4XLhhuip", - "outputId": "7fb96129-28ff-4d9a-ae44-21e30c183989" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "number of trajectories = 1\n", - "number of frames = 750\n", - "number of dimensions = 17\n" - ] - } - ], - "source": [ - "Y = tica_obj.get_output() # get tica coordinates\n", - "print('number of trajectories = ', np.shape(Y)[0])\n", - "print('number of frames = ', np.shape(Y)[1])\n", - "print('number of dimensions = ',np.shape(Y)[2])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "-qtd2SMjhuiq" - }, - "source": [ - "PyEMMA has a [plotting module](http://emma-project.org/latest/api/index_plots.html#plots-plotting-tools-pyemma-plots) which we can use to plot the TICA coordinates" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "id": "qMb4xog5huiq" - }, - "outputs": [], - "source": [ - "from pyemma import plots" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 296 - }, - "id": "Algdtrp0huiq", - "outputId": "b1a77576-f3ef-4c05-f4a8-9f74799d4c77" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Text(0, 0.5, 'TIC 2')" - ] - }, - "metadata": {}, - "execution_count": 19 - }, - { - "output_type": "display_data", - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - } - } - ], - "source": [ - "plots.plot_free_energy(np.vstack(Y)[:, 0], np.vstack(Y)[:, 1], nbins=25)\n", - "plt.xlabel('TIC 1')\n", - "plt.ylabel('TIC 2')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "UxtCaBx4huiq" - }, - "source": [ - "# Clustering the data\n", - "\n", - "## K-means clustering\n", - "Use [k-means clustering](http://emma-project.org/latest/api/generated/pyemma.coordinates.clustering.KmeansClustering.html#pyemma.coordinates.clustering.KmeansClustering) and get the discrete trajectories" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 54, - "referenced_widgets": [ - "12eac87dfa7f4d6daf4bb6a0b1c53d8d", - "c39b58cabaf34f54ab7512271d50a065", - "30680783583f49dfac3fce1c7f921973", - "ec27fc56d99b4772aab7e8d6bf4d638c", - "3f135515027f4699a1c247d0a67f9157", - "aeddc71587da47e692fa91cae62e375f", - "00255cf278c549cd904abf4358b182b8", - "c15777fe05ab4dedb5bfeb3b19e15166", - "54ee9fb171a94cb5bee04716ebd801ab", - "23b1cc84e4454a3895b8ca9922be0a43", - "26d5dc84664d49fcb953f8742fcde39c", - "6929c673d0384b3a874175ca41105281", - "6033fd93fe9c461d89f121278ce8552e", - "36499e52c5be47fe888aaf1ed6f9738d", - "3eaa23007b60461e83c84ff50fd11ab0", - "114659aa609444188cee25dc014f3117", - "dd9bb43747bb4e9d86ea0fe37dae8fa8", - "c63fe38e8ea644f8a4868952015c3065", - "b4f2cc3edced4cdba7abed2afef7a06a", - "54250664e6db440088db1131eb9ff677", - "646c7a54bbe441d08376df397f590707", - "0f4f91c4c0a243919210445a7ca1fb48" - ] - }, - "id": "0ZEArxInhuiq", - "outputId": "84567164-288e-4a42-e7b5-7c950286fa8b" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "31-12-21 01:18:00 pyemma.coordinates.clustering.kmeans.KmeansClustering[15] INFO The number of cluster centers was not specified, using min(sqrt(N), 5000)=27 as n_clusters.\n" - ] - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "12eac87dfa7f4d6daf4bb6a0b1c53d8d", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "initialize kmeans++ centers: 0%| | 0/27 [00:00" - ] - }, - "metadata": { - "needs_background": "light" - } - } - ], - "source": [ - "plots.plot_free_energy(np.vstack(Y)[:, 0], np.vstack(Y)[:, 1], nbins=25)\n", - "plt.scatter(cc_x, cc_y, marker = 'x', s=50,color = 'black')\n", - "plt.xlabel('TIC 1')\n", - "plt.ylabel('TIC 2')" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "45SEntpXhuir" - }, - "source": [ - "If you have time, try other clustering methods (regular space, regular time) and visualize the results. Which method do you prefer?" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "JlpIt3iIhuir" - }, - "source": [ - "# Markov State Model construction\n", - "#### Theory: \n", - "- [Implied timescales](http://docs.markovmodel.org/lecture_implied_timescales.html)\n", - "- Pande, V. S., K. A. Beauchamp, and G. R. Bowman. [Everything you wanted to know about Markov State Models but were afraid to ask](http://www.sciencedirect.com/science/article/pii/S1046202310001568?via%3Dihub)\n", - " Methods 52.1 (2010): 99-105\n", - "Let's estimate MSMs at different lagtimes. Now we would like the lagtime to converge within the statistical uncertainty.\n", - "\n", - "This function estimates an MSM for each of the lag-times we have provided: $\\tau=n_{lag}\\cdot t_{\\text{stride}}$ Where $n_{lag}$ is one of the integers given in the lags list, and $t_{\\text{stride}}$ is the trajectory time-stride. For the example data given here the last parameter is $80\\,\\mathrm{ps}$. The implied-timescales are then computed as:\n", - " $$ t_i = \\frac{-\\tau}{\\ln | \\lambda_i(\\tau) |}.$$\n", - "The its object contains these implied time-scales which can be plotted using one of PyEMMAs built in function for quick inspection - here you can also defined the time-units to ensure the axes express physical time." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "id": "lkKSqkhhhuis" - }, - "outputs": [], - "source": [ - "from pyemma import msm" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 34, - "referenced_widgets": [ - "5be9ed46b5b145a98acd679fbfdde0cb", - "f3cf80888f5d467eb8a62e5b3a1888f9", - "12f1b32c5a4b4f3ba5c91b4acf5b5945", - "588bd025bdc6424091be44525b5aa7ca", - "599ff3abeb9d462ab9dd22a0f229d544", - "5b80d33d778e4d108cae4641aa658649", - "dfd1f458ee0b46df846312fa5f401113", - "e0e9a1f02cd9484a9cab2133fce6bcb4", - "6bd4cf257c864785a356fe27436a4aa9", - "723f1685c13049deb8b9f2edd437b4ee", - "e88b639b312149a6872cc722e998ae3f" - ] - }, - "id": "Tw3hEH7Yhuis", - "outputId": "cb572d10-ca68-42ed-8932-5971a5948b90" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "8\n" - ] - }, - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5be9ed46b5b145a98acd679fbfdde0cb", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "estimating MaximumLikelihoodMSM: 0%| | 0/5 [00:00" - ] - }, - "metadata": { - "needs_background": "light" - } - } - ], - "source": [ - "plots.plot_implied_timescales(its,dt=dt, units='$ps$')\n", - "plt.ylim(1,5000);" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VHbCtEtghuis" - }, - "source": [ - "Use plots.plot_implied_timescales to visualize." - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "WO2IxX1g_dZZ" + }, + "source": [ + "# Cluster ligand binding modes with an MSM\n", + "\n", + "In simulations, ligand binding modes often interconvert somewhat slowly. In this notebook we work through an example which takes stored simulation data and builds an MSM to cluster the ligand binding modes which were sampled. This is based on a research problem we encountered, but takes a slightly simplified form.\n", + "\n", + "Please see [the PyEMMA LiveCoMS article](https://doi.org/10.33011/livecoms.1.1.5965) and associated GitHub repository [https://github.com/markovmodel/pyemma_tutorials](https://github.com/markovmodel/pyemma_tutorials) for additional background and tutorials.\n", + "\n", + "## Preparation for using Google Colab (SKIP IF RUNNING LOCALLY)\n", + "\n", + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MobleyLab/drug-computing/blob/master/uci-pharmsci/lectures/cluster_and_visualize/PyEMMA_Ligand_binding_mode_clustering.ipynb)\n", + "\n", + "If you are running this on Google Colab, you need to take a couple additional steps of preparation:\n", + "1) Mount your Google Drive to this notebook:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Cdy16DIV_dZb", + "outputId": "a06d8cc1-4a44-4464-a167-978add14efdf" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mounted at /content/drive\n" + ] + } + ], + "source": [ + "# Run cell if using collab\n", + "\n", + "# Mount google drive to Colab Notebooks to access files\n", + "from google.colab import drive\n", + "drive.mount('/content/drive', force_remount = True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0NqzAb_Y_dZc" + }, + "source": [ + "2) **If you are running this on Google Colab, install dependencies**:\n", + "\n", + "(You will need to run this cell twice if you encounter an error the first time.)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "DNUPudjHhxfb", + "outputId": "9f8b2cfc-d30c-4155-861e-0e9110fc9bdd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "✨🍰✨ Everything looks OK!\n", + "✨🍰✨ Everything looks OK!\n", + "CPU times: user 27.4 ms, sys: 11.3 ms, total: 38.7 ms\n", + "Wall time: 1.33 s\n" + ] + } + ], + "source": [ + "%%time \n", + "### run this block if running notebook on google collab\n", + "\n", + "# Import condacolab python library and install condacolab (~5-10 minutes). \n", + "\n", + "# Make sure to rerun this cell after it crashes\n", + "!pip install --target=$nb_path -q condacolab\n", + "import condacolab\n", + "condacolab.install()\n", + "\n", + "# Check condacolab to ensure that it works\n", + "condacolab.check()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xDaN08l1AG-3", + "outputId": "7ae530b7-2352-4629-ec86-0aa429bceacb" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: 'conda-forge' already in 'channels' list, moving to the top\n", + "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", + "Solving environment: | \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n", + "\n", + "# All requested packages already installed.\n", + "\n" + ] + } + ], + "source": [ + "# Install packages - MUST be in a separate cell from the above because of library issues with how condacolab works.\n", + "!conda config --add channels conda-forge\n", + "!conda install pyemma " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_lekq2ti_dZd" + }, + "source": [ + "3) **Navigate to where you have the content for this lecture on Drive**; edit this path if needed" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qvSB7KUs_dZe", + "outputId": "0822aa60-26fd-4abc-9841-2fa93c3263e2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/content/drive/MyDrive/drug-computing/uci-pharmsci/lectures/cluster_and_visualize\n", + "4XH_1-top.pdb lig-first.pdb test.h5\n" + ] + } + ], + "source": [ + "# Move into directory so that files can be accessed\n", + "%cd /content/drive/MyDrive/drug-computing/uci-pharmsci/lectures/cluster_and_visualize\n", + "# list files/folders in directory\n", + "%ls" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "eC55dSQD_dZe" + }, + "source": [ + "## Prep for running locally\n", + "\n", + "If running locally, you need a standard scientific Python installation with `openmm` installed (as is standard for this class), and then you additionally need to `conda install -c conda-forge pyemma`. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0Gt4MUdJhuiY" + }, + "source": [ + "## Featurization\n", + "\n", + "Our first step is \"featurization\" -- to try and identify which features or coordinates in our simulations can separate the slow transitions between ligand binding modes.\n", + "\n", + "Import the [pyEMMA coordinates](http://emma-project.org/latest/api/index_coor.html) package: \n", + "> This package contains functions and classes for reading and writing trajectory files, extracting order parameters from them (such as distances or angles), as well as various methods for dimensionality reduction and clustering." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "i-eVcBVShuif" + }, + "outputs": [], + "source": [ + "import pyemma.coordinates as coor\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "UnU19tUthuih" + }, + "outputs": [], + "source": [ + "#Define our input MD files\n", + "md_nc = '4XH_1-md_subsamp.nc'\n", + "topfile = '4XH_1-top.pdb'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "m5etBseChuii" + }, + "source": [ + "From the coordinates module, use the [featurizer](http://emma-project.org/latest/api/generated/pyemma.coordinates.featurizer.html#pyemma.coordinates.featurizer) to select the ligand heavy atoms." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "TT7NXb4Xhuii", + "outputId": "e625faee-7de0-4dad-8211-0a93f9187faa" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "qo3nNwB1huis" - }, - "source": [ - "It has been shown that the implied timescales (y-axis) should be independent of the lag time (x-axis) [3]. For short lag times this is not the case, but after about 40 picoseconds, they are fairly constant. Some of the faster timescales (lower value on y-axis) either still increase as a function of lag-time or fall below the grey shaded area in the plot. This is often the case and is due to a combination of numerical issues and an imperfect discretization - this mean that the processes associated with these time-scales will be unreliable. The grey area defines the lag-time of the model, and therefore also the time-resolution of what can be resolved. If the processess fall within this area, they are generally not resolved.\n", - "\n", - "Basically - we have to make a compromise, we want to have the model which the highest time-resolution which still has implied time-scales which are lag-time independent.\n", - "\n", - "Sometimes it can be useful to quantify the uncertainty of these time-scales, to get a better idea of any changes in the implied-timescales carry any statistical significance. " - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning: importing 'simtk.openmm' is deprecated. Import 'openmm' instead.\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "id": "heQC3tSqhuit" - }, - "source": [ - "Estimate uncertainties using Bayesian sampling. Note that these errors tend to be a bit underestimated, but we can still see that the timescales are flat within the error for lag times above 40 picoseconds." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Selected 11 heavy atoms\n" + ] + } + ], + "source": [ + "feat = coor.featurizer(topfile)\n", + "lig_atoms = feat.select(\"resname LIG and not type H\")\n", + "feat.add_selection(lig_atoms)\n", + "print('Selected %s heavy atoms' % len(lig_atoms))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ljSgmabNhuij" + }, + "source": [ + "Load the coordinates from disc. Often, coordinates will not fit into memory, so we'll just create a loader by specifying the files as follows using the [source module](http://emma-project.org/latest/api/generated/pyemma.coordinates.source.html):" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "NDlk7Gnyhuik", + "outputId": "da5a0219-371f-4aaf-a54f-36499371ab63" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of trajectories = 1\n", + "trajectory length = 750\n", + "number of dimension = 33\n" + ] + } + ], + "source": [ + "inp = coor.source(md_nc, feat)\n", + "print('number of trajectories = ',inp.number_of_trajectories())\n", + "print('trajectory length = ',inp.trajectory_length(0))\n", + "print('number of dimension = ',inp.dimension())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZYX5dMsuhuil" + }, + "source": [ + "# Dimensionality Reduction\n", + "## Time-lagged independent component analysis (TICA)\n", + "[TICA theory](http://docs.markovmodel.org/lecture_tica.html)\n", + "\n", + "Run TICA for a series of lag times and select a suitable lag time for further analysis. In order to do so, have a look at the implied timescales generated by TICA. The [TICA object](http://emma-project.org/latest/api/generated/pyemma.coordinates.tica.html#pyemma.coordinates.tica) has an attribute timescales.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "1MPLRhLqhuil" + }, + "outputs": [], + "source": [ + "#Frames in MD simulation were taken every 10ps \n", + "#Give a range of different lag times to try out\n", + "dt = 10\n", + "lag_list = np.arange(1, 20,5)\n", + "nlags = lag_list.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hqsQ7NiRhuim", + "outputId": "08958f4c-7fc2-4cfb-c073-aaa9b1ed09d9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "type of Y: \n", + "length of Y: 1\n", + "shape of first element: (750, 33)\n" + ] + } + ], + "source": [ + "#Load the trajectory files\n", + "Y = inp.get_output()\n", + "print(\"type of Y:\", type(Y))\n", + "print(\"length of Y:\", len(Y))\n", + "print(\"shape of first element:\", Y[0].shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In 2023 to get the below to work, I had to manually modify my colvartools.py (see [this thread](https://github.com/markovmodel/PyEMMA/issues/1589) to get this to work correctly because the tica function is depecated and uses part of numpy which has been removed/hidden; specifically I changed `numpy.bool` to `numpy.bool_`; this was in my conda site-packages directory in \"envs/drugcomp23/lib/python3.9/site-packages/pyemma/_ext/variational/estimators/covar_c/covartools.py\" " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "-zKfT3Mthuin" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/dmobley/miniconda3-intel/envs/drugcomp23/lib/python3.9/site-packages/decorator.py:232: PyEMMA_DeprecationWarning: Call to deprecated function \"tica\". Called from /Users/dmobley/miniconda3-intel/envs/drugcomp23/lib/python3.9/site-packages/decorator.py line 232. Use deeptime.decomposition.TICA instead.\n", + " return caller(func, *(extras + args), **kw)\n", + "/Users/dmobley/miniconda3-intel/envs/drugcomp23/lib/python3.9/site-packages/decorator.py:232: PyEMMA_DeprecationWarning: Call to deprecated function \"tica\". Called from /Users/dmobley/miniconda3-intel/envs/drugcomp23/lib/python3.9/site-packages/decorator.py line 232. Use deeptime.decomposition.TICA instead.\n", + " return caller(func, *(extras + args), **kw)\n", + "/Users/dmobley/miniconda3-intel/envs/drugcomp23/lib/python3.9/site-packages/decorator.py:232: PyEMMA_DeprecationWarning: Call to deprecated function \"tica\". Called from /Users/dmobley/miniconda3-intel/envs/drugcomp23/lib/python3.9/site-packages/decorator.py line 232. Use deeptime.decomposition.TICA instead.\n", + " return caller(func, *(extras + args), **kw)\n", + "/Users/dmobley/miniconda3-intel/envs/drugcomp23/lib/python3.9/site-packages/decorator.py:232: PyEMMA_DeprecationWarning: Call to deprecated function \"tica\". Called from /Users/dmobley/miniconda3-intel/envs/drugcomp23/lib/python3.9/site-packages/decorator.py line 232. Use deeptime.decomposition.TICA instead.\n", + " return caller(func, *(extras + args), **kw)\n" + ] + } + ], + "source": [ + "#Try out different lag times fr \n", + "lag_times = []\n", + "for lag in lag_list:\n", + " data = coor.tica(np.vstack(Y),lag, kinetic_map=True, var_cutoff=0.95, reversible=True)\n", + " lag_times.append(data.timescales[:10])\n", + "lag_times = np.asarray(lag_times)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "HRY63hpxhuin" + }, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(4,) (4, 10)\n" + ] + } + ], + "source": [ + "print(lag_list.shape, lag_times.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 303 }, + "id": "SiBK7ZnHhuio", + "outputId": "84fae0da-8c86-4341-fc7c-9f7f1574507b" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 25, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 283, - "referenced_widgets": [ - "6aded41370eb449aa6d463d3614d5ec3", - "a23e29680270473a85f181af6f3fab65", - "ddaa7a9c17514568852e9574929a1f2d", - "bb084f436b7445f7bfe407eef748a396", - "20def4a4e0e347bda5abf3eff88dd374", - "4e22091f03944164a0c0f981f66387e0", - "2a98876ebcef401daccdc51ac1289b5c", - "96c456c8ccf34594b78fca8079a790e3", - "ac619431427e4fc0b74268c463ca73b6", - "4c8f73fa0fa348cda24f1394716f7155", - "eee6309694534e6a9284145b4ccd3250" - ] - }, - "id": "zaT002brhuit", - "outputId": "afb901f8-d671-4571-cb1b-cbdd3f1fb635" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6aded41370eb449aa6d463d3614d5ec3", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "estimating BayesianMSM: 0%| | 0/5 [00:00" - ] - }, - "metadata": { - "needs_background": "light" - } - } - ], - "source": [ - "its = msm.timescales_msm(dtrajs, lags=lag, nits=10, errors='bayes')\n", - "plots.plot_implied_timescales(its, dt=dt, units='$ps$')\n", - "plt.ylim(1, 5000);" + "data": { + "text/plain": [ + "Text(0, 0.5, 'Timescales [$ps$]')" ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "markdown", - "metadata": { - "id": "Gq6M99Iqhuit" - }, - "source": [ - "Now build an MSM at a good lag time:" + "data": { + "image/png": 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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Plot the different lag times\n", + "plt.plot(dt*lag_list, dt*lag_times, linewidth=2)\n", + "plt.xlabel(r\"Lag time [$ps$]\", fontsize=12)\n", + "plt.ylabel(r\"Timescales [$ps$]\", fontsize=12)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "QNuUMDuThuio", + "outputId": "b67efddd-ec50-4fe3-d6ca-124878ac6f24" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/dmobley/miniconda3-intel/envs/drugcomp23/lib/python3.9/site-packages/decorator.py:232: PyEMMA_DeprecationWarning: Call to deprecated function \"tica\". Called from /Users/dmobley/miniconda3-intel/envs/drugcomp23/lib/python3.9/site-packages/decorator.py line 232. Use deeptime.decomposition.TICA instead.\n", + " return caller(func, *(extras + args), **kw)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "TICA dimension 17\n" + ] + } + ], + "source": [ + "#Select a lag time where the lines start to level off\n", + "#Here, I'll use 80ps\n", + "lag = int(80/ dt)\n", + "\n", + "tica_obj = coor.tica(inp, lag=lag, kinetic_map=True, var_cutoff=0.95, reversible=True)\n", + "print('TICA dimension ', tica_obj.dimension())" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UGuXMp8uhuip" + }, + "source": [ + "Plot the first two TICA components. The TICA object has a get_output() method." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kbTE4XLhhuip", + "outputId": "7fb96129-28ff-4d9a-ae44-21e30c183989" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "number of trajectories = 1\n", + "number of frames = 750\n", + "number of dimensions = 17\n" + ] + } + ], + "source": [ + "Y = tica_obj.get_output() # get tica coordinates\n", + "print('number of trajectories = ', np.shape(Y)[0])\n", + "print('number of frames = ', np.shape(Y)[1])\n", + "print('number of dimensions = ',np.shape(Y)[2])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "-qtd2SMjhuiq" + }, + "source": [ + "PyEMMA has a [plotting module](http://emma-project.org/latest/api/index_plots.html#plots-plotting-tools-pyemma-plots) which we can use to plot the TICA coordinates" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "qMb4xog5huiq" + }, + "outputs": [], + "source": [ + "from pyemma import plots" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 296 }, + "id": "Algdtrp0huiq", + "outputId": "b1a77576-f3ef-4c05-f4a8-9f74799d4c77" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 26, - "metadata": { - "id": "kn9wi8kMhuit" - }, - "outputs": [], - "source": [ - "M = msm.estimate_markov_model(dtrajs, lag)" + "data": { + "text/plain": [ + "Text(0, 0.5, 'TIC 2')" ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": 27, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Yi192aoFhuit", - "outputId": "5412c273-e78d-4270-9b24-afda304c4fd9" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "array([[ 4., 0., 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0., 1., 1., 0.,\n", - " 0., 1., 2., 0., 0., 5., 0., 0., 5., 4., 2., 2., 1.,\n", - " 0.],\n", - " [ 0., 0., 0., 0., 4., 0., 1., 0., 1., 3., 1., 1., 0.,\n", - " 1., 2., 5., 0., 0., 1., 0., 0., 3., 2., 3., 2., 3.,\n", - " 0.],\n", - " [ 2., 2., 1., 0., 2., 2., 0., 0., 0., 0., 2., 0., 0.,\n", - " 0., 3., 5., 0., 0., 0., 0., 0., 4., 4., 2., 3., 2.,\n", - " 1.],\n", - " [ 0., 3., 1., 6., 2., 0., 1., 0., 2., 3., 5., 1., 0.,\n", - " 0., 1., 3., 0., 0., 0., 0., 1., 1., 0., 5., 2., 3.,\n", - " 2.],\n", - " [ 1., 0., 3., 0., 0., 0., 0., 0., 3., 1., 1., 0., 0.,\n", - " 0., 0., 0., 4., 0., 0., 0., 7., 1., 0., 2., 0., 0.,\n", - " 3.]])" - ] - }, - "metadata": {}, - "execution_count": 27 - } - ], - "source": [ - "#Count of transitions between two states\n", - "M.count_matrix_active" + "data": { + "image/png": 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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plots.plot_free_energy(np.vstack(Y)[:, 0], np.vstack(Y)[:, 1], nbins=25)\n", + "plt.xlabel('TIC 1')\n", + "plt.ylabel('TIC 2')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UxtCaBx4huiq" + }, + "source": [ + "# Clustering the data\n", + "\n", + "## K-means clustering\n", + "Use [k-means clustering](http://emma-project.org/latest/api/generated/pyemma.coordinates.clustering.KmeansClustering.html#pyemma.coordinates.clustering.KmeansClustering) and get the discrete trajectories" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 54, + "referenced_widgets": [ + "12eac87dfa7f4d6daf4bb6a0b1c53d8d", + "c39b58cabaf34f54ab7512271d50a065", + "30680783583f49dfac3fce1c7f921973", + "ec27fc56d99b4772aab7e8d6bf4d638c", + "3f135515027f4699a1c247d0a67f9157", + "aeddc71587da47e692fa91cae62e375f", + "00255cf278c549cd904abf4358b182b8", + "c15777fe05ab4dedb5bfeb3b19e15166", + "54ee9fb171a94cb5bee04716ebd801ab", + "23b1cc84e4454a3895b8ca9922be0a43", + "26d5dc84664d49fcb953f8742fcde39c", + "6929c673d0384b3a874175ca41105281", + "6033fd93fe9c461d89f121278ce8552e", + "36499e52c5be47fe888aaf1ed6f9738d", + "3eaa23007b60461e83c84ff50fd11ab0", + "114659aa609444188cee25dc014f3117", + "dd9bb43747bb4e9d86ea0fe37dae8fa8", + "c63fe38e8ea644f8a4868952015c3065", + "b4f2cc3edced4cdba7abed2afef7a06a", + "54250664e6db440088db1131eb9ff677", + "646c7a54bbe441d08376df397f590707", + "0f4f91c4c0a243919210445a7ca1fb48" + ] + }, + "id": "0ZEArxInhuiq", + "outputId": "84567164-288e-4a42-e7b5-7c950286fa8b" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "08-03-23 10:16:21 pyemma.coordinates.clustering.kmeans.KmeansClustering[39] INFO The number of cluster centers was not specified, using min(sqrt(N), 5000)=27 as n_clusters.\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/dmobley/miniconda3-intel/envs/drugcomp23/lib/python3.9/site-packages/decorator.py:232: PyEMMA_DeprecationWarning: Call to deprecated function \"cluster_kmeans\". Called from /Users/dmobley/miniconda3-intel/envs/drugcomp23/lib/python3.9/site-packages/decorator.py line 232. Use deeptime.clustering.KMeans instead.\n", + " return caller(func, *(extras + args), **kw)\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "initialize kmeans++ centers: 0%| | 0/27 [00:00:200: RuntimeWarning: invalid value encountered in cast\n" + ] + } + ], + "source": [ + "cl = coor.cluster_kmeans(data=np.vstack(Y), max_iter=100)\n", + "# for later use we save the discrete trajectories and cluster center coordinates:\n", + "dtrajs = cl.dtrajs\n", + "cc_x = cl.clustercenters[:, 0]\n", + "cc_y = cl.clustercenters[:, 1]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5fpyWGy9huir" + }, + "source": [ + "Visualize the clusters" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 296 }, + "id": "zhaLxu7Ehuir", + "outputId": "e8322ba2-e65c-47dd-e011-0978642e3a55" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "YH0GRzzIhuit" - }, - "source": [ - "## Validation - Chapman-Kolmogorov test\n", - "\n", - "Let's see if our Markov model fulfill the Chapman-Kolmogorov equation. This is a way of testing the self-consistency of the model. There will be more background this tomorrow morning.\n", - "\n", - "The MSM instance has a method to make the testing very easy:" + "data": { + "text/plain": [ + "Text(0, 0.5, 'TIC 2')" ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "code", - "execution_count": 28, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 17, - "referenced_widgets": [ - "852489bc69ef4407814ff373daaa02b8", - "69de14f48d5645f3b69a2cc7169d0253", - "a11ebaef400c4352ba5f52e8b6b566fa", - "537c3a08e8be41dabf2edb7f5349285a", - "2136ba96fb134dc580abb127dbe4bfe9", - "07fe0fc5805e47b9b1f5b3a95f49adcf", - "274cf6962454417ba109943ca19776d7", - "efc0aa560a074388a8de4f100f72c153", - "3b06178a3e51449b89c5e54dd4937492", - "ae09ec54aaec4eddb23c924f65a0c2bb", - "cc84a5b2e82a48cf9ca7971b008a70e0" - ] - }, - "id": "my4ZkZochuiu", - "outputId": "f28fc740-bea9-483d-d9cc-15a80b8c152d" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "852489bc69ef4407814ff373daaa02b8", - "version_minor": 0, - "version_major": 2 - }, - "text/plain": [ - "estimating MaximumLikelihoodMSM: 0%| | 0/9 [00:00" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plots.plot_free_energy(np.vstack(Y)[:, 0], np.vstack(Y)[:, 1], nbins=25)\n", + "plt.scatter(cc_x, cc_y, marker = 'x', s=50,color = 'black')\n", + "plt.xlabel('TIC 1')\n", + "plt.ylabel('TIC 2')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "45SEntpXhuir" + }, + "source": [ + "If you have time, try other clustering methods (regular space, regular time) and visualize the results. Which method do you prefer?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JlpIt3iIhuir" + }, + "source": [ + "# Markov State Model construction\n", + "#### Theory: \n", + "- [Implied timescales](http://docs.markovmodel.org/lecture_implied_timescales.html)\n", + "- Pande, V. S., K. A. Beauchamp, and G. R. Bowman. [Everything you wanted to know about Markov State Models but were afraid to ask](http://www.sciencedirect.com/science/article/pii/S1046202310001568?via%3Dihub)\n", + " Methods 52.1 (2010): 99-105\n", + "Let's estimate MSMs at different lagtimes. Now we would like the lagtime to converge within the statistical uncertainty.\n", + "\n", + "This function estimates an MSM for each of the lag-times we have provided: $\\tau=n_{lag}\\cdot t_{\\text{stride}}$ Where $n_{lag}$ is one of the integers given in the lags list, and $t_{\\text{stride}}$ is the trajectory time-stride. For the example data given here the last parameter is $80\\,\\mathrm{ps}$. The implied-timescales are then computed as:\n", + " $$ t_i = \\frac{-\\tau}{\\ln | \\lambda_i(\\tau) |}.$$\n", + "The its object contains these implied time-scales which can be plotted using one of PyEMMAs built in function for quick inspection - here you can also defined the time-units to ensure the axes express physical time." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "id": "lkKSqkhhhuis" + }, + "outputs": [], + "source": [ + "from pyemma import msm" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34, + "referenced_widgets": [ + "5be9ed46b5b145a98acd679fbfdde0cb", + "f3cf80888f5d467eb8a62e5b3a1888f9", + "12f1b32c5a4b4f3ba5c91b4acf5b5945", + "588bd025bdc6424091be44525b5aa7ca", + "599ff3abeb9d462ab9dd22a0f229d544", + "5b80d33d778e4d108cae4641aa658649", + "dfd1f458ee0b46df846312fa5f401113", + "e0e9a1f02cd9484a9cab2133fce6bcb4", + "6bd4cf257c864785a356fe27436a4aa9", + "723f1685c13049deb8b9f2edd437b4ee", + "e88b639b312149a6872cc722e998ae3f" + ] + }, + "id": "Tw3hEH7Yhuis", + "outputId": "cb572d10-ca68-42ed-8932-5971a5948b90" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "8\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "estimating MaximumLikelihoodMSM: 0%| | 0/5 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plots.plot_implied_timescales(its,dt=dt, units='$ps$')\n", + "plt.ylim(1,5000);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VHbCtEtghuis" + }, + "source": [ + "Use plots.plot_implied_timescales to visualize." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qo3nNwB1huis" + }, + "source": [ + "It has been shown that the implied timescales (y-axis) should be independent of the lag time (x-axis) [3]. For short lag times this is not the case, but after about 40 picoseconds, they are fairly constant. Some of the faster timescales (lower value on y-axis) either still increase as a function of lag-time or fall below the grey shaded area in the plot. This is often the case and is due to a combination of numerical issues and an imperfect discretization - this mean that the processes associated with these time-scales will be unreliable. The grey area defines the lag-time of the model, and therefore also the time-resolution of what can be resolved. If the processess fall within this area, they are generally not resolved.\n", + "\n", + "Basically - we have to make a compromise, we want to have the model which the highest time-resolution which still has implied time-scales which are lag-time independent.\n", + "\n", + "Sometimes it can be useful to quantify the uncertainty of these time-scales, to get a better idea of any changes in the implied-timescales carry any statistical significance. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "heQC3tSqhuit" + }, + "source": [ + "Estimate uncertainties using Bayesian sampling. Note that these errors tend to be a bit underestimated, but we can still see that the timescales are flat within the error for lag times above 40 picoseconds." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 283, + "referenced_widgets": [ + "6aded41370eb449aa6d463d3614d5ec3", + "a23e29680270473a85f181af6f3fab65", + "ddaa7a9c17514568852e9574929a1f2d", + "bb084f436b7445f7bfe407eef748a396", + "20def4a4e0e347bda5abf3eff88dd374", + "4e22091f03944164a0c0f981f66387e0", + "2a98876ebcef401daccdc51ac1289b5c", + "96c456c8ccf34594b78fca8079a790e3", + "ac619431427e4fc0b74268c463ca73b6", + "4c8f73fa0fa348cda24f1394716f7155", + "eee6309694534e6a9284145b4ccd3250" + ] + }, + "id": "zaT002brhuit", + "outputId": "afb901f8-d671-4571-cb1b-cbdd3f1fb635" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "estimating BayesianMSM: 0%| | 0/5 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "its = msm.timescales_msm(dtrajs, lags=lag, nits=10, errors='bayes')\n", + "plots.plot_implied_timescales(its, dt=dt, units='$ps$')\n", + "plt.ylim(1, 5000);" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Gq6M99Iqhuit" + }, + "source": [ + "Now build an MSM at a good lag time:" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "id": "kn9wi8kMhuit" + }, + "outputs": [], + "source": [ + "M = msm.estimate_markov_model(dtrajs, lag)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Yi192aoFhuit", + "outputId": "5412c273-e78d-4270-9b24-afda304c4fd9" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 2., 0., 0., 6., 5., 0., 0., 0., 1., 1., 0., 0., 0.,\n", + " 0., 2., 0., 0., 7., 0., 0., 0., 0., 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This is a way of testing the self-consistency of the model. There will be more background this tomorrow morning.\n", + "\n", + "The MSM instance has a method to make the testing very easy:" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 17, + "referenced_widgets": [ + "852489bc69ef4407814ff373daaa02b8", + "69de14f48d5645f3b69a2cc7169d0253", + "a11ebaef400c4352ba5f52e8b6b566fa", + "537c3a08e8be41dabf2edb7f5349285a", + "2136ba96fb134dc580abb127dbe4bfe9", + "07fe0fc5805e47b9b1f5b3a95f49adcf", + "274cf6962454417ba109943ca19776d7", + "efc0aa560a074388a8de4f100f72c153", + "3b06178a3e51449b89c5e54dd4937492", + "ae09ec54aaec4eddb23c924f65a0c2bb", + "cc84a5b2e82a48cf9ca7971b008a70e0" + ] + }, + "id": "my4ZkZochuiu", + "outputId": "f28fc740-bea9-483d-d9cc-15a80b8c152d" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "estimating MaximumLikelihoodMSM: 0%| | 0/9 [00:00" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plots.plot_cktest(ckt,dt=dt,units=\"ps\");" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wQJfUAIZhuiu" + }, + "source": [ + "What is shown here is the predicted transition probabilities from the Markov model (blue) and corresponding ones estimated directly from the data you used to estimate the Markov model (that is, training a Markov model with a larger lag-time). More formally what we check is whether the following relation is approximately fulfilled:\n", + "$$ \\underbrace{T^k(\\tau)}_{\\text{Markov model prediction}} \\approx \\underbrace{T(k\\tau)}_{\\text{estimation from data}} $$\n", + "where $k$ is some positive integer, and $T(\\cdot)$ is a transition probability matrix for a given lag-time. Basically, we want to ensure these lines are as close as possible to each other. More depth on this topic is given tomorrow morning.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vAHJm9i1huiu" + }, + "source": [ + "## Spectral analysis\n", + "\n", + "\n", + "Let us have a closer look at the timescales that were already seen in the its plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 296 }, + "id": "lZru2Cx3huiu", + "outputId": "7c45a09c-7ec4-46d2-e28c-672a80592678" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 29, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 624 - }, - "id": "4Y_e1UWPhuiu", - "outputId": "378e9994-3928-405c-93e9-81395556af2e" - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - } - } - ], - "source": [ - "plots.plot_cktest(ckt,dt=dt,units=\"ps\");" + "data": { + "text/plain": [ + "(-0.5, 10.5)" ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "markdown", - "metadata": { - "id": "wQJfUAIZhuiu" - }, - "source": [ - "What is shown here is the predicted transition probabilities from the Markov model (blue) and corresponding ones estimated directly from the data you used to estimate the Markov model (that is, training a Markov model with a larger lag-time). More formally what we check is whether the following relation is approximately fulfilled:\n", - "$$ \\underbrace{T^k(\\tau)}_{\\text{Markov model prediction}} \\approx \\underbrace{T(k\\tau)}_{\\text{estimation from data}} $$\n", - "where $k$ is some positive integer, and $T(\\cdot)$ is a transition probability matrix for a given lag-time. Basically, we want to ensure these lines are as close as possible to each other. More depth on this topic is given tomorrow morning.\n" + "data": { + "image/png": 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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(dt*M.timescales(),linewidth=0,marker='o')\n", + "plt.xlabel('index');plt.ylabel(r'timescale (1 ps)'); plt.xlim(-0.5,10.5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "L6_ZJ7rfhuiv" + }, + "source": [ + "## PCCA\n", + "\n", + "#### Theory\n", + "- [PCCA: perron-cluster cluster analysis](http://docs.markovmodel.org/lecture_pcca.html)\n", + "\n", + "Select a suitable number of metastable states and run PCCA." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "id": "xEszrNkthuiv" + }, + "outputs": [], + "source": [ + "#Perform PCCA analysis and store data\n", + "n_clusters = 3\n", + "M.pcca(n_clusters)\n", + "pcca_sets = M.metastable_sets\n", + "centers = cl.clustercenters[M.active_set]\n", + "cluster_labels = M.metastable_assignments" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tG4zNrLMhuiv" + }, + "source": [ + "Visualize the PCCA assignments in the free energy landscape defined by the first two TICA coordinates.\n", + "Hints: A Markov model object has an attribute M.metastable_sets. Also, you need to find out how to access the clustercenters defined in the previous step." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 368 }, + "id": "Xs50aH1Mhuiv", + "outputId": "d77f784a-88ef-487f-ef2e-cd75963be54d" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "id": "vAHJm9i1huiu" - }, - "source": [ - "## Spectral analysis\n", - "\n", - "\n", - "Let us have a closer look at the timescales that were already seen in the its plot:" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/7z/88tvgj2941bclbbw1xhn0t8r0000gn/T/ipykernel_7611/2701532399.py:9: UserWarning: *c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", + " ax.scatter(cl.clustercenters[pcca_sets[i],0], cl.clustercenters[pcca_sets[i],1], marker='X', c=color,s=100, edgecolors='k')\n" + ] }, { - "cell_type": "code", - "execution_count": 30, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 296 - }, - "id": "lZru2Cx3huiu", - "outputId": "7c45a09c-7ec4-46d2-e28c-672a80592678" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "(-0.5, 10.5)" - ] - }, - "metadata": {}, - "execution_count": 30 - }, - { - "output_type": "display_data", - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - } - } - ], - "source": [ - "plt.plot(dt*M.timescales(),linewidth=0,marker='o')\n", - "plt.xlabel('index');plt.ylabel(r'timescale (1 ps)'); plt.xlim(-0.5,10.5)" + "data": { + "text/plain": [ + "Text(0, 0.5, 'TIC 2')" ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" }, { - "cell_type": "markdown", - "metadata": { - "id": "L6_ZJ7rfhuiv" - }, - "source": [ - "## PCCA\n", - "\n", - "#### Theory\n", - "- [PCCA: perron-cluster cluster analysis](http://docs.markovmodel.org/lecture_pcca.html)\n", - "\n", - "Select a suitable number of metastable states and run PCCA." + "data": { + "image/png": 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\n", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Plot PCCA data\n", + "_, ax = plots.plot_free_energy(np.vstack(Y)[:, 0], np.vstack(Y)[:, 1], nbins=25)\n", + "pcca_sets = M.metastable_sets\n", + "\n", + "cm = plt.get_cmap('gist_rainbow')\n", + "colors = [cm(1.*i/n_clusters) for i in range(n_clusters)]\n", + "\n", + "for i, color in enumerate(colors[:n_clusters]):\n", + " ax.scatter(cl.clustercenters[pcca_sets[i],0], cl.clustercenters[pcca_sets[i],1], marker='X', c=color,s=100, edgecolors='k')\n", + "ax.set_xlabel('TIC 1')\n", + "ax.set_ylabel('TIC 2')\n", + "#plt.savefig('4XH_1_c0-tiCA-PCCA.png', dpi=300,bbox_inches='tight')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Mf0TO2j1huiv" + }, + "source": [ + "### Extracting the PCCA cluster frames" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HMxLEF2ahuiv", + "outputId": "27c5b512-260c-4a86-d132-18588bb17c4a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Storing 100 PCCA samples each to: \n", + "\t4XH_1_c0-pcca0_samples.dcd\n", + "\t4XH_1_c0-pcca1_samples.dcd\n", + "\t4XH_1_c0-pcca2_samples.dcd\n" + ] + } + ], + "source": [ + "#Extract the assigned trajectory frames from PCCA\n", + "n_samples = 100\n", + "pcca_dist = M.metastable_distributions\n", + "pcca_samples = M.sample_by_distributions(pcca_dist, n_samples)\n", + "\n", + "outfiles = ['4XH_1_c0-pcca%s_samples.dcd' % (N) for N in range(n_clusters)]\n", + "pcca_outfiles = coor.save_trajs(inp, pcca_samples, outfiles=outfiles)\n", + "print('Storing %s PCCA samples each to: \\n\\t%s' % (n_samples, '\\n\\t'.join(pcca_outfiles)))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VCzV_2YEhuiw" + }, + "source": [ + "## Assigning frames to PCCA clusters" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "id": "86jeHg8Shuiw" + }, + "outputs": [], + "source": [ + "def assignTrajFramesToPCCACenters(tica_coord, pcca_centers, pcca_labels):\n", + " \"\"\"\n", + " Given the TICA coordinates data for a trajectory, assign data to\n", + " the nearest clsuter centers as defined from PCCA.\n", + " Parameters:\n", + " -----------\n", + " tica_coord : numpy array, tica coordinates for the associated trajectory.\n", + " pcca_centers : numpy array, contains the coordinates of the cluster centers\n", + " from the active set states obtained from PCCA.\n", + " pcca_labels : list, cluster assignment of the centers from PCCA.\n", + " Returns:\n", + " -------\n", + " traj_cluster_labels : list of arrays, cluster assignment for each\n", + " trajectory frame where every array is for each trajectory\n", + " \"\"\"\n", + " assignments = coor.assign_to_centers(tica_coord, pcca_centers)\n", + " traj_cluster_labels = []\n", + " for a in assignments:\n", + " traj_cluster_labels.append(pcca_labels[a])\n", + " return traj_cluster_labels" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "id": "ujTFZGzUhuix" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/dmobley/miniconda3-intel/envs/drugcomp23/lib/python3.9/site-packages/decorator.py:232: PyEMMA_DeprecationWarning: Call to deprecated function \"assign_to_centers\". Called from /Users/dmobley/miniconda3-intel/envs/drugcomp23/lib/python3.9/site-packages/decorator.py line 232. Use deeptime.clustering.ClusterModel instead.\n", + " return caller(func, *(extras + args), **kw)\n", + "<__array_function__ internals>:200: RuntimeWarning: invalid value encountered in cast\n" + ] + } + ], + "source": [ + "traj_cluster_labels = assignTrajFramesToPCCACenters(Y, centers, cluster_labels)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "q09GFJFZhuix", + "outputId": "8cff0869-e6b1-4f3c-d3d1-f54b2edfe913" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1,\n", + " 1, 1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 0, 1, 1, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0,\n", + " 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", + " 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", + " 1, 0])]" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "traj_cluster_labels" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "N8fKGxdChuix" + }, + "source": [ + "# Plotting ligand binding mode sampling with RMSD" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "id": "gyPhjsAqhuix" + }, + "outputs": [], + "source": [ + "def plotPoseSampling(distances,\n", + " cluster_labels,\n", + " title=\"Binding Mode Frequency\",\n", + " acc_it=[], n_clusters=4,\n", + " cmap='gist_rainbow'):\n", + " \"\"\"Function that plots the RMSD of the ligand atoms with each frame/data point colored\n", + " according to the cluster assignment.\n", + " \n", + " Parameters:\n", + " - distances : np.array of the desired RMSD to plot\n", + " - cluster_labels : list of the cluster assigments of the trajectory frames\n", + " \"\"\"\n", + " \n", + " #Define plot settings\n", + " N = len(cluster_labels)\n", + " plt.figure(figsize=(12, 8), dpi=300,tight_layout=True)\n", + " f, ax = plt.subplots(2)\n", + " \n", + " #Get the color mappings for the number of clusters\n", + " cm = plt.get_cmap(cmap)\n", + " colors = [cm(1.*i/n_clusters) for i in range(n_clusters)]\n", + "\n", + " #Compute and plot the % frequency in bar plot\n", + " labeled_frames = {n:[] for n in range(n_clusters)}\n", + " for i, label in enumerate(cluster_labels):\n", + " labeled_frames[label].append(i)\n", + " freq = { key:len(val)/N*100 for key,val in labeled_frames.items() }\n", + " rect = ax[0].bar(range(len(freq.keys())),freq.values(), align='center', color=colors)\n", + " ax[0].set_xlabel(\"Ligand Binding Mode\")\n", + " ax[0].set_ylabel(\"% Frequency\")\n", + " ax[0].set_xticks([])\n", + " \n", + " #Generate a list of colors corresponding to the cluster assignment \n", + " colr_list = []\n", + " for x in cluster_labels:\n", + " colr_list.append(colors[x])\n", + " \n", + " #Convert frame numbers to time\n", + " time = [0.2*t for t in range(N)]\n", + " \n", + " #Lineplot of RMSD relative to initial frame\n", + " #Convert distances from nm to angstrom (distances*10.0)\n", + " ax[1].plot(time, 10.0*distances, 'k', linewidth=0.25)\n", + " #Colored Scatterplot points to state of minimum RMSD\n", + " ax[1].scatter(time, 10.0*distances,c=colr_list,clip_on=False,s=25, edgecolors=None)\n", + " \n", + " #With BLUES, plot the vertical lines to indicate the accepted moves\n", + " if acc_it:\n", + " for it in acc_it:\n", + " ax[1].axvline(x=it, color='k', linestyle='--')\n", + " \n", + " #Label axis \n", + " ax[1].set_xlabel(\"Time (ns)\")\n", + " ax[1].set_ylabel(r\"RMSD $\\AA$\")\n", + " ax[1].xaxis.grid(False)\n", + " ax[1].yaxis.grid(False, which='major')\n", + " plt.autoscale(enable=True, axis='both', tight=True)\n", + " ax[0].set_title(title+\" N=%s\" %N)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "id": "QN9vduYrhuix" + }, + "outputs": [], + "source": [ + "def rmsdNP(traj, ref, idx):\n", + " \"\"\"Compute the RMSD wihtout trajectory alignment\n", + " \n", + " Parameters:\n", + " - traj : mdtraj.Trajectory \n", + " - ref : mdtraj.Trajectory \n", + " reference trajectory to compare `traj` object\n", + " - idx : list of atom inidices\n", + " \"\"\"\n", + " return np.sqrt(3*np.sum(np.square(traj.xyz[:,idx,:] - ref.xyz[:,idx,:]),\n", + " axis=(1, 2))/idx.shape[0])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "id": "1-9LhP4Bhuiy" + }, + "outputs": [], + "source": [ + "import mdtraj as md" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Or-O5gbFhuiy", + "outputId": "2172bdea-37a5-4f0f-e903-48d18115808f" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "#Create an mdtraj.Trajectory object to extract frames from\n", + "pdb = md.load(topfile)\n", + "ctraj = md.load(md_nc,top=pdb.top)\n", + "print(ctraj)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "id": "sm_T4o4Jhuiy" + }, + "outputs": [], + "source": [ + "# Compute the RMSD of the ligand heavy atoms relative to the starting frame\n", + "md_dist = rmsdNP(ctraj, ctraj[0],ctraj.top.select('resname LIG and not type H'))" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 312 }, + "id": "haSe7o5shuiy", + "outputId": "80a8d17b-c58d-4551-b2b3-947a67f90ff7" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 31, - "metadata": { - "id": "xEszrNkthuiv" - }, - "outputs": [], - "source": [ - "#Perform PCCA analysis and store data\n", - "n_clusters = 3\n", - "M.pcca(n_clusters)\n", - "pcca_sets = M.metastable_sets\n", - "centers = cl.clustercenters[M.active_set]\n", - "cluster_labels = M.metastable_assignments" + "data": { + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "cell_type": "markdown", - "metadata": { - "id": "tG4zNrLMhuiv" - }, - "source": [ - "Visualize the PCCA assignments in the free energy landscape defined by the first two TICA coordinates.\n", - "Hints: A Markov model object has an attribute M.metastable_sets. Also, you need to find out how to access the clustercenters defined in the previous step." + "data": { + "image/png": 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\n", + "text/plain": [ + "
" ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 368 - }, - "id": "Xs50aH1Mhuiv", - "outputId": "d77f784a-88ef-487f-ef2e-cd75963be54d" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", - "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n", - "*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*. Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "Text(0, 0.5, 'TIC 2')" - ] - }, - "metadata": {}, - "execution_count": 32 - }, - { - "output_type": "display_data", - "data": { - "image/png": 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Shaw DE, Maragakis P, Lindorff-Larsen K, Piana S, Dror RO, Eastwood MP, Bank JA, Jumper JM, Salmon JK, Shan Y, Wriggers W: Atomic-level characterization of the structural dynamics of proteins. *Science* **330**:341-346 (2010). doi: 10.1126/science.1187409.\n", + "2. Molgedey, L. and H. G. Schuster, Phys. Rev. Lett. 72, 3634 (1994).\n", + "3. Pérez-Hernández, G. and Paul, F. and Giogino, T. and de Fabritiis, G. and Noé, F. Identification of slow molecular order parameters for Markov model construction. *J. Chem. Phys.* **139**:015102 (2013)\n", + "4. Swope WC, Pitera JW and Suits F. Describing protein folding kinetics by molecular dynamics simulations: 1. Theory. *J. Phys. Chem. B* **108**:6571-6581 (2004)\n", + "5. Röblitz S. and M. Weber: Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification. Adv. Data. Anal. Classif. DOI 10.1007/s11634-013-0134-6 (2013) \n", + "6. Noé F, Doose S, Daidone I, Löllmann M, Chodera JD, Sauer M, Smith JC. Dynamical fingerprints for probing individual relaxation processes in biomolecular dynamics with simulations and kinetic experiments. *Proc. Natl. Acad. Sci. USA*, **108**: 4822-4827 (2011)\n", + "7. Metzner P, Schütte C, Vanden-Eijnden, E. Transition Path Theory for Markov Jump Processes. *Multiscale Model. Simul.* **7**. 1192--1219 (2009)\n", + "8. Noé F, Schütte C, Vanden-Eijnden E, Reich L and Weikl T. Constructing the Full Ensemble of Folding Pathways from Short Off-Equilibrium Simulations. *Proc. Natl. Acad. Sci. USA*, **106**:19011-19016 (2009)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "anaconda-cloud": {}, + "colab": { + "collapsed_sections": [], + "name": "PyEMMA_Ligand_binding_mode_clustering.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "drugcomp23", + "language": "python", + "name": "drugcomp23" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + }, + "toc": { + "nav_menu": {}, + "number_sections": true, + "sideBar": true, + "skip_h1_title": false, + "toc_cell": false, + "toc_position": {}, + "toc_section_display": "block", + "toc_window_display": false + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "00255cf278c549cd904abf4358b182b8": { + 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"@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_12f1b32c5a4b4f3ba5c91b4acf5b5945", + "IPY_MODEL_588bd025bdc6424091be44525b5aa7ca", + "IPY_MODEL_599ff3abeb9d462ab9dd22a0f229d544" ], - "source": [ - "#Extract the assigned trajectory frames from PCCA\n", - "n_samples = 100\n", - "pcca_dist = M.metastable_distributions\n", - "pcca_samples = M.sample_by_distributions(pcca_dist, n_samples)\n", - "\n", - "outfiles = ['4XH_1_c0-pcca%s_samples.dcd' % (N) for N in range(n_clusters)]\n", - "pcca_outfiles = coor.save_trajs(inp, pcca_samples, outfiles=outfiles)\n", - "print('Storing %s PCCA samples each to: \\n\\t%s' % (n_samples, '\\n\\t'.join(pcca_outfiles)))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "VCzV_2YEhuiw" - }, - "source": [ - "## Assigning frames to PCCA clusters" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": { - "id": "86jeHg8Shuiw" - }, - "outputs": [], - "source": [ - "def assignTrajFramesToPCCACenters(tica_coord, pcca_centers, pcca_labels):\n", - " \"\"\"\n", - " Given the TICA coordinates data for a trajectory, assign data to\n", - " the nearest clsuter centers as defined from PCCA.\n", - " Parameters:\n", - " -----------\n", - " tica_coord : numpy array, tica coordinates for the associated trajectory.\n", - " pcca_centers : numpy array, contains the coordinates of the cluster centers\n", - " from the active set states obtained from PCCA.\n", - " pcca_labels : list, cluster assignment of the centers from PCCA.\n", - " Returns:\n", - " -------\n", - " traj_cluster_labels : list of arrays, cluster assignment for each\n", - " trajectory frame where every array is for each trajectory\n", - " \"\"\"\n", - " assignments = coor.assign_to_centers(tica_coord, pcca_centers)\n", - " traj_cluster_labels = []\n", - " for a in assignments:\n", - " traj_cluster_labels.append(pcca_labels[a])\n", - " return traj_cluster_labels" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": { - "id": "ujTFZGzUhuix" - }, - "outputs": [], - "source": [ - "traj_cluster_labels = assignTrajFramesToPCCACenters(Y, centers, cluster_labels)" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "q09GFJFZhuix", - "outputId": "8cff0869-e6b1-4f3c-d3d1-f54b2edfe913" - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", - " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", - " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", - " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n", - " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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"grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": "100%" + } + }, + "646c7a54bbe441d08376df397f590707": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "6929c673d0384b3a874175ca41105281": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_36499e52c5be47fe888aaf1ed6f9738d", + "IPY_MODEL_3eaa23007b60461e83c84ff50fd11ab0", + "IPY_MODEL_114659aa609444188cee25dc014f3117" ], - "source": [ - "traj_cluster_labels" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "N8fKGxdChuix" - }, - "source": [ - "# Plotting ligand binding mode sampling with RMSD" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": { - "id": "gyPhjsAqhuix" - }, - "outputs": [], - "source": [ - "def plotPoseSampling(distances,\n", - " cluster_labels,\n", - " title=\"Binding Mode Frequency\",\n", - " acc_it=[], n_clusters=4,\n", - " cmap='gist_rainbow'):\n", - " \"\"\"Function that plots the RMSD of the ligand atoms with each frame/data point colored\n", - " according to the cluster assignment.\n", - " \n", - " Parameters:\n", - " - distances : np.array of the desired RMSD to plot\n", - " - cluster_labels : list of the cluster assigments of the trajectory frames\n", - " \"\"\"\n", - " \n", - " #Define plot settings\n", - " N = len(cluster_labels)\n", - " plt.figure(figsize=(12, 8), dpi=300,tight_layout=True)\n", - " f, ax = plt.subplots(2)\n", - " \n", - " #Get the color mappings for the number of clusters\n", - " cm = plt.get_cmap(cmap)\n", - " colors = [cm(1.*i/n_clusters) for i in range(n_clusters)]\n", - "\n", - " #Compute and plot the % frequency in bar plot\n", - " labeled_frames = {n:[] for n in range(n_clusters)}\n", - " for i, label in enumerate(cluster_labels):\n", - " labeled_frames[label].append(i)\n", - " freq = { key:len(val)/N*100 for key,val in labeled_frames.items() }\n", - " rect = ax[0].bar(range(len(freq.keys())),freq.values(), align='center', color=colors)\n", - " ax[0].set_xlabel(\"Ligand Binding Mode\")\n", - " ax[0].set_ylabel(\"% Frequency\")\n", - " ax[0].set_xticks([])\n", - " \n", - " #Generate a list of colors corresponding to the cluster assignment \n", - " colr_list = []\n", - " for x in cluster_labels:\n", - " colr_list.append(colors[x])\n", - " \n", - " #Convert frame numbers to time\n", - " time = [0.2*t for t in range(N)]\n", - " \n", - " #Lineplot of RMSD relative to initial frame\n", - " #Convert distances from nm to angstrom (distances*10.0)\n", - " ax[1].plot(time, 10.0*distances, 'k', linewidth=0.25)\n", - " #Colored Scatterplot points to state of minimum RMSD\n", - " ax[1].scatter(time, 10.0*distances,c=colr_list,clip_on=False,s=25, edgecolors=None)\n", - " \n", - " #With BLUES, plot the vertical lines to indicate the accepted moves\n", - " if acc_it:\n", - " for it in acc_it:\n", - " ax[1].axvline(x=it, color='k', linestyle='--')\n", - " \n", - " #Label axis \n", - " ax[1].set_xlabel(\"Time (ns)\")\n", - " ax[1].set_ylabel(r\"RMSD $\\AA$\")\n", - " ax[1].xaxis.grid(False)\n", - " ax[1].yaxis.grid(False, which='major')\n", - " plt.autoscale(enable=True, axis='both', tight=True)\n", - " ax[0].set_title(title+\" N=%s\" %N)" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": { - "id": "QN9vduYrhuix" - }, - "outputs": [], - "source": [ - "def rmsdNP(traj, ref, idx):\n", - " \"\"\"Compute the RMSD wihtout trajectory alignment\n", - " \n", - " Parameters:\n", - " - traj : mdtraj.Trajectory \n", - " - ref : mdtraj.Trajectory \n", - " reference trajectory to compare `traj` object\n", - " - idx : list of atom inidices\n", - " \"\"\"\n", - " return np.sqrt(3*np.sum(np.square(traj.xyz[:,idx,:] - ref.xyz[:,idx,:]),\n", - " axis=(1, 2))/idx.shape[0])\n" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": { - "id": "1-9LhP4Bhuiy" - }, - "outputs": [], - "source": [ - "import mdtraj as md" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "Or-O5gbFhuiy", - "outputId": "2172bdea-37a5-4f0f-e903-48d18115808f" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n" - ] - } + "layout": "IPY_MODEL_6033fd93fe9c461d89f121278ce8552e" + } + }, + "69de14f48d5645f3b69a2cc7169d0253": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": "inline-flex", + "flex": null, + "flex_flow": "row wrap", + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": "100%" + } + }, + "6aded41370eb449aa6d463d3614d5ec3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HBoxModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + "box_style": "", + "children": [ + "IPY_MODEL_ddaa7a9c17514568852e9574929a1f2d", + "IPY_MODEL_bb084f436b7445f7bfe407eef748a396", + "IPY_MODEL_20def4a4e0e347bda5abf3eff88dd374" ], - "source": [ - "#Create an mdtraj.Trajectory object to extract frames from\n", - "pdb = md.load(topfile)\n", - "ctraj = md.load(md_nc,top=pdb.top)\n", - "print(ctraj)" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": { - "id": "sm_T4o4Jhuiy" - }, - "outputs": [], - "source": [ - "# Compute the RMSD of the ligand heavy atoms relative to the starting frame\n", - "md_dist = rmsdNP(ctraj, ctraj[0],ctraj.top.select('resname LIG and not type H'))" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": { - "colab": { - 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Lim**\n", - " - Material adapted from Markov State Modeling workshop at UCSD 2017 hosted by **Frank Noe** and **Rommie Amaro**\n", - "\n", - " \n", - "# References\n", - "1. Shaw DE, Maragakis P, Lindorff-Larsen K, Piana S, Dror RO, Eastwood MP, Bank JA, Jumper JM, Salmon JK, Shan Y, Wriggers W: Atomic-level characterization of the structural dynamics of proteins. *Science* **330**:341-346 (2010). doi: 10.1126/science.1187409.\n", - "2. Molgedey, L. and H. G. Schuster, Phys. Rev. Lett. 72, 3634 (1994).\n", - "3. Pérez-Hernández, G. and Paul, F. and Giogino, T. and de Fabritiis, G. and Noé, F. Identification of slow molecular order parameters for Markov model construction. *J. Chem. Phys.* **139**:015102 (2013)\n", - "4. Swope WC, Pitera JW and Suits F. Describing protein folding kinetics by molecular dynamics simulations: 1. Theory. *J. Phys. Chem. B* **108**:6571-6581 (2004)\n", - "5. Röblitz S. and M. Weber: Fuzzy spectral clustering by PCCA+: application to Markov state models and data classification. Adv. Data. Anal. Classif. DOI 10.1007/s11634-013-0134-6 (2013) \n", - "6. Noé F, Doose S, Daidone I, Löllmann M, Chodera JD, Sauer M, Smith JC. Dynamical fingerprints for probing individual relaxation processes in biomolecular dynamics with simulations and kinetic experiments. *Proc. Natl. Acad. Sci. USA*, **108**: 4822-4827 (2011)\n", - "7. Metzner P, Schütte C, Vanden-Eijnden, E. Transition Path Theory for Markov Jump Processes. *Multiscale Model. Simul.* **7**. 1192--1219 (2009)\n", - "8. Noé F, Schütte C, Vanden-Eijnden E, Reich L and Weikl T. Constructing the Full Ensemble of Folding Pathways from Short Off-Equilibrium Simulations. *Proc. Natl. Acad. Sci. USA*, **106**:19011-19016 (2009)" - ] + "layout": "IPY_MODEL_69de14f48d5645f3b69a2cc7169d0253" + } + }, + "96c456c8ccf34594b78fca8079a790e3": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "ProgressStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "ProgressStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "bar_color": null, + "description_width": "" + } + }, + "a11ebaef400c4352ba5f52e8b6b566fa": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + 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--git a/uci-pharmsci/lectures/empirical_physical_properties/physprops.key b/uci-pharmsci/lectures/empirical_physical_properties/physprops.key index 438d58f..0606517 100644 --- a/uci-pharmsci/lectures/empirical_physical_properties/physprops.key +++ b/uci-pharmsci/lectures/empirical_physical_properties/physprops.key @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:8ee2ff6b31aaf007becd13c78780499063982baf03117f9751e70b1b5506db00 -size 18782663 +oid sha256:771347bd2b0838d8423380e8062447ffae51ff80db56ddd29fb2b602bf9fd3a2 +size 19124117 diff --git a/uci-pharmsci/lectures/empirical_physical_properties/physprops_solubility.ipynb b/uci-pharmsci/lectures/empirical_physical_properties/physprops_solubility.ipynb index 4f56ff4..70903af 100644 --- a/uci-pharmsci/lectures/empirical_physical_properties/physprops_solubility.ipynb +++ b/uci-pharmsci/lectures/empirical_physical_properties/physprops_solubility.ipynb @@ -17,6 +17,15 @@ "Here, let's take a sample molecule and calculate a series of descriptors which we might want to use in constructing a simple solubility model. " ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + " \"Open\n", + "" + ] + }, { "cell_type": "code", "execution_count": 2, @@ -159,22 +168,23 @@ } ], "source": [ - "# Run cell if using collab\n", - "\n", - "# Import condacolab python library and install condacolab (~5 minutes). \n", - "# Rerun cell after crashing\n", - "!pip install --target=$nb_path -q condacolab\n", - "import condacolab\n", - "condacolab.install()\n", + "# Run cells if using collab\n", "\n", - "#check condacolab to ensure that it works\n", - "condacolab.check()\n", - "\n", - "#other installs\n", - "#!conda install -c conda-forge nb_conda nglview py3dmol mdtraj --yes \n", - "!conda install -c anaconda scipy numpy --yes\n", - "!conda install -c openeye openeye-toolkits --yes\n", - "!pip install --extra-index-url https://pypi.org/simple --extra-index-url https://pypi.anaconda.org/openeye/simple/ -i https://pypi.anaconda.org/openeye/label/oenotebook/simple openeye-oenotebook" + "%env PYTHONPATH=" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%env PYTHONPATH=\n", + "! wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh\n", + "! chmod +x Miniforge3-Linux-x86_64.sh\n", + "! bash ./Miniforge3-Linux-x86_64.sh -b -f -p /usr/local\n", + "import sys\n", + "sys.path.append('/usr/local/lib/python3.12/site-packages/')" ] }, { @@ -215,6 +225,15 @@ "drive.mount('/content/drive', force_remount = True)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!mamba install -c openeye openeye-toolkits --yes" + ] + }, { "cell_type": "code", "execution_count": 4, @@ -422,7 +441,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -493,7 +512,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -543,9 +562,9 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python [conda env:drugcomp] *", + "display_name": "drugcomp", "language": "python", - "name": "conda-env-drugcomp-py" + "name": "drugcomp" }, "language_info": { "codemirror_mode": { @@ -557,7 +576,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.12.8" } }, "nbformat": 4, diff --git a/uci-pharmsci/lectures/energy_minimization/energy_minimization.ipynb b/uci-pharmsci/lectures/energy_minimization/energy_minimization.ipynb index 5fbfe5e..ff5b353 100644 --- a/uci-pharmsci/lectures/energy_minimization/energy_minimization.ipynb +++ b/uci-pharmsci/lectures/energy_minimization/energy_minimization.ipynb @@ -41,18 +41,16 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, - "outputs": [], "source": [ "## Preparation for using Google Colab (skip if running locally)\n", "\n", - "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MobleyLab/drug-computing/blob/master/uci-pharmsci/lectures/energy_minimization/energy_minimization.ipynb)\n", + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MobleyLab/drug-computing/blob/master/uci-pharmsci/lectures/Python_Linux_Vi/Lec1_Python_Linux_Vi.ipynb)\n", "\n", "If you are running this on Google Colab, you need to take a couple additional steps of preparation:\n", "1) Mount your Google Drive to this notebook:" @@ -88,7 +86,7 @@ } }, "source": [ - "2) **If you are running this on Google Colab, please add the installation blocks from the [getting started notebook](https://github.com/MobleyLab/drug-computing/blob/master/uci-pharmsci/Getting_Started.ipynb) here to install miniconda, libgfortran, and the OpenEye toolkits and license.**" + "2) **If you are running this on Google Colab, please add the installation blocks from the [getting started notebook](https://github.com/MobleyLab/drug-computing/blob/master/uci-pharmsci/Getting_Started.ipynb) here to install mamba, gfortran, and the OpenEye toolkits and license.**" ] }, { @@ -155,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -167,23 +165,7 @@ "slide_type": "fragment" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 7, - "metadata": { - "image/png": { - "width": 400 - } - }, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from IPython.display import Image\n", "Image('images/funnel.png',width = 400)" @@ -279,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -291,23 +273,7 @@ "slide_type": "fragment" } }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "" - ] - }, - "execution_count": 9, - "metadata": { - "image/png": { - "width": 600 - } - }, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "Image(\"images/funnel_labeled.png\",width = 600)" ] @@ -466,7 +432,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -509,7 +475,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 4, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -611,6 +577,9 @@ "metadata": { "collapsed": true, "id": "uzJdXiXrWv-D", + "jupyter": { + "outputs_hidden": true + }, "slideshow": { "slide_type": "slide" } @@ -746,6 +715,9 @@ "metadata": { "collapsed": true, "id": "UnbPUOw7Wv-E", + "jupyter": { + "outputs_hidden": true + }, "slideshow": { "slide_type": "skip" } @@ -853,7 +825,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -869,6 +841,7 @@ "name": "stdout", "output_type": "stream", "text": [ + "%pylab is deprecated, use %matplotlib inline and import the required libraries.\n", "Populating the interactive namespace from numpy and matplotlib\n" ] } @@ -883,7 +856,7 @@ "xlower = -5 #Start search at xlower\n", "xupper = 5 #End search at xupper\n", "#Define a starting guess for the location of the minimum\n", - "xstart = 0.01\n", + "xstart = 1.5\n", "#Create an array of x values for our plot, starting with xlower \n", "#and running to xupper with step size 0.01\n", "xvals = np.arange( xlower, xupper, 0.01) " @@ -891,7 +864,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 6, "metadata": { "id": "rni-l_bZWv-F", "slideshow": { @@ -902,17 +875,17 @@ "source": [ "#Define and the function f we want to minimize\n", "def f(x):\n", - " return 10*np.cos(x)+x**2-3.\n", + " return 10*np.cos(x)+x**2-8.+x\n", "#Store function values at those x values for our plot\n", "fvals = f(xvals)\n", "\n", "#Do our minimization (\"line search\" of sorts), store results to 'res'\n", - "res = scipy.optimize.minimize(f, xstart) # Apply canned minimization algorithm from scipy" + "res = scipy.optimize.minimize(f, xstart) # Apply canned minimization algorithm from scipy " ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -931,20 +904,18 @@ "Text(0, 0.5, 'f(x)')" ] }, - "execution_count": 19, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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", 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" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -963,6 +934,7 @@ "cell_type": "markdown", "metadata": { "id": "CGAEoUZAWv-G", + "jp-MarkdownHeadingCollapsed": true, "slideshow": { "slide_type": "slide" } @@ -1318,7 +1290,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 8, "metadata": { "id": "fvBCMpslWv-I", "slideshow": { @@ -1342,12 +1314,12 @@ "yvals = np.arange( ylower, yupper, 0.01)\n", "\n", "# Make a grid of x and y values\n", - "xx, yy = np.meshgrid(xvals, yvals)" + "xx, yy = np.meshgrid(xvals, yvals) " ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1362,14 +1334,12 @@ "outputs": [ { "data": { - "image/png": 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\n", 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"text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1388,7 +1358,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 30, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1404,23 +1374,21 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 28, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1631,7 +1597,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 13, "metadata": { "id": "Ddc0dgTUWv-K", "slideshow": { @@ -1661,7 +1627,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 14, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -1677,23 +1643,21 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 34, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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", 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1790,9 +1754,9 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python [conda env:drugcomp] *", + "display_name": "drugcomp25", "language": "python", - "name": "conda-env-drugcomp-py" + "name": "drugcomp25" }, "language_info": { "codemirror_mode": { @@ -1804,7 +1768,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.12.8" }, "livereveal": { "height": 900, @@ -1823,5 +1787,5 @@ } }, "nbformat": 4, - "nbformat_minor": 1 + "nbformat_minor": 4 } diff --git a/uci-pharmsci/lectures/fluctuations_correlations_error/density.py b/uci-pharmsci/lectures/fluctuations_correlations_error/density.py index f586c25..1cf4c2d 100644 --- a/uci-pharmsci/lectures/fluctuations_correlations_error/density.py +++ b/uci-pharmsci/lectures/fluctuations_correlations_error/density.py @@ -5,7 +5,7 @@ # Author: Dr Gaetano Calabro' # University of California, Irvine # ver 0.0 06/23/2016 -# Adapted Nov. 2017 by David Mobley +# Adapted Nov. 2017, Feb. 2022, Feb. 2025 by David Mobley #-------------------------------------- @@ -31,9 +31,9 @@ #trajectory file and jump straight into production skip_equilibration = False -identifier = "phenol_toluene_cyclohexane_1_10_100" +identifier = "phenol_toluene_cyclohexane" -DATA_PATH = "mixtures/amber/" +DATA_PATH = "mixtures/" RESULT_PATH = "density_simulation/" @@ -41,8 +41,8 @@ md_platform = 'OpenCL' # Some file names -prmtop_filename = DATA_PATH + identifier + '.prmtop' -inpcrd_filename = DATA_PATH + identifier + '.inpcrd' +top_filename = DATA_PATH + identifier + '.top' +gro_filename = DATA_PATH + identifier + '.gro' xml_filename = RESULT_PATH + identifier + '.xml' # For serialized system #--------------MINIMIZATION---------------- @@ -110,9 +110,10 @@ prod_data_filename = RESULT_PATH + "prod/" + identifier + "_prod.csv" prod_dcd_filename = RESULT_PATH + "prod/" + identifier + "_prod.dcd" -# Load prmtop, crd -prmtop = AmberPrmtopFile( prmtop_filename ) -inpcrd = AmberInpcrdFile( inpcrd_filename ) +# Load top, gro +gro = GromacsGroFile( gro_filename ) +top = GromacsTopFile( top_filename, periodicBoxVectors=gro.getPeriodicBoxVectors() ) + def make_path(filename): try: @@ -122,7 +123,7 @@ def make_path(filename): pass # Create System and store to OpenMM XML for easier reuse later -system = prmtop.createSystem(nonbondedMethod = PME, nonbondedCutoff = 1*nanometer, constraints = HBonds) +system = top.createSystem(nonbondedMethod = PME, nonbondedCutoff = 1*nanometer, constraints = HBonds) make_path(xml_filename) serialized_system = XmlSerializer.serialize(system) file = open(xml_filename, 'w') @@ -142,10 +143,10 @@ def minimization(): integrator = mm.LangevinIntegrator(TEMPERATURE, MIN_FRICTION, MIN_TIME_STEP) # Set Simulation - simulation = app.Simulation(prmtop.topology, system, integrator, MIN_PLATFORM) + simulation = app.Simulation(top.topology, system, integrator, MIN_PLATFORM) # Set Position - simulation.context.setPositions(inpcrd.positions) + simulation.context.setPositions(gro.positions) state = simulation.context.getState(getEnergy=True) @@ -154,7 +155,9 @@ def minimization(): # Minimization print('Minimizing...\n') - simulation.minimizeEnergy(tolerance=MIN_TOLERANCE, maxIterations=MIN_STEPS) + # Units for tolerance have changed, commenting this out + #simulation.minimizeEnergy(tolerance=MIN_TOLERANCE, maxIterations=MIN_STEPS) + simulation.minimizeEnergy( maxIterations=MIN_STEPS) state = simulation.context.getState(getPositions=True, getEnergy=True) @@ -176,7 +179,7 @@ def nvt(coords): integrator = mm.LangevinIntegrator(TEMPERATURE, NVT_FRICTION, NVT_TIME_STEP) # Set Simulation - simulation = app.Simulation(prmtop.topology, system, integrator, NVT_PLATFORM, NVT_PROPERTIES) + simulation = app.Simulation(top.topology, system, integrator, NVT_PLATFORM, NVT_PROPERTIES) # Set Position and velocities @@ -221,7 +224,7 @@ def npt(coords, velocities): system.addForce(mm.MonteCarloBarostat(PRESSURE, TEMPERATURE, BAROSTAT_FREQUENCY)) # Set Simulation - simulation = app.Simulation(prmtop.topology, system, integrator, NPT_PLATFORM, NPT_PROPERTIES) + simulation = app.Simulation(top.topology, system, integrator, NPT_PLATFORM, NPT_PROPERTIES) # Set Position and velocities simulation.context.setPositions(coords) @@ -267,7 +270,7 @@ def production(coords, velocities, box): system.addForce(mm.MonteCarloBarostat(PRESSURE, TEMPERATURE, BAROSTAT_FREQUENCY)) # Set Simulation - simulation = app.Simulation(prmtop.topology, system, integrator, PROD_PLATFORM, PROD_PROPERTIES) + simulation = app.Simulation(top.topology, system, integrator, PROD_PLATFORM, PROD_PROPERTIES) # Set Position and velocities simulation.context.setPositions(coords) @@ -301,7 +304,7 @@ def production(coords, velocities, box): d = pd.read_csv(prod_data_filename, names=["step", "U", "Temperature", "Density"], skiprows=1) density_ts = np.array(d.Density) - [t0, g, Neff] = ts.detectEquilibration(density_ts, nskip=1000) + [t0, g, Neff] = ts.detect_equilibration(density_ts, nskip=1000) density_ts = density_ts[t0:] density_mean_stderr = density_ts.std() / np.sqrt(Neff) @@ -339,7 +342,7 @@ def production(coords, velocities, box): else: import mdtraj as md # Load dcd file with relevant INFO - traj = md.load_dcd( npt_dcd_filename, prmtop_filename ) + traj = md.load_dcd( npt_dcd_filename, top_filename ) coords = traj.xyz[-1] #velocities = traj.velocities[-1] box = traj.unitcell_vectors[-1] diff --git a/uci-pharmsci/lectures/fluctuations_correlations_error/error_analysis_OpenMM_convergence.ipynb b/uci-pharmsci/lectures/fluctuations_correlations_error/error_analysis_OpenMM_convergence.ipynb index 4ad9afc..255860b 100644 --- a/uci-pharmsci/lectures/fluctuations_correlations_error/error_analysis_OpenMM_convergence.ipynb +++ b/uci-pharmsci/lectures/fluctuations_correlations_error/error_analysis_OpenMM_convergence.ipynb @@ -153,61 +153,34 @@ "\n", "- We want some inputs to work with as we see how OpenMM works\n", "- And for our sample density calculation\n", - "- [`SolvationToolkit`](https://github.com/mobleylab/SolvationToolkit) provides simple wrappers for building arbitrary mixtures\n", - " - OpenEye toolkits\n", - " - [`packmol`](https://github.com/mcubeg/packmol)\n", - " - GAFF small molecule force field (and TIP3P or TIP4P, etc. for water)\n", + "- We use the [Evaluator](https://openff-evaluator.readthedocs.io/en/stable/index.html) package to build the systems to simulate. This package does a lot more that we're not using here because it's outside scope. \n", "\n", - "Let's build a system to use later" + "Previously this notebook used a simple set of wrappers my lab had put together, [`SolvationToolkit`](https://github.com/mobleylab/SolvationToolkit), which uses the [`packmol`](https://github.com/mcubeg/packmol) tool for preparing mixtures. However, that's not really maintained so we now use OpenFF's `evaluator`.\n", + "\n", + "## Prerequisites: The normal stuff, PLUS OpenFF Evaluator\n", + "To use what follows, you will need the standard conda/miniconda/mamba installation for the class, and **additionally OpenFF Evaluator** which you can install via `conda install -c conda-forge -c omnia openff-evaluator` or `mamba install -c conda-forge -c omnia openff-evaluator`. You will also want the OpenEye toolkits installed (along with their license). If you're on Google Colab, [these instructions are probably the best place to draw from](https://github.com/MobleyLab/drug-computing/blob/master/uci-pharmsci/Getting_Started_condacolab.ipynb).\n", + "\n", + "\n", + "## Get going:" ] }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "WARNING: component name not provided; label will be used as component name\n", - "\n", - "\n", - "WARNING: component name not provided; label will be used as component name\n", - "\n", - "Unexpected errors encountered running AMBER tool. Offending output:\n", - "Exiting LEaP: Errors = 0; Warnings = 0; Notes = 0.\n" - ] - }, - { - "ename": "RuntimeError", - "evalue": "Error encountered running AMBER tool. Exiting.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mmixture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maddComponent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'cyclohexane'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msmiles\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'C1CCCCC1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;31m#Generate output files for AMBER\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mmixture\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuild\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mamber\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m/Applications/anaconda/envs/drugcomp/lib/python3.6/site-packages/solvationtoolkit-0.4.3-py3.6.egg/solvationtoolkit/solvated_mixtures.py\u001b[0m in \u001b[0;36mbuild\u001b[0;34m(self, amber, gromacs, solute_index)\u001b[0m\n\u001b[1;32m 666\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 667\u001b[0m \u001b[0;31m# Call the monomers creation and packmol systems\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 668\u001b[0;31m \u001b[0mbuild_monomers\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 669\u001b[0m \u001b[0mbuild_boxes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 670\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Applications/anaconda/envs/drugcomp/lib/python3.6/site-packages/solvationtoolkit-0.4.3-py3.6.egg/solvationtoolkit/solvated_mixtures.py\u001b[0m in \u001b[0;36mbuild_monomers\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 388\u001b[0m \u001b[0;31m#Generate amber coordinate and topology files for the unsolvated molecules\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 389\u001b[0m \u001b[0mmol_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbasename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmol2_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 390\u001b[0;31m \u001b[0mopenmoltools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mamber\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_tleap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmol_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmol2_filename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfrcmod_filename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprmtop_filename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minpcrd_filename\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 391\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 392\u001b[0m \u001b[0;31m#Read Mol2 File and write SDF file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Applications/anaconda/envs/drugcomp/lib/python3.6/site-packages/openmoltools/amber.py\u001b[0m in \u001b[0;36mrun_tleap\u001b[0;34m(molecule_name, gaff_mol2_filename, frcmod_filename, prmtop_filename, inpcrd_filename, log_debug_output, leaprc)\u001b[0m\n\u001b[1;32m 463\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlog_debug_output\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 464\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 465\u001b[0;31m \u001b[0mcheck_for_errors\u001b[0m\u001b[0;34m(\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother_errors\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'Improper number of arguments'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 466\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 467\u001b[0m \u001b[0;31m#Copy back target files\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m/Applications/anaconda/envs/drugcomp/lib/python3.6/site-packages/openmoltools/amber.py\u001b[0m in \u001b[0;36mcheck_for_errors\u001b[0;34m(outputtext, other_errors, ignore_errors)\u001b[0m\n\u001b[1;32m 255\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Unexpected errors encountered running AMBER tool. Offending output:\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 256\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0merror_lines\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 257\u001b[0;31m \u001b[0;32mraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Error encountered running AMBER tool. Exiting.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 259\u001b[0m \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mRuntimeError\u001b[0m: Error encountered running AMBER tool. Exiting." - ] - } - ], + "execution_count": 22, + "metadata": {}, + "outputs": [], "source": [ - "from solvationtoolkit.solvated_mixtures import *\n", - "mixture = MixtureSystem('mixtures')\n", - "mixture.addComponent(label='phenol', name=\"phenol\", number=1)\n", - "mixture.addComponent(label='toluene', smiles='Cc1ccccc1', number=10)\n", - "mixture.addComponent(label='cyclohexane', smiles='C1CCCCC1', number=100)\n", - "#Generate output files for AMBER\n", - "mixture.build(amber = True)" + "from openff.interchange.components._packmol import UNIT_CUBE, pack_box\n", + "from openff.units import unit, Quantity\n", + "\n", + "\n", + "from openff.toolkit.topology import Molecule\n", + "phenol = Molecule.from_smiles(\"c1ccccc1O\")\n", + "toluene = Molecule.from_smiles(\"Cc1ccccc1\")\n", + "cyclohexane = Molecule.from_smiles(\"C1CCCCC1\")\n", + "\n", + "packed_topology = pack_box([phenol, toluene, cyclohexane], [1, 10, 140], target_density = 1.0*unit.gram/unit.mL,\n", + " working_directory = '2025', retain_working_files=True)" ] }, { @@ -250,13 +223,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "slideshow": { "slide_type": "fragment" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning: importing 'simtk.openmm' is deprecated. Import 'openmm' instead.\n" + ] + } + ], "source": [ "# Example Topology generation from a couple mechanisms:\n", "\n", @@ -268,13 +249,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/davidmobley/miniforge3/envs/drugcomp/lib/python3.12/site-packages/mdtraj/core/trajectory.py:441: UserWarning: top= kwargs ignored since this file parser does not support it\n", + " warnings.warn(\"top= kwargs ignored since this file parser does not support it\")\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "acb6d560fb924721a692048fd2637902", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "787f4bf8eb6b46c7ae9e8cec982cd343", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "NGLWidget()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Load a mol2: MDTraj supports a variety of file formats including mol2\n", "import mdtraj\n", @@ -293,64 +309,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [], - "source": [ - "# Load AMBER gas phase topology\n", - "from simtk.openmm.app import *\n", - "prmtop = AmberPrmtopFile('sample_files/mobley_20524.prmtop')\n", - "print(\"Topology has %s atoms\" % prmtop.topology.getNumAtoms())\n", - "\n", - "# Gromacs files can be loaded by GromacsTopFile and GromacsGroFile but you need topology/coordinate files\n", - "# which don't have include statements, or a GROMACS installation" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "skip" - } - }, - "source": [ - "If the below cell does not run because of missing `oeommtools`, you'll need to install it, e.g. via `conda install -c openeye/label/Orion oeommtools -c omnia`" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "13\n" + "Topology has 13 atoms\n" ] } ], "source": [ - "# Load an OEMol and convert (note advantage over MDTraj for bond order, etc.)\n", + "try:\n", + " import openmm\n", + " from openmm import app\n", + "except ImportError:\n", + " from simtk import openmm, app\n", "\n", - "from openeye.oechem import *\n", - "from oeommtools.utils import *\n", - "mol = OEMol()\n", - "istream = oemolistream( 'sample_files/mobley_20524.mol2')\n", - "OEReadMolecule(istream, mol)\n", - "istream.close()\n", + "# Load AMBER gas phase topology\n", + "prmtop = openmm.app.AmberPrmtopFile('sample_files/mobley_20524.prmtop')\n", + "print(\"Topology has %s atoms\" % prmtop.topology.getNumAtoms())\n", "\n", - "# Convert OEMol to Topology using oeommtools -- so you can get a topology from almost any format OE supports\n", - "topology, positions = oemol_to_openmmTop(mol)\n", - "print(topology.getNumAtoms())" + "# Gromacs files can be loaded by GromacsTopFile and GromacsGroFile but you need topology/coordinate files\n", + "# which don't have include statements, or a GROMACS installation" ] }, { @@ -373,7 +359,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 10, "metadata": { "slideshow": { "slide_type": "subslide" @@ -383,136 +369,96 @@ "source": [ "# Example system creation\n", "#From OpenMM Docs: http://docs.openmm.org/latest/userguide/application.html#running-simulations\n", - "from simtk.openmm.app import *\n", - "from simtk.openmm import *\n", - "from simtk.unit import *\n", - "from sys import stdout\n", + "try:\n", + " import openmm\n", + " from openmm import app\n", + " from openmm import unit\n", + "except ImportError:\n", + " from simtk import openmm, app, unit\n", + "\n", "\n", "# Example System creation using OpenMM XML force field libraries -- good for biomolecules, ions, water\n", - "pdb = PDBFile('sample_files/input.pdb')\n", - "forcefield = ForceField('amber99sb.xml', 'tip3p.xml')\n", - "system = forcefield.createSystem(pdb.topology, nonbondedMethod=PME,\n", - " nonbondedCutoff=1*nanometer, constraints=HBonds)\n" + "pdb = openmm.app.PDBFile('sample_files/input.pdb')\n", + "forcefield = openmm.app.ForceField('amber99sb.xml', 'tip3p.xml')\n", + "system = forcefield.createSystem(pdb.topology, nonbondedMethod=openmm.app.PME,\n", + " nonbondedCutoff=1*openmm.unit.nanometer, constraints=openmm.app.HBonds)\n" ] }, { - "cell_type": "code", - "execution_count": 6, + "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "subslide" } }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Generated /Users/dmobley/github/drug-computing/uci-pharmsci/lectures/fluctuations_correlations_error/phenol.prmtop and /Users/dmobley/github/drug-computing/uci-pharmsci/lectures/fluctuations_correlations_error/phenol.inpcrd\n" - ] - } - ], "source": [ - "# Or you could set up your own molecule for simulation with e.g. GAFF using AmberTools\n", + "## `Simulation`: The system, topology, and positions you're simulating, under what conditions\n", + "- Could be for energy minimization, or different types of dynamics\n", + "- Has an integrator attached (even if just minimizing), including temperature\n", + "- `context` -- including positions, periodic box if applicable, etc.\n", + "- If dynamics, has:\n", + " - timestep\n", + " - velocities\n", + "- potentially also has reporters which store properties like energies, trajectory snapshots, etc.\n", "\n", - "from openmoltools.amber import *\n", - "# Generate GAFF-typed mol2 file and AMBER frcmod file using AmberTools\n", - "gaff_mol2_file, frcmod_file = run_antechamber('phenol', 'sample_files/mobley_20524.mol2')\n", - "# Generate AMBER files\n", - "prmtop_name, inpcrd_name = run_tleap( 'phenol', gaff_mol2_file, frcmod_file)\n", - "print(\"Generated %s and %s\" % (prmtop_name, inpcrd_name))\n", + "## Let's build a simple solvent mixture system to simulate\n", "\n", - "# Create System -- in this case, single molecule in gas phase\n", - "prmtop = AmberPrmtopFile( prmtop_name)\n", - "inpcrd = AmberInpcrdFile( inpcrd_name)\n", - "system = prmtop.createSystem(nonbondedMethod = NoCutoff, nonbondedCutoff = NoCutoff, constraints = HBonds)" + "\n", + "Here we use the OpenFF toolkit and force field. We will need to tell the toolkit what \"molecules\" we want and then build a topology/system containing these. To work with them in OpenFF, we need full chemical details of the molecules involved, so we create OpenFF `Molecule` objects from SMILES strings so it knows what exactly we're working with. These Molecules have considerable chemical detail and are a requirement for assigning OpenFF parameters to the full system." ] }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "7efdf934e270468da9e6374f6db507ad", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "NGLWidget()" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "execution_count": 43, + "metadata": {}, + "outputs": [], "source": [ - "# Load the mixture we generated above in Section 3\n", - "file_prefix = 'mixtures/amber/phenol_toluene_cyclohexane_1_10_100'\n", - "prmtop = AmberPrmtopFile( file_prefix+'.prmtop')\n", - "inpcrd = AmberInpcrdFile( file_prefix+'.inpcrd')\n", - "# Create system: Here, solution phase with periodic boundary conditions and constraints\n", - "system = prmtop.createSystem(nonbondedMethod = PME, nonbondedCutoff = 1*nanometer, constraints = HBonds)\n", + "# Import some OpenFF tools to use\n", + "from openff.interchange.components._packmol import UNIT_CUBE, pack_box\n", + "from openff.units import unit, Quantity\n", "\n", - "#You can visualize the above with VMD, or we can do:\n", - "traj = mdtraj.load( file_prefix + '.inpcrd', top = file_prefix + '.prmtop')\n", - "view = nglview.show_mdtraj(traj)\n", - "view" + "from openff.toolkit.topology import Molecule\n", + "\n", + "# Define the molecules that will comprise our system\n", + "phenol = Molecule.from_smiles(\"c1ccccc1O\")\n", + "toluene = Molecule.from_smiles(\"Cc1ccccc1\")\n", + "cyclohexane = Molecule.from_smiles(\"C1CCCCC1\")\n", + "\n", + "# Create a topology out of these components, packing a box\n", + "packed_topology = pack_box([phenol, toluene, cyclohexane], [1, 10, 140], target_density = 1.0*unit.gram/unit.mL,\n", + " working_directory = '2025', retain_working_files=True)" ] }, { "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, + "metadata": {}, "source": [ - "## `Simulation`: The system, topology, and positions you're simulating, under what conditions\n", - "- Could be for energy minimization, or different types of dynamics\n", - "- Has an integrator attached (even if just minimizing), including temperature\n", - "- `context` -- including positions, periodic box if applicable, etc.\n", - "- If dynamics, has:\n", - " - timestep\n", - " - velocities\n", - "- potentially also has reporters which store properties like energies, trajectory snapshots, etc.\n" + "## Next we load a force field and create an OpenMM system" ] }, { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], "source": [ - "### Let's take that last `System` we set up and energy minimize it\n", - "(The mixture of toluene, phenol, and cyclohexane we generated originally)" + "# Load force field\n", + "forcefield = ForceField(\"openff-2.0.0.offxml\")\n", + "\n", + "# Create OpenMM system from OpenFF topology\n", + "system = forcefield.create_openmm_system(packed_topology)" ] }, { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "outputs": [], + "cell_type": "markdown", + "metadata": {}, "source": [ - "# Prepare the integrator\n", - "integrator = LangevinIntegrator(300*kelvin, 1/picosecond, 0.002*picoseconds)" + "Now that we've done that, we go ahead and set up for a simulation and then do an energy minimization:" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 45, "metadata": { "slideshow": { "slide_type": "fragment" @@ -520,14 +466,26 @@ }, "outputs": [], "source": [ + "#Do imports\n", + "try:\n", + " import openmm\n", + " from openmm import unit\n", + "except ImportError:\n", + " from simtk import openmm, unit\n", + "\n", + "# Prepare the integrator\n", + "integrator = openmm.openmm.LangevinMiddleIntegrator(300*openmm.unit.kelvin, 1/openmm.unit.picosecond, 0.002*openmm.unit.picoseconds)\n", + "\n", + "\n", "# Prep the simulation\n", - "simulation = Simulation(prmtop.topology, system, integrator)\n", - "simulation.context.setPositions(inpcrd.positions)" + "simulation = openmm.app.Simulation(omm_topology, system, integrator)\n", + "positions = pdbfile.getPositions()\n", + "simulation.context.setPositions(positions)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 46, "metadata": { "slideshow": { "slide_type": "fragment" @@ -538,15 +496,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Energy before minimization (kcal/mol): 1.4e+11\n", - "Energy after minimization (kcal/mol): -5.2e+02\n" + "Energy before minimization (kcal/mol): 4.8e+03\n", + "Energy after minimization (kcal/mol): 3.8e+03\n" ] } ], "source": [ "# Get and print initial energy\n", "state = simulation.context.getState(getEnergy = True)\n", - "energy = state.getPotentialEnergy() / kilocalories_per_mole\n", + "energy = state.getPotentialEnergy() / openmm.unit.kilocalories_per_mole\n", "print(\"Energy before minimization (kcal/mol): %.2g\" % energy)\n", "\n", "# Energy minimize\n", @@ -554,13 +512,13 @@ "\n", "# Get and print final energy\n", "state = simulation.context.getState(getEnergy=True, getPositions=True)\n", - "energy = state.getPotentialEnergy() / kilocalories_per_mole\n", + "energy = state.getPotentialEnergy() / openmm.unit.kilocalories_per_mole\n", "print(\"Energy after minimization (kcal/mol): %.2g\" % energy)" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 47, "metadata": { "slideshow": { "slide_type": "subslide" @@ -572,23 +530,24 @@ "output_type": "stream", "text": [ "#\"Step\",\"Potential Energy (kJ/mole)\",\"Temperature (K)\"\n", - "100,-1104.1612038835774,56.240383237667835\n", - "200,-258.1508279070149,107.11272177220852\n", - "300,486.5828146711101,141.20197403652844\n", - "400,1000.3723654523601,173.45463216100626\n", - "500,1385.82158420236,200.48514456168613\n", - "600,1842.013234592985,220.3795403473273\n", - "700,2149.51836154611,234.29764379251156\n", - "800,2302.23369357736,248.00630540972793\n", - "900,2526.37236545236,261.8691875786783\n", - "1000,2727.16777560861,266.3660576562966\n" + "100,17157.04162585268,57.11195286732014\n", + "200,18253.11303698549,103.37917857539684\n", + "300,18942.87023913393,140.58776644381237\n", + "400,19822.22729479799,168.0460935351116\n", + "500,20492.08459460268,193.57227045390357\n", + "600,20833.860839719866,210.93963330054297\n", + "700,21295.114745969866,224.3494695882381\n", + "800,21421.524902219866,241.1816583947492\n", + "900,21762.33178698549,250.05477367125368\n", + "1000,22097.864257688616,248.56059177445317\n" ] } ], "source": [ "# While we're at it, why don't we just run a few steps of dynamics\n", - "simulation.reporters.append(PDBReporter('sample_files/mixture_output.pdb', 100))\n", - "simulation.reporters.append(StateDataReporter(stdout, 100, step=True,\n", + "import sys\n", + "simulation.reporters.append(openmm.app.PDBReporter('sample_files/mixture_output.pdb', 100))\n", + "simulation.reporters.append(openmm.app.StateDataReporter(sys.stdout, 100, step=True,\n", " potentialEnergy=True, temperature=True))\n", "simulation.step(1000) # Runs 1000 steps of dynamics\n", "state = simulation.context.getState(getEnergy=True, getPositions=True)" @@ -618,12 +577,16 @@ } }, "source": [ - "## The most bare-bones version" + "## The most bare-bones version\n", + "\n", + "This picks up from the example just prior and adds a barostat so that we can bring the system to the correct density and pressure.\n", + "\n", + "(Note if you want to run this again you may need to re-run some of the prep above.)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 48, "metadata": { "slideshow": { "slide_type": "subslide" @@ -632,18 +595,15 @@ "outputs": [], "source": [ "# We'll pick up that same system again, loading it up again so we can add a barostat before setting up the simulation\n", - "import simtk.openmm as mm\n", - "file_prefix = 'mixtures/amber/phenol_toluene_cyclohexane_1_10_100'\n", - "prmtop = AmberPrmtopFile( file_prefix+'.prmtop')\n", - "inpcrd = AmberInpcrdFile( file_prefix+'.inpcrd')\n", - "system = prmtop.createSystem(nonbondedMethod = PME, nonbondedCutoff = 1*nanometer, constraints = HBonds)\n", + "\n", + "output_file_prefix = 'mysimulation'\n", "\n", "# Now add a barostat\n", - "system.addForce(mm.MonteCarloBarostat(1*atmospheres, 300*kelvin, 25))\n", + "system.addForce(openmm.MonteCarloBarostat(1*openmm.unit.atmospheres, 300*openmm.unit.kelvin, 25))\n", "\n", "# Set up integrator and simulation\n", - "integrator = LangevinIntegrator(300*kelvin, 1/picosecond, 0.002*picoseconds)\n", - "simulation = Simulation(prmtop.topology, system, integrator)\n", + "integrator = openmm.openmm.LangevinMiddleIntegrator(300*openmm.unit.kelvin, 1/openmm.unit.picosecond, 0.002*openmm.unit.picoseconds)\n", + "simulation = openmm.app.Simulation(pdbfile.topology, system, integrator)\n", "\n", "\n", "# Let's pull the positions from the end of the brief \"equilibration\" we ran up above.\n", @@ -651,21 +611,20 @@ "\n", "\n", "# Set up a reporter to assess progress; will report every 100 steps (somewhat short)\n", - "prod_data_filename = os.path.join('sample_files', os.path.basename(file_prefix)+'.csv')\n", + "prod_data_filename = os.path.join('sample_files', os.path.basename(output_file_prefix)+'.csv')\n", "simulation.reporters.append(app.StateDataReporter( prod_data_filename, 100, step=True, potentialEnergy=True,\n", " temperature=True, density=True))\n" ] }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, + "execution_count": 49, + "metadata": {}, "outputs": [], "source": [ + "# Here we will use OpenFF Interchange to store files for possible reuse later\n", + "from openff.interchange import Interchange\n", + "\n", "# Set up for run; for a somewhat reasonable convergence threshold you probably want run_steps >= 2500\n", "# and a density tolerance of 1e-3 or smaller; higher thresholds will likely result in early termination\n", "# due to slow fluctuations in density.\n", @@ -674,12 +633,27 @@ "converged = False\n", "density_tolerance = 0.001\n", "import pandas as pd\n", - "from pymbar import timeseries as ts" + "from pymbar import timeseries as ts\n", + "import numpy as np\n", + "\n", + "# Load force field\n", + "sage = ForceField(\"openff-2.0.0.offxml\")\n", + "# Set up an Interchange object so we can export to other file formats/simulation packages\n", + "interchange = Interchange.from_smirnoff(topology=packed_topology, force_field=sage, box=packed_topology.box_vectors)\n", + "\n", + "# Export GROMACS file for possible reuse later.\n", + "identifier = \"phenol_toluene_cyclohexane\"\n", + "data_path = \"mixtures\"\n", + "import os\n", + "if not os.path.isdir(data_path): os.mkdir(data_path)\n", + "interchange.to_top(os.path.join(data_path, f\"{identifier}.top\"))\n", + "interchange.to_gro(os.path.join(data_path, f\"{identifier}.gro\"))\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 50, "metadata": { "slideshow": { "slide_type": "subslide" @@ -687,14 +661,18 @@ }, "outputs": [ { - "ename": "NameError", - "evalue": "name 'np' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;31m# Read data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0md\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprod_data_filename\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"step\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"U\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Temperature\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Density\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskiprows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdensity_ts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDensity\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;31m# Detect when it seems to have equilibrated and clip off the part prior\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'np' is not defined" + "name": "stdout", + "output_type": "stream", + "text": [ + "Current density mean std error = 0.001712 g/mL\n", + "Current density mean std error = 0.001558 g/mL\n", + "Current density mean std error = 0.002043 g/mL\n", + "Current density mean std error = 0.002262 g/mL\n", + "Current density mean std error = 0.002271 g/mL\n", + "Current density mean std error = 0.001249 g/mL\n", + "Current density mean std error = 0.000951 g/mL\n", + "...Convergence is OK; equilibration estimated to be achieved after data point 166\n", + "\n" ] } ], @@ -707,7 +685,7 @@ " density_ts = np.array(d.Density)\n", " \n", " # Detect when it seems to have equilibrated and clip off the part prior\n", - " [t0, g, Neff] = ts.detectEquilibration(density_ts)\n", + " [t0, g, Neff] = ts.detect_equilibration(density_ts)\n", " density_ts = density_ts[t0:]\n", " \n", " # Compute standard error of what's left\n", @@ -841,7 +819,7 @@ "density_ts = np.array(d.Density)\n", "\n", "# Detect when it seems to have equilibrated and clip off the part prior\n", - "[t0, g, Neff] = ts.detectEquilibration(density_ts)\n", + "[t0, g, Neff] = ts.detect_equilibration(density_ts)\n", "density_ts = density_ts[t0:]\n", "```" ] @@ -874,7 +852,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 39, "metadata": { "slideshow": { "slide_type": "fragment" @@ -884,18 +862,18 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 6, + "execution_count": 39, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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WLmfmnzKRUhAEQfAli3kWgX0W1mzr89IXQxAEQSgyOkNnBUEQhAJTlJXyBKGi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\nSUVORK5CYII=\n", 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", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -935,7 +913,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 41, "metadata": { "slideshow": { "slide_type": "fragment" @@ -946,14 +924,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "22\n" + "Data equilibrated after snapshot number 22...\n" ] } ], "source": [ "#Detect equilibration\n", "density_ts = np.array(d.Density)\n", - "[t0, g, Neff] = ts.detectEquilibration(density_ts)\n", + "[t0, g, Neff] = ts.detect_equilibration(density_ts)\n", "print(\"Data equilibrated after snapshot number %s...\" % t0)\n", "\n", "# Clip out unequilibrated region\n", @@ -964,7 +942,7 @@ "mean_density = [ density_ts[0:i].mean() for i in range(2, len(density_ts)) ]\n", "mean_density_stderr = [ ]\n", "for i in range(2,len(density_ts)):\n", - " g = ts.statisticalInefficiency( density_ts[0:i])\n", + " g = ts.statistical_inefficiency( density_ts[0:i])\n", " stderr = density_ts[0:i].std()/sqrt(i/g)\n", " mean_density_stderr.append(stderr)" ] @@ -982,7 +960,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 42, "metadata": { "slideshow": { "slide_type": "fragment" @@ -991,9 +969,9 @@ "outputs": [ { "data": { - "image/png": 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nq1tj5MuFZP27pPR3p5EtpwFPpG6BjcBbJC1Ip+pvIeuv3QP8StJpabTMu5vK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\n", 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" ] }, "metadata": {}, @@ -1287,9 +1265,9 @@ "metadata": { "celltoolbar": "Slideshow", "kernelspec": { - "display_name": "Python [conda env:anaconda-drugcomp]", + "display_name": "drugcomp", "language": "python", - "name": "conda-env-anaconda-drugcomp-py" + "name": "drugcomp" }, "language_info": { "codemirror_mode": { @@ -1301,7 +1279,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.12.8" }, "livereveal": { "height": 900, diff --git a/uci-pharmsci/lectures/fluctuations_correlations_error/fluct_corr_error.key b/uci-pharmsci/lectures/fluctuations_correlations_error/fluct_corr_error.key index 4bf034b..7448598 100644 Binary files a/uci-pharmsci/lectures/fluctuations_correlations_error/fluct_corr_error.key and b/uci-pharmsci/lectures/fluctuations_correlations_error/fluct_corr_error.key differ diff --git a/uci-pharmsci/lectures/fluctuations_correlations_error/fluct_corr_error.pdf b/uci-pharmsci/lectures/fluctuations_correlations_error/fluct_corr_error.pdf index 5368ebe..011b4f4 100644 Binary files a/uci-pharmsci/lectures/fluctuations_correlations_error/fluct_corr_error.pdf and b/uci-pharmsci/lectures/fluctuations_correlations_error/fluct_corr_error.pdf differ diff --git a/uci-pharmsci/lectures/free_energy_basics/free_energy_lecture.key b/uci-pharmsci/lectures/free_energy_basics/free_energy_lecture.key old mode 100644 new mode 100755 index 021fdcd..55785b9 --- a/uci-pharmsci/lectures/free_energy_basics/free_energy_lecture.key +++ b/uci-pharmsci/lectures/free_energy_basics/free_energy_lecture.key @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:82088877406997f68f5f0070ac5c0f2bacddd3287c312f88e93cebeab0027ee7 -size 135876937 +oid sha256:bb32494a4226608e83b2e7a086051ee4be0a228f23741f0086b39f8ee312ff4f +size 96177703 diff --git a/uci-pharmsci/lectures/free_energy_basics/free_energy_lecture.pdf b/uci-pharmsci/lectures/free_energy_basics/free_energy_lecture.pdf index 4063a7a..2976563 100644 Binary files a/uci-pharmsci/lectures/free_energy_basics/free_energy_lecture.pdf and b/uci-pharmsci/lectures/free_energy_basics/free_energy_lecture.pdf differ diff --git a/uci-pharmsci/lectures/guest_lectures/DrugEx.pdf b/uci-pharmsci/lectures/guest_lectures/DrugEx.pdf new file mode 100644 index 0000000..e679022 Binary files /dev/null and b/uci-pharmsci/lectures/guest_lectures/DrugEx.pdf differ diff --git a/uci-pharmsci/lectures/guest_lectures/README.md b/uci-pharmsci/lectures/guest_lectures/README.md new file mode 100644 index 0000000..bf62014 --- /dev/null +++ b/uci-pharmsci/lectures/guest_lectures/README.md @@ -0,0 +1,3 @@ +## Guest lectures + +- [DrugEx.pdf](DrugEx.pdf): Guest lecture by Willem Jespers (Leiden University), 2023-02-21, to accompany the [DrugEx repository](https://github.com/CDDLeiden/DrugEx). In particular, see the material under [the sequence RNNtutorial](https://github.com/CDDLeiden/DrugEx/blob/master/tutorial/Sequence-RNN.ipynb) which has a Colab link. diff --git a/uci-pharmsci/lectures/library_searching/AAAA.smi b/uci-pharmsci/lectures/library_searching/AAAA.smi new file mode 100644 index 0000000..4ed67de --- /dev/null +++ b/uci-pharmsci/lectures/library_searching/AAAA.smi @@ -0,0 +1,4233 @@ +smiles zinc_id +CO[C@H]1OC[C@@H](O)[C@H](O)[C@H]1O 4371221 +N[C@H]1[C@H](O)[C@@H](O)[C@H](CO)O[C@H]1O 5443821 +C[S@@](=O)CC(N)=O 34310585 +NC(=O)N[C@@H]1NC(=O)NC1=O 1843030 +NC(=O)CN1CCC(N)CC1 9256947 +CNC(=O)c1n[nH]c(N)n1 19844301 +NCc1cc(=O)[nH]c(O)n1 26507110 +CN1C(=O)CO[C@H]2CNC[C@@H]21 95346843 +NC(=O)[C@H]1[C@H]2C=C[C@@H](O2)[C@@H]1N 242677143 +N=C(N)[C@@H](NC=O)C(N)=O 66370807 +O[C@@H]1[C@@H](O)[C@H]2[C@@H](O)CCN2C[C@H]1O 247855053 +O=c1[nH]cc(N2CCOCC2)c(=O)[nH]1 55860 +NCCOCCOCCN 1651868 +OC[C@H]1O[C@H](O)[C@@H](F)[C@@H]1O 44154413 +CS(=O)(=O)CCS(N)(=O)=O 44594710 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2539043 +C[C@@H]1CC[C@@H]2C(C)(C)CCC[C@]2(C)O1 4026369 +CC(C)(C)C(=O)CC1CCCCC1 53766204 +C[Si](C)(C)C1=C[C@H]2CC[C@@H](C1)C2 169828449 +CC(C)c1cccc(C(C)(C)C)c1O 5761672 +CC1=C[C@H](C)[C@](C)(CC[C@@H](C)O)CC1 4027257 +C1=Cc2ccccc2Oc2ccccc21 3051878 +CCCC(C)(C)Nc1cccc(C)c1 526058846 +CCC[C@H](C)c1ccccc1 2011549 +CC[C@H](C)c1ccc(Cl)cc1 80401455 +CCC[C@](C)(CC)c1ccc(O)cc1 71773079 +CSc1cc(Cl)ccc1Cl 394211 +CC(C)C(C)(C)[C@@H]1C[C@H](C)CCC1=O 2077886 +CCC[C@H](C)Nc1ccc(C)c(F)c1 19956233 +Cc1cc(Cl)c(Cl)cc1Cl 1733812 +CCC[C]1[C](C)[C](C)[C](C)[C]1C 100132035 +Cc1ccc2cccc(Cl)c2c1 71256775 +Cc1ccc(SC(F)(F)F)cc1 153625 +CC(C)(C)C1CCCCC1 1699277 +CC(C)(C)C(=O)c1cccc(Cl)c1 2514233 +C[C@H]1CCCC=C1[Si](C)(C)C 169827611 +C[C@@H]1C=CC[C@@]23C=C[C@@]12CCCC3 71785753 +Fc1ccc(Oc2ccccc2)cc1 1682864 +CCCCCCc1ccc(O)cc1 2036134 +Cc1cccc(/C=C\c2ccccc2)n1 4522653 +CCCCNc1ccc(Cl)cc1 1577254 +CC[C@@H](C)c1ccc([C@H](N)CC)cc1 2549356 +Fc1cccc(Oc2ccccc2)c1 34099878 +Cc1ccc(Cc2cccc(C)c2)cc1 137015839 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+Cc1ccc2c(c1)NC(C)(C)C[C@@H]2C 2569935 +CCC[C@H](C)Nc1cc(F)ccc1F 19907923 +Cc1[nH]c2ccccc2c(=S)c1C 65364443 +CC1[C@H]2c3cccc4cccc(c34)[C@H]12 101048373 +CC1=CCCC(C)(C)[C@@]12CC[C@H](C)O2 1850501 +CC[C@@H](C)Nc1cc(C)ccc1C 5139966 +CC[C@H](Nc1cccc(F)c1)C(C)C 37202653 +Clc1ccc(C2=CCCC2)cc1 113993679 +Clc1ccc(Cl)c2cnccc12 19721619 +CC[C@H](C)Nc1ccc(C)cc1C 19772200 +CC(C)c1cc(N)cc(C(C)C)c1 39394588 +Cc1ccc2nc(Cl)cc(C)c2c1 3163109 +CC1=CC(C)(C)Nc2ccc(C)cc21 4003344 +Cc1ccc(F)cc1N[C@H](C)C(C)C 19953518 +Cc1cccc(N[C@H](C)C(C)C)c1C 19964299 +CC[C@@H](C)Nc1ccc(SC)cc1 19906478 +CCC[C@@H](C)Nc1cc(F)cc(F)c1 19962026 +CC1=CC(C)=C[C@@]2(C)C[C@H]2C(C)=C1 254705613 +CC(C)[C@@H](C)N[C@@H](C)C1CCCCC1 37249686 +C1CCCCCCCCC1 71613926 +Sc1ccc(-c2ccccc2)cc1 4269988 +CC[C@@H](C)[C@H](C)Nc1ccccc1C 20550986 +Fc1c(Cl)cc(Cl)cc1Cl 66325555 +CCCCC[C@@]1(C)CCC(C)(C)[C@@H]1O 1857787699 +C=COC(=O)C(C)(C)CCCCCC 2020140 +Cc1cc(N[C@@H](C)C(C)C)ccc1F 61117542 +Cc1ccc(N(C)c2ccccc2)cc1 38974059 +CCc1ccc(Cl)cc1Cl 111343952 +Cc1cc(Cl)cc(C)c1Cl 114543144 +C1CC[C@H]2C(=C3[C@H]4CCCC[C@@H]34)[C@H]2C1 216366809 +CCCC[C@H](CC)CC[C@@H](O)CC 1655700 +Cc1ccc2cc(C(C)(C)C)[nH]c2c1 25336330 +CCC[C@@H](C)C[C@H]1CCCCC1=O 1601460 +CC1=CCCC(C)(C)[C@]12CC[C@H](C)O2 1850504 +C[C@H]1CCCC[C@H]1Nc1ccccc1 7277333 +c1ccc2c(c1)Cc1ccccc1S2 1847607 +CC1CCC(=C(Cl)Cl)CC1 104836649 +CCCC[C@H](C)CCC 1602832 +O=C(O)CCCCCCCCC1CC1 12153157 +CCC(CC)Nc1cc(F)cc(F)c1 19961873 +CC(C)(C)Nc1ccc(Cl)cc1 32153624 +CCc1cccc(N[C@H](C)C(C)C)c1 19962206 +CCC[C@@H](C)Nc1ccccc1C 19965114 +CC(C)(C)c1ccccc1-n1cccc1 70205220 +Cc1cccc(N[C@@H](C)CC(C)C)c1 19877110 +C=Cc1cccc(-c2ccccc2)c1 114444224 +CC1=C(C)C[C@H]2C(C)=C(C)[C@H]2C1 71785758 +C1=C[C@@]23CCCC[C@@]2(CC1)CC3 104282272 +Clc1csc2ccccc12 1690435 +COc1ccccc1-c1ccccc1C 3124975 +CC(=O)CC[C@@H]1[C@H](C)CCCC1(C)C 6030814 +CC(C)C[C@H](C)Nc1cccc(F)c1 19920683 +CCCC[C@H](CC)CC[C@H](O)CC 1655698 +CC1=CC=C[C@@]23C=C[C@@]12CCCC3 71785763 +CCCCSc1ccc(C)cc1 1733404 +Cc1ccc(C(=O)C(C)(C)C)c(C)c1 1748859 +CC[C@H](C)[C@H](C)Nc1ccc(C)cc1 21011987 +CCC1C(C)=C(C)C(C)=C1C 2539433 +CP(C(C)(C)C)C(C)(C)C 2559504 +CCC[C@H](C)Nc1cc(F)ccc1C 19953769 +Oc1ccc(CC2CCCCC2)cc1 95721683 +Fc1ccc(F)c(-c2ccsc2)c1 217506245 +Cc1ccccc1C1=CCCCC1 1674684 +CCCCCC[C@@H](O)C1CCCC1 2077724 +CCCCCOc1ccc(Cl)cc1 39189053 +C[C@@H]1CCC[C@H](c2ccccc2)C1 1601476 +Clc1cc2ccccc2nc1Cl 14982639 +O=Cc1ccc(C2CCCCC2)s1 19088282 +Cc1ccc2c(c1)-c1ccccc1C2 1841179 +CCCCCCc1cccs1 2164365 +Clc1ccc2c(Cl)cccc2n1 39758 +Cc1ccccc1Cc1ccccc1 1674256 +Cc1ccc(Cl)c(C(F)F)c1F 66323206 +Cc1cc(C(F)(F)F)ccc1Cl 63361055 +Fc1ccccc1-c1cccs1 16947490 +CCCCCc1cc(F)cc(F)c1 59291015 +CC1=C[C@]23C=CC[C@]2(CCCC3)C1 104293693 +c1csc(-c2cc3ccccc3[nH]2)c1 66217 +C[C@H]1C[C@H](c2ccccc2)CCS1 1226854 +Cc1ccc(C)c2c1[nH]c1ccccc12 1867846 +CCC[C@H](C)Nc1cccc(C)c1C 19964347 +CC1CCC(Nc2ccccc2)CC1 19772135 +CC(C)c1cccc(C(C)C)c1N 2014547 +Cc1cc(C)cc(NC2CCCC2)c1 19952582 +CCC(CC)Nc1cccc(F)c1C 43319506 +CCCCCc1nc2ccccc2o1 2029694 +Cc1ccc(C)c(-c2ccccc2)c1 2566823 +O=C(O)/C1=C\CCCCCCCCC1 4786787 +CC(C)(C)c1ccc(Cl)cc1F 116862049 +Cc1nc2ccccc2c(Cl)c1C 1671645 +Cc1cc(C(C)(C)C)cc(C)c1C 2584498 +CCC(C)(C)C(=O)c1ccc(C)cc1 2514176 +CCO[C@H](C)C1[C@@]2(C)CCCC[C@]12C 104281696 +CCC(CC)CCC(=O)CC(C)C 1690312 +CCC[C@H](C)Nc1cc(F)cc(F)c1 19962028 +Fc1ccc2[nH]cc(C3CCC3)c2c1 98213769 +Oc1ccccc1/C=C/c1ccccc1 2530754 +Cc1cccc(N[C@H](C)CC(C)C)c1 19877108 +CC[C@H](C)[C@@H](C)Nc1ccc(F)cc1 19978799 +CCCSc1cccc(Cl)c1 96024749 +CC(C)c1ccccc1C(C)C 2034691 +CCC1=Cc2cccc(Cl)c2C1 71241975 +CC[C@H](C)[C@H](C)Nc1cccc(C)c1 21519085 +CC1=CCCC(C)(C)[C@]12CC[C@@H](C)O2 1850505 +FC(F)c1ccc2ccccc2c1 59281901 +Cc1ccc(C(F)(F)F)cc1Cl 66330506 +C1=CC[C@@]23CCCC[C@@]2(C1)CSC3 100991022 +CC(C)Cc1c[nH]c2ccc(F)cc12 96029508 +CCc1cccc(CC)c1-n1cccc1 4237695 +CC(C)c1cccc(Cl)c1F 143264764 +P#CC12CC3CC(CC(C3)C1)C2 57988473 +Cc1ccc2c(c1C)CCCC2(C)C 85925372 +CCCC1CCC(NCC(C)C)CC1 19233166 +C[C@]12CCCCC1=Nc1ccccc12 4532291 +CC[C@@H](C)[C@H](C)Nc1ccccc1 20085426 +C1=C[C@H]([C@@H]2C=CCCC2)CCC1 71785529 +CCc1cccc(C(C)(C)C)c1 2528264 +C[C@@]12CCCCC1=Nc1ccccc12 4532290 +Cc1ccc2c(c1C)N[C@H](C)C[C@H]2C 1241945 +c1ccc2nc3c(cc2c1)CCCCC3 4051026 +Clc1ccc2c(Cl)nccc2c1 36257949 +Cc1cccc(C)c1-c1cccc(O)c1 149626220 +O=Cc1cc(C2CCCCC2)cs1 40449061 +Cc1ccc(NCC(C)(C)C)cc1C 41051112 +CCC[C@@](C)(CC)c1ccc(O)cc1 71773078 +CC(C)CC(=O)c1ccc(Cl)cc1 32009434 +Cc1cccc(C)c1NC(C)(C)C 38212635 +CCCCCCC(C)C 1602836 +CCCC[C@H](C)[C@H](C)CC 90425809 +Fc1cccc(NC2CCCCC2)c1 19772288 +CC(C)=C1[C@H]2CC[C@H]1c1ccccc12 100349948 +CC(C)(C)OCc1cccc(Cl)c1 142057350 +CCCCCn1ccc2ccccc21 5309013 +CC(C)C[C@H](C)N[C@H]1CCCC[C@H]1C 37021603 +CC(C)[Si](C(C)C)C(C)C 195814983 +C=Cc1ccc(-c2ccc(C)nc2)cc1 143070146 +Cc1cccc2[nH]c(C(C)(C)C)cc12 25336333 +CC(C)Cc1c[nH]c2cc(F)ccc12 98213768 +CCCCC[C@]1(C)CCC(C)(C)[C@@H]1O 1857787701 +CC(F)(F)c1ccc(F)c(Cl)c1 66330523 +Cc1cnccc1/C=C/c1ccccc1 1234101 +Cc1ccccc1-c1ccccc1C 1580986 +CCCCCCC(=O)CCCC 1606035 +CCCCCC[C@H](CO)CCCC 2038253 +CCC(C)(C)c1ccc(O)c(Cl)c1 394938 +CCN[C@@H](C)c1cccc2ccccc12 19808026 +CCc1ccc(N[C@H](C)C(C)C)cc1 19955359 +CC[C@@H](C)[C@H](C)Nc1ccccc1F 20018926 +CCC1CCC(N[C@@H](C)C(C)C)CC1 20306223 +Cc1ccc(C)c(N[C@@H](C)C(C)C)c1 19964634 +CC(C)c1cccc(Br)c1 1651944 +Cc1cccc(N(C(C)C)C(C)C)c1 57217740 +Cc1ccc(C(=O)CC(C)(C)C)cc1 2514134 +CCCC[C@@H](C)[C@@H](C)CC 90425803 +ClC1(Sc2ccccc2)CC1 95627044 +CC[Si](CC)(CC)CC 169746808 +Cc1cccc2scc(Cl)c12 34364013 +CC[C@H](C)[C@H](C)N[C@H]1CCCC[C@@H]1C 238853101 +CC(C)C[C@H](C)N[C@H]1CCCC[C@@H]1C 19945272 +CCCCNC1CCC(CCC)CC1 21012088 +CC[C@@H](C)[C@H](C)Nc1cccc(C)c1 21519084 +CC1(c2ccc(O)cc2)CCCCC1 39369361 +CC(C)CNc1ccc(C(C)C)cc1 21042795 +Cc1cc(C)cc(NCC(C)(C)C)c1 34638583 +C[C@@H]1CC[C@H]2C(C)(C)CCC[C@]2(C)O1 4026371 +Cc1cc(C)c2nc(Cl)ccc2c1 65337475 +C[Si](C)(C)Sc1ccccc1 169746869 +Cc1ccc(-c2ccsc2)cc1F 2392376 +O=C(O)CCCCCC1CCCCC1 1577187 +CCCCCCC(=O)CC(C)C 1690301 +Cc1ccc2c(Cl)ccnc2c1C 19721836 +CC[C@H](C)Nc1ccccc1Cl 19772255 +CC[C@H](C)N[C@@H](CC)c1ccccc1 19882014 +CC(C)(C)c1cccc2cccnc12 33606572 +CC1=CCCC[C@@H]1Oc1ccccc1 5179825 +Fc1ccc(Cl)c(Cl)c1Cl 111340357 +CCCCCNC1CCC(CC)CC1 21049938 +C1=C[C@@]23CCCC[C@]2(C=C1)CCC3 71785755 +Cc1cc(S)c(Cl)c(Cl)c1 238503643 +Cc1cc(C)c2cccc-2c(C)c1 1511770 +FC(F)(F)c1ccc2ccccc2c1 77011900 +C[C@@H]1CC[C@@H]2CCCC[C@H]2C1 136742033 +c1ccc2c(c1)Nc1ccccc1O2 120208 +Cc1cc(Cl)ccc1C(F)(F)F 66329434 +CCN(CC)c1cccc2ccccc12 1693413 +CC[C@H](C)[C@H](C)NC1CCCCCC1 20003468 +FC1(F)CCC(c2ccccc2)CC1 77011862 +CSc1ccc(C(C)(C)C)cc1 1699350 +CC1=CCCC[C@H]1Oc1ccccc1 1711583 +Cc1ccc(N[C@H](C)C(C)C)cc1F 19955998 +Cc1cc(Cl)c(Cl)cc1S 238497738 +Cc1csc(-c2ccccc2)c1 2557579 +CC(C)CC(C)CC(C)C 2563392 +Cc1ccc2c(c1)NC(C)(C)C[C@H]2C 4521004 +C1=C[C@H]2C3=CCC=C3[C@H]1C21CCCC1 101081599 +Cc1cc(C)c2c3c([nH]c2c1)CCCC3 1462460 +CCC(=O)[C@H]1C[C@H](C(C)C)CC=C1C 1589956 +Cc1cccc(/C=C/c2ccccc2)n1 3941522 +Cc1cc(C)c(Cl)cc1Cl 393289 +CC(=O)c1cccc(-c2ccccc2)c1 1562199 +Cc1cc(C)c2c(Cl)ccnc2c1 19721832 +Clc1ccc2ccnc(Cl)c2c1 32299962 +C1=CCC2=C(C1)CCCCCC2 59075584 +Cc1ccc2[nH]c(=S)cc(C)c2c1 201933 +CC[C@@H](C)Nc1ccc(Cl)cc1 19772236 +CCCCCSc1ccccc1 32135461 +Cc1cc(C)cc(NC(C)(C)C)c1 33796370 +CC1=CC[C@@]23C=C[C@]2(CCCC3)C1 101047341 +Cc1cc2c(cc1C)SC(C)S2 503390 +CCCOc1ccc2ccccc2c1 2046112 +CC[C@H](C)Nc1ccc(SC)cc1 19906479 +CCC[C@H](C)C[C@@H]1CCCCC1=O 1601463 +CCCCc1ccc(Cl)cc1 2510144 +CCC[C@@H](C)c1ccccc1 2011547 +CC(C)(C)Oc1ccc(Cl)cc1 2525320 +Cc1ccc(-c2cccc(N)c2)cc1C 2529007 +Cc1cccc(Cc2cccc(C)c2)c1 59510995 +C[C@H]1CCC[C@@]1(C)c1ccccc1 71786477 +c1ccc(Sc2ccsc2)cc1 156694 +CCCCCN[C@H](C)c1ccccc1 19901307 +C[C@@H]1CCCC=C1[Si](C)(C)C 169827608 +C=Cc1ccc(Cl)c(Cl)c1 1852610 +CCC[C@@H](C)Nc1ccc(F)c(C)c1 61117395 +Clc1ccc([C@H]2C=CCCC2)cn1 45028390 +[C-]#[N+]c1cc(Cl)ccc1Cl 59544404 +c1ccc2c(c1)CC1=C2CCCC1 63361146 +CC1(C)CC(C)(C)c2ccccc21 95611788 +CCCCCC(=O)c1ccc(F)cc1 36940515 +Cc1ccc2c(c1)[C@@H](C)CC(C)(C)N2 1076068 +C1=C(c2ccccc2)CCCC1 1677082 +CC[C@H](C)[C@H](C)Nc1cccc(F)c1 21801073 +[C-]#[N+]c1ccccc1-c1ccccc1 95697981 +Cc1cccc(Cc2ccccc2)c1 95803229 +CCC(=O)c1ccc(C(C)(C)C)cc1 2144724 +CC[C@@H](C)[C@@H](C)Nc1cccc(F)c1 21801065 +Clc1cc2ccccc2c(Cl)n1 331717 +c1cc2c(cnc3sccc32)s1 480988 +CCc1ccc2c(c1)Cc1ccccc1-2 1038919 +Cc1ccc(-c2ccccc2)cc1 1095292 +CCCCCC(=O)[C@H](C)CCC 2167281 +CCC(CC)N[C@@H](C)C1CCCCC1 37414400 +CCCC[C@@H](C)NCC1CCCCC1 2491556 +CCC(CC)c1ccc(OC)cc1 34200146 +CC(C)P([C]1[CH][CH][CH][CH]1)C(C)C 100009662 +c1cc2cnc3ccsc3c2s1 481011 +CCCP(CCC)CCC 1081139 +Cl/C=C/CSc1ccccc1 1704969 +C=Cc1ccc(Cl)cc1Cl 2038875 +CC1=C[C@@H](C)[C@](C)(CC[C@H](C)O)CC1 4027258 +CCCC(=O)c1ccc(C(C)C)cc1 5417428 +Cc1ccc(-c2cccs2)cc1 6814964 +CCCNc1cccc2ccsc12 307229386 +CC(C)C(F)(F)c1ccc(F)cc1 66329346 +C1=C[C@]23CCCC[C@]2(C=C1)CCC3 101047353 +C[C@@H]1CCC[C@@H](Nc2ccccc2)C1 7277040 +CCC(CC)N[C@H](C)c1cccs1 19883883 +CCC(CC)Nc1ccc(C)c(C)c1 2546115 +CC(C)Nc1cccc(C(C)C)c1 20136546 +CCc1ccc(-c2ccc(O)cc2)cc1 12649743 +CCC(C)(C)C1CCC(CCO)CC1 34427895 +Cc1cc(N)cc(-c2ccccc2C)c1 71507545 +CC[C@@H](C)[C@H](C)NC1CCCCCC1 20003465 +Cc1cccc(-c2ccccc2)c1 2031557 +c1csc(Sc2ccsc2)c1 161196 +CCCCNc1ccccc1Cl 19942738 +CC(=O)CCC1=C(C=C(C)C)CCC1 45071597 +CC(F)(F)c1cccc2ccccc12 77011889 +CCCCCC[C@@H]1CCCCC1=O 5820480 +Cc1ccc(OCc2ccccc2)cc1 1691448 +CC1=CC(C)(C)Nc2cc(C)ccc21 4003347 +Cc1c(Cl)cc(S)cc1Cl 146734569 +Cc1cc(C)c(Cl)c(C)c1Cl 2507459 +CCCCN[C@@H](CC)c1ccccc1 5973602 +CCC(CC)Nc1ccc(C)c(F)c1 19955915 +Cc1ccc2[nH]c3ccccc3c2c1 1869244 +FC(F)=C(F)C1=CCCCC1 64220167 +O=C(CC1CCCC1)CC1CCCC1 140103185 +CCCCCc1ccc(F)cc1 2571863 +C/C=C/C(C)=C/c1ccc(OC)cc1 498049465 +[C-]#[N+]c1ccc(Cl)c(Cl)c1 95698202 +C1=C[C@@]23CCCC[C@]2(CC1)CC3 104282282 +Cc1cccc(NCC(C)(C)C)c1C 41051139 +CC1(C)CCC=C(c2cccnc2)C1 725245975 +CSc1ccc(Cl)c(Cl)c1 397775 +CCCCCCC(=O)C1CCCC1 2077791 +CC1=C[C@@]23C=C[C@@]2(C=C1)CCCC3 101047414 +CC(C)CCCC(=O)c1ccccc1 37117010 +C1=C[C@]23CCCC[C@@]2(CC1)CC3 104282277 +Cc1ccccc1NCc1ccccc1 1586388 +Cc1cccc2c1NC(C)(C)C[C@H]2C 33947724 +CC(C)c1ccc(C(C)C)c(N)c1 34558032 +CC/C(C)=C/O/C=C(\C)CC 1534067589 +CC(C)(C)c1cc2cc(F)ccc2[nH]1 1518242 +Cc1ccc(-c2cccc(C)c2)cc1 2040357 +Cc1cccc2cc(C(C)(C)C)[nH]c12 1518962 +CCc1cccc2sc(Cl)nc12 2455688 +c1ccc(-c2ccc3[nH]ccc3c2)cc1 16946102 +Cc1cccc(CNc2ccccc2)c1 19879236 +O=Cc1ccccc1C1CCCCC1 12649586 +Clc1ccc(NC2CCCC2)cc1 19921035 +CCCC[C@H](C)NCC1CCCCC1 2491558 +Cc1cc(C)cc(N[C@@H](C)C(C)C)c1 19952860 +CC(C)Nc1ccccc1C(C)C 19907262 +c1ccn(-c2ccc3sccc3c2)c1 82375625 +Nc1ccc2c(c1)sc1ccccc12 480818 +C[C@H](Nc1ccccc1)c1ccccc1 3075599 +CCCCCC(=O)CCCCC 2040174 +Cc1cc(C)c2c(C)cccc2c1 72099276 +CC(C)CC1CCCCC1 1566661 +FC(F)(F)CCCCC(F)(F)F 64370138 +Cc1ccc(Nc2ccccc2)cc1 968704 +Clc1ccc2sccc2c1 16697568 +CCCCc1ccc(CC)s1 2510289 +CC1=C[C@@H](C)[C@](C)(CC[C@@H](C)O)CC1 4027256 +Cc1ccc(C)c2c(C)cccc12 2038871 +Cc1cc(C)c(C(F)(F)F)c(C)c1 66383336 +CC[C@H](N[C@H](C)CC)c1ccccc1 19882012 +CCCC[C@H](C)[C@@H](C)CC 90425805 +Cc1ccc(NC2CCCC2)cc1C 19920077 +C[C@@H]1CC[C@@H](C)P1C1CCCC1 60290893 +CCC[C@@H](C)Nc1cccc(C)c1 19877323 +C[C@H]1CCC[C@H](Nc2ccccc2)C1 7277340 +Cc1ccc2[nH]c3ccc(C)cc3c2c1 32233372 +C[C@@H]1CC[C@@H]2CCCC[C@@H]2C1 78244451 +CC(C)C(=O)c1ccc(C(C)C)cc1 5413598 +CC(C)[C@@H](C)Nc1ccccc1Cl 19942663 +CCCC[C@@H](C)[C@H](C)CC 90425807 +C[C@@H](c1ccccc1)c1ccc(O)cc1 1086601 +CCCCCC[C@H](O)CCCC 2508102 +CC(C)[C@H](C)N[C@@H](C)C1CCCCC1 37249688 +C[C@H]1CCC[C@H](c2ccccc2)C1 1601475 +CCCCCCNCCCCCC 1683599 +CC(=O)CC[C@@H]1[C@@H](C)CCCC1(C)C 6037492 +c1ccc2c(c1)[C@H]1CC[C@H]2[C@@H]2CC[C@@H]12 230109826 +CCCc1[nH]c2ccccc2c1CC 83728781 +C1=C[C@H]2O[C@H]1c1cc3ccccc3cc12 100807345 +Cc1ccc([C@H](C)C(F)(F)F)cc1 66323188 +CSc1ccc(Cl)cc1Cl 2570859 +CC(C)P(C(C)C)C(C)C 1765050 +CCC12CC3CC(CC(C3)C1)C2 59681824 +CC1=CC=C[C@]23C=C[C@]12CCCC3 71785764 +CSc1cc(Cl)cc(Cl)c1 404113 +CC(=O)c1ccc(-c2ccccc2)cc1 1504550 +CNc1cccc(Oc2ccccc2)c1 2513111 +Clc1cc(Cl)c2ccccc2n1 331138 +C[C@H]1CCCC[C@@H]1Nc1ccccc1 7277328 +CCC[C@H](C)C[C@H]1CCCCC1=O 1601461 +Fc1cccc(-c2cccc(F)c2)c1 2579114 +CC1[C@@H]2c3cccc4cccc(c34)[C@@H]12 101048377 +CC(C)c1cccc(C(C)C)c1C=O 114911634 +CC1=C[C@@H]2[C@H]3C=C[C@@H](c4ccccc43)[C@H]12 253592448 +Fc1ccc(-c2cccs2)c(F)c1 2563754 +CC(C)(C)[Si](C)(C)[Si](C)(C)C 169794789 +C[C@@H]1C=CC[C@]23C=C[C@@]12CCCC3 101047344 +CC(C)CCC[C@@H](C)NCCC(C)C 2040424 +CCO[C@@]1(C(C)C)CC=C(C)CC1 5861006 +CC(C)(C)c1cccc(Cl)c1 111341171 +CCC(C)(C)Nc1cccc(C)c1 257373359 +CC(C)c1ccc2oc(Cl)nc2c1 33420150 +CCCC(CC)CCC 2038775 +CC(C)C[C@H](C)N[C@@H]1CCCC[C@H]1C 37021604 +CCC[C@@H](C)Nc1cc(F)ccc1C 19953764 +C[C@@H](Nc1ccccc1)c1ccccc1 1643339 +CC[C@H](C)N[C@H]1C[C@@H](C)CC(C)(C)C1 19882786 +Cc1cccc2c1-c1c(C)cccc1C2 80687043 +CC(C)C[C@@H](C)Nc1ccccc1F 19944341 +CCc1cccc(N[C@@H](C)C(C)C)c1 19962204 +Cc1ccc(Cl)cc1C(F)(F)F 66329439 +CC(C)CCC[C@H](C)CCCCO 2106141 +Cc1ccc(N[C@@H](C)C(C)C)cc1F 19956002 +Cc1cc(F)ccc1NCC(C)(C)C 41051131 +CCC[C@H](C)Nc1ccc(C)cc1F 20371386 +C(=C\c1ccccc1)\c1ccccc1 8585877 +CC[C@H](C)[C@@H](C)Nc1cccc(F)c1 21801068 +CC[C@H](C)[C@@H](C)Nc1ccccc1 20085425 +CCCCOc1ccc(Cl)cc1 1732341 +Cc1cc(C)c2ccc(Cl)cc2n1 38148210 +Cc1ccc(C2=CCCCC2)cc1 1674682 +c1ccc(Oc2ccsc2)cc1 2556065 +C[C@@H]1CCC[C@@]1(C)c1ccccc1 71786476 +CC[C@H](C)Nc1ccc(Cl)cc1 19772238 +CCC(CC)c1cc(F)ccc1F 146654190 +CC[C@H](Nc1ccccc1)C(C)C 37186072 +CC(C)[C@H](C)Nc1cccc(Cl)c1 19920402 +c1ccc2sc(C3CCC3)nc2c1 66324320 +CCC[C@@H](C)Nc1ccc(F)c(F)c1 19918408 +Cc1ccc(Cl)c(C(F)(F)F)c1 2243599 +CC(C)(C)c1cc(Cl)ccc1F 144655702 +Fc1c(Cl)cccc1C1CCC1 669379219 +CCCC1CCC(F)(F)CC1 77011849 +CCC(CC)Nc1cccc(C)c1 19877143 +C=Cc1ccc(-c2ccccc2)cc1 1688878 +CCCCCC12CCC(O)(CC1)CC2 2471450 +N#[N+]c1ccc(Nc2ccccc2)cc1 3954158 +Clc1cc(Cl)c2ccoc2c1 2510200 +c1ccc(-n2ccc3ccccc32)cc1 3083155 +C[C@@H]1CCCC[C@@H]1Nc1ccccc1 7277014 +CC(C)c1ccc(F)c(Cl)c1 75838897 +CCC(CC)Nc1cccc(C)c1C 19964279 +Clc1ccc2c(Cl)ccnc2c1 119471 +CC12CC3CC(C1)CC(C)(C3)C2 54962211 +Cc1ccc(S)c(C(C)(C)C)c1 113601327 +CCCCC[C@H](O)/C=C/C(C)(C)C 3132866 +CC(C)(C)c1ccc(F)c(Cl)c1 114037369 +CCCCC[C@@H](C)CC 1602830 +Cc1ccc(C)c(NC2CCCC2)c1 19964578 +CC(C)c1cccc(C(C)C)c1O 968303 +CCc1ccccc1N[C@H](C)C(C)C 19956638 +CCC[C@@H](C)Nc1ccc(F)cc1C 19961484 +CC(=O)CC(C)(C)C1CCCCC1 50934988 +COc1ccc2oc3ccccc3c2c1 4529157 +CCN(CC)C(C)(C)CC(C)(C)C 2564785 +C1=C[C@@]23CCCC[C@@]2(C=C1)CCC3 101047358 +c1ccc(-c2cc3c([nH]2)CCCC3)cc1 5604493 +CC[C@@H](C)Nc1cc(C)cc(C)c1 19772355 +Cc1ccc(C(C)(C)C)cc1C 2390942 +Cc1ccc2c(c1)[nH]c1c(C)cccc12 6071476 +CC(C)c1ccc(N)c(C(C)C)c1 22216178 +Cc1cccc(NC2CCCC2)c1C 19964234 +Clc1cc(Cl)c2cnccc2c1 82767274 +C[C@]12CC=CC[C@@]1(C)CCCC2 71785760 +CC(C)Cc1c[nH]c2c(F)cccc12 98213753 +Fc1ccc(NC2CCCCC2)cc1 19942472 +CC[C@H](C)[C@@H](C)Nc1ccc(C)cc1 21011985 +Cc1ccc(C2(C)CC=CC2)cc1 59671692 +OC(C1CCCCC1)C1CCCCC1 1689945 +CCCc1ccc(C(C)(F)F)cc1 77011821 +CCC(C)(C)Nc1ccc(Cl)cc1 526058835 +CC(C)c1ccc2oc(=S)[nH]c2c1 38761609 +CCC[C@H](C)N[C@@H]1CCC[C@@H](C)[C@H]1C 238855109 +c1ccc(-c2c[nH]c3ccccc23)cc1 1023851 +Cc1ccc2cc(C)c(C)cc2c1 1037159 +Cc1cc2cccc(C)c2s1 5782779 +C[C@@H]1C[C@H](c2ccccc2)CCS1 1226855 +c1ccc([C@H]2C[C@@H]2c2ccccc2)cc1 1705929 +Clc1ccc(SCC2CC2)cc1 204392896 +CC[C@@H](Nc1cccc(F)c1)C(C)C 37202654 +CCCCCN[C@H](C)c1cccs1 19901350 +CCC[C@@H](C)CC(=O)c1ccccc1 53765111 +C[C@@H]1C=CC[C@]23C=C[C@]12CCCC3 257358370 +C[C@]12C[C@@H]3C[C@](C)(C1)C[C@@](Cl)(C3)C2 100231532 +C(=C/c1cccs1)\c1ccccc1 1038965 +Cc1ccccc1C(=O)CC(C)(C)C 2514132 +CC1=C[C@H]2[C@H]3C=C[C@@H](c4ccccc43)[C@H]12 253592440 +CC1(C)CCC=C1[Si](C)(C)C 169827519 +Cc1ccc(C)c(SC(C)C)c1 96024719 +Cc1ccc(F)c(N[C@H](C)C(C)C)c1 20471728 +Cc1ccc(F)cc1NCC(C)(C)C 41051124 +Cc1ccc(N[C@H](C)C(C)C)c(C)c1 19877457 +c1ccc2c(c1)CCc1ccccc1N2 1470731 +CC(C)Nc1ccc(C(C)C)cc1 19772324 +Fc1ccc(-c2ccc(F)cc2)cc1 404361 +Clc1ccc2ccncc2c1Cl 16124161 +Cc1cc(S)c(Cl)cc1Cl 2584568 +Fc1ccc(Cl)cc1C1CCC1 669380232 +Cc1csc2ccc(Cl)cc12 162004 +Cc1c(C)c(C)c(C)c(C)c1C 1672878 +Cc1ccccc1Cc1ccccc1C 78206064 +CC[C@@H](C)N[C@H](C)c1ccc(C)s1 19882849 +CCC[C@H](C)Nc1cc(C)ccc1F 20471600 +CCCCCc1ccc(C(C)=O)cc1 1697731 +COc1ccc(C2=CCCCC2)cc1 1708376 +CC(C)CC(=O)c1cccc(Cl)c1 36179736 +COc1ccccc1[C@H]1C=CCCC1 2173792 +CCCCC(=O)c1ccc(Cl)cc1 1707965 +Cc1cccc(N[C@@H](C)C(C)C)c1C 19964300 +Cc1cc(N[C@H](C)C(C)C)ccc1F 61117544 +Oc1ccc(/C=C/c2ccccc2)cc1 1676024 +C1=CCCCCCCCC1 1691774 +C1CC2CCC[C@H]3CC[C@H](C1)C23 90751171 +Cc1ccc(C(F)(F)F)c(Cl)c1 2556409 +CCCCCCc1csc(C=O)c1 95830640 +Cc1[nH]c2c(ccc3ccccc32)c1C 1459899 +CCC(CC)Nc1cc(F)ccc1C 19953425 +O=Nc1ccc(Nc2ccccc2)cc1 3861649 +Fc1cccc2c(C3CCC3)c[nH]c12 96029513 +C1CCC([C@@H]2CCCSC2)CC1 22015961 +CC1=CC(C)=C[C@]2(C)C[C@@H]2C(C)=C1 254705610 +CC1(C)CCC=C(c2ccncc2)C1 725246032 +Cc1cccc(C)c1-c1ccc(O)cc1 33427267 +Cc1cc(-c2ccccc2C)ccc1O 65340208 +C(=C/c1ccsc1)\c1ccccc1 984455 +CCC[C@@H](C)Nc1ccc(C)c(F)c1 19956229 +CCC(C)(C)C1CCC(OC)CC1 2163958 +C1=C[C@H](c2ccccc2)CCCC1 3075454 +Fc1cc(Cl)ccc1C1CCC1 113826764 +CC(C)(C)c1ccc(C(C)(C)C)[nH]1 39274311 +Clc1cc(Cl)c2ncccc2c1 1722852 +c1coc(-c2ccc(-c3ccco3)[nH]2)c1 2519527 +CC[C@H](C)[C@H](C)Nc1ccccc1 20085427 +CC1=C(C)C2(C)C(C)=C(C)C12C 2036866 +NC1CCCCCCCCCCC1 3860297 +CCCC[C@H](CC)C[C@H]1CCCCN1 15442175 +CC[C@H](C)C(=O)[C@@H]1CCCC[C@H]1CC 82658275 +CCC[C@H](C)Nc1ccccc1C 19965115 +Cc1c(C)c(C)c(S)c(C)c1C 20268022 +CC[C@H](Nc1cccc(C)c1)C(C)C 37185353 +CCCCC[C@@]1(C)CCC(C)(C)[C@H]1O 1857787702 +CCCCN[C@H](CC)c1ccccc1 1622063 +Cc1cc(C)c2sc(Cl)nc2c1 2455719 +CCC[C@@H](C)Nc1cccc(F)c1C 43319713 +CCCCC(CCCC)C(C)=O 1598175 +CCC(C)(C)[C@H]1CC=CCC1 2162338 +Cc1nc2ccc3ccccc3c2s1 155600 +C[C@@H]1CCC[C@H](Nc2ccccc2)C1 7277028 +CCO[C@H](C)[C@H]1[C@]2(C)CCCC[C@]12C 253386833 +Cc1ccc(-c2ccccc2N)c(C)c1 967463 +CSc1cc2ccsc2s1 480676 +C=C(c1ccccc1)c1ccccc1 1688163 +COc1ccc2ccccc2c1Cl 35822655 +COc1ccccc1[C@@H]1C=CCCC1 2173790 +Cc1c(C)c(C)c(Cl)c(C)c1C 3123623 +CCCC1CCC(N[C@@H](C)CC)CC1 20564814 +c1ccc2c3c(ccc2c1)CCCC3 1758806 +CCCC(C)(C)Nc1ccc(F)cc1 526058839 +Cc1ccc(-c2cc(C)c(C)cn2)cc1 1239479367 +CC(C)Sc1ccc(Cl)cc1 33835179 +CC(C)(C)OCc1ccc(Cl)cc1 79929931 +C1=CC[C@]23CCCC[C@]2(C1)CSC3 100991026 +C1=C[C@@H](c2ccccc2)CCCC1 3075453 +Cc1ccc2sc(Cl)nc2c1C 2455714 +c1cc2ccc(-c3ccsc3)cc2[nH]1 2526992 +CCC(CC)Nc1ccc(F)cc1C 19961185 +CC[C@H](C)[C@H](C)Nc1ccccc1F 20018928 +Clc1cccc2nccc(Cl)c12 1734732 +CCCCC(C)(C)CC 2564085 +CCCCC1=Cc2ccccc2C1 55168351 +CCC[C@@H](C)[C@H](C)CCC 71789892 +CCC[C@@H](C)C[C@@H](C)CC 72099237 +CC[C@H]1C([Si](C)(C)C)=CC[C@@H]1C 169829358 +Cc1cc(C)cc(C(C)(C)C)c1 1712653 +Fc1cc(Cl)cc(F)c1C(F)F 66323195 +Fc1ccc(Cl)c(F)c1C(F)F 66323203 +CCCc1ccc(C(F)(F)F)cc1 59302226 +CCC[C@@H](C)Nc1cccc(C)c1C 19964346 +CC[Si](CC)(CC)C(F)(F)F 169806106 +CCCCOc1cccc(Cl)c1 59784760 +Fc1ccc(Cl)c2ccccc12 86485938 +c1ccc(C2SCCCS2)cc1 393243 +Cc1ccc(F)c(NCC(C)(C)C)c1 41051210 +CC[C@H](N[C@H]1CCCC[C@H]1C)C(C)C 238854031 +CCC[C@H](C)C[C@@H](C)CC 72099238 +CC[C@@H](C)[C@@H](C)Nc1ccccc1C 20550982 +CC(C)P(C(C)C)C1C=CC=C1 135937511 +Clc1cccc2sccc12 89195313 +CCCC1CCC(N[C@H](C)CC)CC1 20564816 +CC1=CC=C[C@]23C=C[C@@]12CCCC3 101047431 +C/C=C/CSc1ccccc1C 2165434 +Cc1ccccc1-c1ccccc1 1680848 +CC[C@H](C)[C@H](C)N[C@H]1CCCC[C@H]1C 238853103 +CCC[C@@H](C)Nc1ccc(C)cc1F 20371383 +CCCCCCc1ccccc1O 31623032 +CCCCC[C@]1(C)CCC(C)(C)C1=O 5820949 +CCC(=O)c1ccc([C@H](C)CC)cc1 2576203 +Cc1cc2c(C)nc3ccccc3c2o1 274923 +CCCOc1cccc2ccccc12 2168373 +CC(C)(C)C(=O)c1ccccc1Cl 38078367 +C1C[C@H]2CCC[C@@H]3CC[C@H](C1)[C@@H]23 299754673 +Cc1cc2nc(Cl)sc2cc1C 11919326 +C[C@@H]1C=CC[C@@]23C=C[C@]12CCCC3 257358369 +CNC1CCCCCCCCCCC1 4782889 +Cc1ccc(NCC(C)(C)C)c(F)c1 41051207 +Cc1c(S)cc(Cl)cc1Cl 231270519 +c1ccc2c(c1)oc1ccccc12 3861058 +Cc1cc(C)c2nc(Cl)sc2c1 2455715 +CC1=CC[C@]23C=C[C@]2(CCCC3)C1 71785751 +CCC[C@@H](C)C[C@H](C)CC 2564066 +CCCC[C@@H](CC)CC[C@@H](O)CC 1655699 +CCc1ccc(NCC(C)(C)C)cc1 41051125 +CC(C)c1cc(Cl)ccc1F 75838821 +C[C@H]1CCC[C@@H](Nc2ccccc2)C1 7277351 +FC(F)c1cccc(Cl)c1Cl 112731853 +CC[C@@H](C)[C@@H](C)Nc1ccccc1F 20018920 +COc1ccc2ccc(Cl)cc2c1 32232106 +CC1=Cc2c(ccc3ccccc23)C1 72099269 +CCCCOc1cc(C)cc(Cl)c1 261493852 +CCCCc1cc2ccccc2o1 2141025 +CCCCNc1cccc(C)c1C 19964338 +CCC(CC)Nc1ccc(C)cc1 19964439 +OC1CCCCCCCCCCC1 3860295 +CC(C)C(=O)c1cccc2ccccc12 39203327 +CCC[C@@H](C)Nc1cc(C)ccc1F 20471599 +C=C1c2ccccc2-c2ccccc21 67738097 +CC[C@H](C)NC1CCC(C(C)C)CC1 54554363 +C/C(=C/C(C)C)c1ccccc1 71782929 +Cc1cc(=S)[nH]c2c(C)cccc12 5932126 +CC(C)C[C@H]1CCC[C@H](CC(C)C)N1 34528953 +CC(C)[C@@H](C)Nc1ccc(Cl)cc1 19921165 +c1cc2c3c(cccc3c1)CSC2 1721897 +O=C(O)C1=CCCCCCCCCC1 254408414 +C1=Cc2ccccc2Cc2ccccc21 1847609 +Cc1ccc(F)cc1N[C@@H](C)C(C)C 19953522 +Clc1ccc2ccsc2c1 89195397 +C[C]1[CH][C](C)[C]2[C]1C[C@H]1C[C@H]2C1(C)C 104305698 +CC(C)CCC[C@H](C)NC1CCCC1 7989132 +CC[C@@H](C)N[C@H](CC)c1ccccc1 19882016 +CCCC1(CCC)CCC(=O)CC1 2562370 +CCCCCC(=O)c1ccc(C)cc1 5417874 +Cc1ccccc1/C=C/c1ccncc1 169429696 +CC(C)CCCCC(C)C 1693348 +CC(C)(Cl)CCC(C)(C)Cl 1600593 +Cc1[nH]c2ccc3ccccc3c2c1C 21982742 +Clc1ccc2cccnc2c1Cl 38537521 +C[C@@H]1CC[C@H]2CCCC[C@@H]2C1 78244457 +Sc1ccc2cc(Cl)ccc2c1 82048874 +Clc1cc(Cl)c(Cl)s1 2244224 +CC(C)C(C)(C)[C@@H]1CCC(=O)[C@@H](C)C1 2077786 +CCCCCCC(=O)c1ccccc1 1995239 +CCC1CCC(N[C@H](C)C(C)C)CC1 20306224 +C1CC[C@@H]2C(=C3[C@@H]4CCCC[C@@H]34)[C@H]2C1 100993740 +Clc1cccc(SCC2CC2)c1 261494252 +C=Cc1ccc(C(C)(C)C)cc1 2039279 +CC(=O)CCC1=C(C)CCCC1(C)C 2565574 +CC(C)(C)C1=CCCCC1 57988182 +CC1=C(C)C=CC(C)=C(C)C=C1 71785759 +S=c1sc2ccccc2s1 345307 +C=Cc1c(Cl)cccc1Cl 404353 +Cc1ccc(NCc2ccccc2)cc1 1586389 +CCC(CC)(CC)c1ccc(N)cc1 34414609 +ClC(Cl)(Cl)c1ccccc1 1653138 +c1ccc2c(c1)CCC1=C2CCCC1 1711500 +Fc1ccccc1Nc1ccccc1 2243136 +C1=C[C@@H]([C@@H]2C=CCCC2)CCC1 71785530 +CCCCCC[C@H](O)C1CCCC1 2077722 +C=Cc1ccc(-c2cccnc2C)cc1 725241408 +C[C@@H]1CC[C@@H]2C(C)(C)CCC[C@@]2(C)O1 12656702 +COc1ccc(C2CCCCC2)cc1 1693411 +Cc1cccc(Cc2ccccc2C)c1 95934861 +Cc1ccc2c(c1C)N[C@@H](C)C[C@@H]2C 1241944 +FC(F)c1ccc(C(F)F)cc1 2379325 +CCC[C@@H](C)Nc1cc(F)ccc1F 19907922 +C[C@@]12CC3CC(Cl)(C1)C[C@](C)(C3)C2 66055322 +Cc1cc(C)cc(C(=O)C(C)(C)C)c1 2514240 +C1=C[C@H](c2ccccc2)c2ccccc21 3198815 +Cc1cccc(NC2CCCCC2)c1 19772144 +CC(C)N[C@@H]1CCCC[C@H]1C(C)(C)C 19880409 +CC[C@H](C)N[C@H](C)c1ccc(C)s1 19882850 +CCCCc1ccc2[nH]ccc2c1 78279097 +C1=Cc2ccccc2Nc2ccccc21 1036792 +CSc1ccc2ccccc2c1 2023114 +CCCCCCC1CCC(=O)CC1 1577482 +Cc1ccc(CNc2ccccc2)cc1 1606288 +CCCCN(CCCC)CCCC 1675345 +Cc1ccc(-c2ccc(C)cc2)cc1 8444949 +Cc1ccc(N[C@H](C)CC(C)C)cc1 19964418 +c1ccc2c(c1)Nc1ccccc1S2 100009616 +CC1=CC(C)=C[C@@]2(C)C[C@@H]2C(C)=C1 254705611 +CC[C@@H]1C[C@@H](CC)P1[C]1[CH][CH][CH][CH]1 100009727 +FC(F)(c1ccccc1)C1CCC1 77011828 +CC[C@@H](C)SCc1ccccc1 95147710 +C[Si](C)(C)CSc1ccccc1 170153466 +CC(C)=CCC/C(C)=C/CC[C@@H](C)O 1808775 +CC(C)C(C)(C)Nc1ccc(F)cc1 526058837 +CCC(CC)Nc1ccc(C)cc1F 20371943 +CCNc1ccc(-c2ccccc2)cc1 35022250 +CC(C)CC(=O)C[C@@H](C)CC(C)C 1851015 +CC[C@H](C)[C@H](C)Nc1ccccc1C 20550988 +Cc1ccc2c(Cl)cc(C)nc2c1 39256125 +CC(C)c1ccc2ccccc2c1 1655712 +CCCCC[C@]1(C)CCC(C)(C)[C@H]1O 1857787700 +CC(C)N[C@@H]1CCCC[C@@H]1C(C)(C)C 19880406 +CC(=O)C(CC=C(C)C)CC=C(C)C 1841047 +CC[C@H]1C[C@H](CC)P1[C]1[CH][CH][CH][CH]1 100009667 +CN(C1CCCCC1)C1CCCCC1 1720041 +CC[C@@H](C)[C@H](C)Nc1ccc(F)cc1 19978804 +Cc1cc(S)cc(Cl)c1Cl 263625149 +CCCCCCc1ccc(C=O)s1 43199410 +c1csc(-c2ccc3cc[nH]c3c2)c1 2527003 +Cc1cc2cccc(C)c2nc1Cl 19721533 +C1CC2CCC[C@@H]3CC[C@@H](C1)C23 90751173 +Cc1cccc(C)c1-c1ccccc1 111341176 +CC1=CC(C)=C[C@]2(C)C[C@H]2C(C)=C1 254705612 +c1cc(C2CCCCC2)cs1 1442954 +Cc1cccc(-c2cccc(C)c2)c1 1689951 +C[C@@H]1C[C@@H](c2ccccc2)CCS1 1226853 +Cc1ccc(-c2ccccc2)s1 5241107 +CC[C@@H](C)[C@H](C)Nc1ccc(C)cc1 21011986 +Cc1ccc(Cc2ccc(C)cc2)cc1 49942698 +C1=CC[C@H](c2ccccc2)CC1 2037482 +CCCNc1cccc(C(C)C)c1 21515804 +CC(C)(C)COc1cccc(Cl)c1 60309880 +Cc1ccc2ccc3c(c2c1)CC=C3 71787038 +CCc1c(C)[nH]c2ccc(Cl)cc12 83530022 +CCc1ccc(NC2CCCC2)cc1 19955124 +CCCCc1cncc(CCCC)c1 1689944 +Clc1cc(Cl)c2occc2c1 6119186 +Cc1cc(Cl)cc(C(C)(C)C)c1 65337080 +Cc1cc(F)ccc1N[C@H](C)C(C)C 19961264 +Cc1c(Cl)ccc(Cl)c1Cl 394194 +Cc1ccc(C)c(C(=O)C(C)(C)C)c1 2514237 +CCCCNc1cc(C)cc(C)c1 19953037 +CCOc1ccc(-c2ccccc2)cc1 1648198 +FC(F)Sc1ccc(Cl)cc1 2572377 +Cc1cc(Cl)cc(S)c1Cl 263623791 +CC(C)c1ccc(C(C)C)cc1 1736679 +Clc1cc(Cl)c2cccnc2c1 38537202 +CC(C)c1ccccc1-n1cccc1 1519670 +Clc1cc2cccnc2cc1Cl 16677935 +CC(C)c1ccc(C(C)C)c(O)c1 2165950 +c1csc(Sc2cccs2)c1 161195 +CCCC[C@H](CC)C[C@@H]1CCCCN1 15442172 +CC(C)(C)c1c(N)ccc2ccccc12 77011615 +CC(C)C(C)(C)[C@H]1C[C@@H](C)CCC1=O 2077882 +Clc1ccc(SC2CC2)cc1 39309991 +CC[C@@H](C)NC1CCC(C(C)C)CC1 54554362 +Oc1cccc(/C=C/c2ccccc2)c1 2566001 +Cc1ccc(NC2CCCC2)c(C)c1 19877361 +Cc1ccccc1N[C@@H](C)CC(C)C 19964977 +Cc1ccc(C)c(NCC(C)(C)C)c1 41051141 +CC(C)Cc1c[nH]c2cccc(F)c12 98213774 +CCCCNc1ccc(C)cc1C 19877522 +Cc1ccc(NCC(C)(C)C)c(C)c1 41051066 +Cc1cc(Cl)cc(OCC(C)C)c1 261493767 +CC[C@H](C)N[C@H]1C[C@H](C)CC(C)(C)C1 35708345 +Cc1cnc2c(C)cccc2c1Cl 72236936 +C[Si](C)(C)[C@H]1CC2CCC1CC2 169828466 +CCC(C)(C)[C@@H]1CC=CCC1 2162337 +Cc1cc(C)cc(-c2ccccc2)c1 44404105 +c1ccc2cc(-n3cccc3)ccc2c1 4677763 +CC(C)(C)OCc1ccccc1Cl 200127984 +C1CC[C@H]2C(=C3[C@@H]4CCCC[C@@H]34)[C@H]2C1 114638701 +C[C@H]1CC[C@H](C2CCC(=O)CC2)CC1 263584382 +CC(C)=CC/C(C)=C/N1CCCCC1 102315016 +N#[N+]c1ccc2c(c1)Cc1ccccc1-2 4797499 +Clc1ccc(C2CCCC2)cc1 75840536 +Cc1ccc(Cc2ccccc2C)cc1 71787091 +c1ccc(C2CCCCC2)cc1 1672037 +CC=C1[C@H]2CC[C@H]1c1ccccc12 101037513 +Cc1ccc(NCC(C)(C)C)cc1F 41051126 +CCCC/C=C\CCCC 72099247 +Clc1cccc2c(Cl)nccc12 1692678 +Clc1cccc2ccsc12 3199655 +CC[C@H](C)[C@@H](C)Nc1ccccc1C 20550984 +Cc1ccc([C@H](F)C(F)(F)F)cc1 78206535 +CCC(CC)Nc1ccccc1C 19964998 +Cc1ccc(C)n1C1CCCCC1 3268273 +Cc1cccc(Cl)c1C(F)(F)F 63361048 +CCC(C)(C)C(=O)c1cccc(C)c1 2514175 +CC[C@@H](Nc1ccccc1C)C(C)C 37216373 +Fc1cc(Cl)c(Cl)cc1Cl 71617576 +CCCC[C@@H](C)CCC 2038773 +CC(C)CCN[C@H](C)C1CCCCC1 37199833 +Cc1cc2cc(C(C)C)ccc2[nH]1 71494922 +CC1=CCCC(C)(C)[C@@]12CC[C@@H](C)O2 1850503 +Cc1ccccc1N[C@H](C)CC(C)C 19964976 +CC(C)[C@@H](C)Nc1cccc(Cl)c1 19920400 +CC(C)(C)c1ccc(Cl)cc1 55161645 +CSC(C)(C)CC(C)(C)C 2141668 +CC(C)c1ccc(O)c(C(C)C)c1 2033144 +Cc1ccc2nc(Cl)c(C)cc2c1 19721535 +CC(C)CCC[C@@H](C)NC1CCCC1 7989131 +CC(C)C[C@H](C)Nc1ccc(F)cc1 19942109 +Sc1ccccc1-c1ccccc1 4269985 +CC[C@H]1CCCCCCCCC1=O 45027654 +CC1=CC([Si](C)(C)C)=CCC1 169828536 +CC(C)CC(=O)C[C@H](C)CC(C)C 1693233 +Fc1ccc(-c2cccs2)cc1 8764139 +CC(C)C[C@@H](C)Nc1cccc(F)c1 19920681 +CCCCCCc1ccccc1 1760823 +Cc1cc(C)c2[nH]c3ccccc3c2c1 6069886 +CC1=C[C@]23C=C[C@]2(C=C1)CCCC3 101047409 +C=Cc1ccc(OC(C)(C)C)cc1 2516976 +CC[C@@H]1CCCCCCCCC1=O 45027653 +Cc1ccc(Oc2ccccc2)cc1 1736034 +C[Si](C)(C)[C@@H]1CC2CCC1CC2 169828464 +CC1(C)c2ccccc2-c2ccccc21 1038917 +CCCC(=O)c1ccc(Cl)c(C)c1 31893209 +Cc1c(Cl)cccc1C(F)(F)F 66330469 +ClC1(Cl)[C@H]2CCC=CCC[C@H]21 95892815 +CC[C@@H](C)Nc1cccc(C)c1C 19772374 +CCC(CC)c1nc2ccccc2o1 1855250 +Cc1ccc(C)c(Cc2ccccc2)c1 66325005 +Cc1ccc2c(c1)Cc1ccccc1-2 1580786 +CC[C@H](C)[C@@H](C)NC1CCCCCC1 20003462 +CC(=O)CC[C@@H]1C(C)=CCCC1(C)C 2010911 +CC(C)c1cccc(C(C)C)c1 2041162 +CCC/C=C/CCCC 2166902 +Cc1ccc(F)c(N[C@@H](C)C(C)C)c1 20471729 +CCC(F)(F)c1cccc(Cl)c1 77011864 +CC[C@H](C)C(=O)[C@H]1CCCC[C@@H]1CC 82658274 +CCC(CC)C(CC)CC 2564041 +CC[C@H](C)Nc1cccc(SC)c1 19900677 +CCCNc1ccc(C(C)C)cc1 19912484 +Cc1ccccc1NC1CCCCC1 19965109 +Clc1cccc(NC2CCCC2)c1 19920306 +CC(=O)CC[C@H]1[C@@H](C)CCCC1(C)C 2507854 +CCCCc1ccc(-n2cccc2)cc1 4237712 +Cc1ccc2nc(C)cc(Cl)c2c1 320757 +Cc1cccc(C)c1C(=O)C(C)(C)C 2514238 +CCCCNc1cc(C)ccc1C 19964669 +CC(C)=C1[C@H]2C=C[C@H]1c1ccccc12 100244500 +COc1ccc2cc(Cl)ccc2c1 33605573 +CCCCNC1CCC(C(C)C)CC1 54554380 +C[C@H]1CCC[C@@H](c2ccccc2)C1 1601473 +Cc1cccc2c1Cc1c(C)cccc1-2 3130625 +CC(C)=C1C=CC(C(C)(C)C)=C1 80223459 +Cc1ccc(C)c2sc(Cl)nc12 2455717 +Cc1ccc(N[C@H](C)C(C)C)c(F)c1 20371835 +CCc1cc(C)cc(C(C)(C)C)c1 1689942 +CC(C)CCC[C@@H](C)CCCCO 2106140 +CCCC[C@@H](CC)C[C@H]1CCCCN1 15442174 +CCCCC1CCCCC1 1586760 +Cc1cccc2c1Cc1ccccc1-2 1724367 +Clc1ccc(Cl)c2ncccc12 38950985 +CC(C)C[C@H](C)Nc1ccccc1 19879323 +CCCCCN[C@@H](C)c1ccccc1 19901305 +CC[C@H](C)SCc1ccccc1 95147711 +CSc1cccc(Cl)c1Cl 2511087 +Clc1cc(Cl)cc(Cl)c1 391973 +CNc1ccccc1Oc1ccccc1 20041614 +C1CCC([C@H]2CCCSC2)CC1 22015966 +CCC[C@H](C)N[C@@H]1CCC[C@H](C)[C@@H]1C 37021713 +C=CC1=C[C@]2(C(C)C)C[C@H]2C1(C)C 101142308 +Clc1cccc2ccnc(Cl)c12 71482439 +CCc1ccc2[nH]c3ccccc3c2c1 2509970 +CC(C)(C)c1cccc(C(C)(C)C)n1 1716337 +CCC[C@H](C)N[C@@H]1CCC[C@@H](C)[C@@H]1C 19963362 +CC[C@H](C)CCCC(C)C 2168667 +Cc1ccccc1N(C(C)C)C(C)C 68677290 +CC(C)CCC[C@H](C)NCCC(C)C 2040425 +CCc1cccc(NCC(C)(C)C)c1 41051133 +Cc1cccc(C(=O)C(C)(C)C)c1C 2514236 +CC(C)C[C@H](C)N[C@@H]1CCCC[C@@H]1C 19945266 +CC(C)N[C@H]1CCCC[C@@H]1C(C)(C)C 19880408 +Cc1cccc(SC(F)(F)F)c1 34577064 +CC1=CC[C@@]23C=C[C@@]2(CCCC3)C1 71785750 +Clc1ccc2cccc(Cl)c2n1 39759 +CC(C)=Cc1ccc(Cl)cc1 2162084 +Cc1c(F)cccc1N[C@H](C)C(C)C 43319549 +CC1=C[C@@]23C=CC[C@]2(CCCC3)C1 104293697 +CC1=C[C@H](C)[C@](C)(CC[C@H](C)O)CC1 4027259 +CCCP(CCC)c1ccccc1 1606008 +CC(C)c1ccc(C(F)(F)F)cc1 71791913 +Cc1c(F)cccc1NCC(C)(C)C 43320439 +CC1=C[C@@]23C=CC[C@@]2(CCCC3)C1 104293690 +C=C/C=C/C=C/CCCCC 2560655 +CCCCCCNc1ccccc1 1813957 +CCCCNc1ccc(C)c(C)c1 19920251 +CC1=C[C@@H]2[C@H]1[C@H]1C=C[C@H]2c2ccccc21 253592452 +Cc1cc(Cl)c2c(C)cccc2n1 104676185 +CC1(C)CCC=C(c2ccoc2)C1 725246375 +c1csc(-c2cc3ccccn3c2)c1 388868 +Cc1ccc(C(C)(C)C)cc1S 19991255 +C1=C/CCCCCCCC/1 57988190 +CC1=C(C)CC2=C(CC=CC2)C1 71782938 +c1ccc(-c2cc3ccccn3c2)cc1 1023862 +CC[C@H](C)Nc1cc(C)ccc1C 5140080 +CCC[C@H](C)Nc1cccc(F)c1C 43319715 +CC[C@@H](C)Nc1ccccc1Cl 19772254 +CC(C)C[C@@H]1CCC[C@@H](CC(C)C)N1 149896246 +CCCCCC(=O)[C@@H](C)CCC 2167280 +C/C(=C\Cl)COc1ccc(C)cc1 16293552 +CC[C@@H](Nc1cccc(C)c1)C(C)C 37185354 +[C-]#[N+]c1ccccc1[C@H](C)CC 100000332 +Cc1cc(C)c2nccc(Cl)c2c1 2575232 +C=CC1=CC[C@@H](C(C)(C)C)CC1 71786919 +C=Cc1ccccc1-c1ccccc1 111461523 +C1CCC(PC2CCCC2)C1 2562320 +CCO[C@]1(C(C)C)CC=C(C)CC1 5861451 +Cc1cc(Cl)cc(C)c1OC(C)C 39642465 +CCc1ccc(C)c2ccc(C)c-2c1 968466 +CSc1ccc(NC(C)(C)C)cc1 238417221 +Cc1cccc(-c2ccccc2)c1C 2012236 +CCCc1cccc(CCC)c1 3107134 +CC[C@H](C)Nc1cc(C)cc(C)c1 19772356 +[C-]#[N+]c1ccccc1[C@@H](C)CC 100000333 +CC(C)C[C@@H](C)Nc1ccccc1 19879322 +C1CCCC(NC2CCCCC2)CC1 19878872 +C/C=C/C12CC3CC(CC(C3)C1)C2 63767556 +CC(C)(C)SCc1ccccc1 79129312 +Nc1ccc(N=Nc2ccccc2)cc1 98089001 +CCc1cccc(NC2CCCC2)c1 19962087 +[C-]#[N+]c1cccc(Cl)c1Cl 95698204 +CCCCCCNc1cccc(C)c1 2275118 +CC(C)c1cnc(Cl)cc1Cl 238173856 +ClC1(Cl)[C@@H]2CCC=CCC[C@H]21 104281828 +C1=CC(=C2C3CC4CC(C3)CC2C4)C=C1 49952419 +Cc1ccc(-c2ccc(F)cc2)cc1 59975230 +Cc1ccc(-c2ccccc2C)cc1 78637493 +CNc1cccc(C2CCCCC2)c1 259121872 +C1=CC2(CCCCC2)c2ccccc2N1 504491563 +c1ccc2c(c1)C1CC2c2ccccc21 71785924 +CCc1ccc(N[C@@H](C)C(C)C)cc1 19955354 +CCCOc1ccc(C(C)(C)C)cc1 26892037 +c1ccc2c(c1)[C@H]1CC[C@H]2[C@@H]2CC[C@H]12 216350439 +CC(=O)CC[C@H]1C(C)=CCCC1(C)C 2010910 +Cc1cccc(C2=CCCCC2)c1 1674683 +Fc1ccc2c(c1)oc1ccccc12 4798629 +CC(C)[C@H](C)N[C@H](C)C1CCCCC1 37249692 +CCc1cccc(-c2ccccc2)c1 1674255 +CC(=O)c1ccccc1-c1ccccc1 2515950 +OCCCCCCCCCC1CC1 34303607 +Cc1ccc2c(c1)[nH]c1cc(C)ccc12 5747656 +CC[C@H](C)N[C@@H](C)c1ccc(C)s1 19882848 +c1ccc(Sc2ccccc2)cc1 1679972 +CCc1ccc2cc(CC)ccc2c1 2583617 +CNc1ccc(Oc2ccccc2)cc1 15446555 +CCNc1ccccc1-c1ccccc1 19901719 +CC1(C)CCc2[nH]c3ccccc3c2C1 83728783 +CCCCCC(O)CCCCC 1605992 +CC1=C[C@]23C=C[C@@]2(C=C1)CCCC3 71785762 +CCc1ccc2nc(Cl)sc2c1 2455698 +FC(F)c1cccc2ccccc12 66323193 +Cc1ccc(C)c(-c2ccccc2O)c1 60159487 +CCCC(F)(F)c1ccc(F)cc1 66329366 +CC(C)c1cccc2ccccc12 1726216 +C1=C[C@]23CCCC[C@]2(CC1)CC3 104282285 +CC(C)(C)C(=O)CCC1CCCC1 42375089 +CCC[C@H](C)Nc1ccc(C)c(C)c1 19920264 +FC(F)c1cccc(C(F)(F)F)c1 66323376 +C[C@@H]1CCCC[C@H]1Nc1ccccc1 7277022 +Cc1cc(Cl)c(C)cc1Cl 404355 +O=C1CCCCCCCCCCC1 3860296 +O=C(c1ccsc1)C1CCCCC1 34327720 +CC(C)c1ccccc1Br 2029638 +Cc1cccc(Oc2ccccc2)c1 2025594 +CC[C@H](N[C@H]1CCCC[C@@H]1C)C(C)C 37211447 +CCCNc1cccc2ccccc12 1598351 +Clc1ccc2nccc(Cl)c2c1 529910 +Cc1ccc(C2CCCC2)cc1 57606821 +CC(C)=C/C=C/c1ccccc1 3075451 +CCCCCc1cccc(F)c1 91301297 +FC(F)c1cc(Cl)cc(Cl)c1 112731864 +CCC[C@H](C)Nc1ccc(F)c(C)c1 61117397 +C1=C[C@H]([C@H]2C=CCCC2)CCC1 71785528 +CCC(=O)[C@H]1C[C@@H](C(C)C)CC=C1C 1589954 +Fc1ccc(-c2ccccc2)c(F)c1 2584348 +CCCP(=O)(CCC)CCC 1756222 +CCC(CC)N[C@@H](C)c1ccccc1 2389437 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+CNS(=O)(=O)CC(=O)N1CCCC[C@@H]1C(N)=O 408405371 diff --git a/uci-pharmsci/lectures/library_searching/Library_lingo_lecture.key b/uci-pharmsci/lectures/library_searching/Library_lingo_lecture.key index 7560ccb..0898552 100644 --- a/uci-pharmsci/lectures/library_searching/Library_lingo_lecture.key +++ b/uci-pharmsci/lectures/library_searching/Library_lingo_lecture.key @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:6f898f51284c0b32171e1cfd8900ba2c9bdff95e8d513c4fae892357abf001fd -size 4237908 +oid sha256:4449f982967e823cce094705be0efb21433d7f9461d0f54fc3fefa3fc687d140 +size 3773005 diff --git a/uci-pharmsci/lectures/library_searching/Library_lingo_lecture.pdf b/uci-pharmsci/lectures/library_searching/Library_lingo_lecture.pdf index c2f8e19..cb973c4 100644 Binary files a/uci-pharmsci/lectures/library_searching/Library_lingo_lecture.pdf and b/uci-pharmsci/lectures/library_searching/Library_lingo_lecture.pdf differ diff --git a/uci-pharmsci/lectures/library_searching/Library_searching_and_lingo_sandbox.ipynb b/uci-pharmsci/lectures/library_searching/Library_searching_and_lingo_sandbox.ipynb index cdb95de..9d17e59 100644 --- a/uci-pharmsci/lectures/library_searching/Library_searching_and_lingo_sandbox.ipynb +++ b/uci-pharmsci/lectures/library_searching/Library_searching_and_lingo_sandbox.ipynb @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -121,7 +121,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Molecule 1 is 1-cyclopropyl-6-fluoro-4-oxo-7-piperazin-1-yl-quinoline-3-carboxylic acid and has smiles c1c2c(cc(c1F)N3CCNCC3)n(cc(c2=O)C(=O)O)C4CC4\n", + "Molecule 1 is 1,3,7-trimethylpurine-2,6-dione and has smiles Cn1cnc2c1c(=O)n(c(=O)n2C)C\n", "Created molecule 2 from SMILES. It's called cyclohexane\n" ] } @@ -133,8 +133,8 @@ "#Create a new empty molecule\n", "mol = OEMol()\n", "#Parse this IUPAC name into the molecule\n", - "OEParseIUPACName(mol, '1-cyclopropyl-6-fluoro-4-oxo-7-piperazin-1-yl-quinoline-3-carboxylic acid')\n", - "#OEParseIUPACName(mol, 'ibuprofen')\n", + "#OEParseIUPACName(mol, '1-cyclopropyl-6-fluoro-4-oxo-7-piperazin-1-yl-quinoline-3-carboxylic acid')\n", + "OEParseIUPACName(mol, 'caffeine')\n", "\n", "#Create an IUPAC name AND canonical SMILES string for this molecule\n", "name = OECreateIUPACName(mol)\n", @@ -161,7 +161,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -187,7 +187,7 @@ "True" ] }, - "execution_count": 2, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -234,12 +234,12 @@ "outputs": [ { "data": { - "image/png": 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\n", 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NyemSplrxFLZr165kWNIpcosWLYpz5sxJk0wzMvMAsbxXL7yYPbuHeGz52AKyJQwm/LIEsXf7JNJVJeJ2aYY4f2owuW2ZRghokVrIkZJs7UZeTfzkk0+QNtpNq9HatWuxbt26GrnvuOMO3Lhxo2nyQxG0dOlSZYpMh8cU8iVQvfD330MRkZSWMdqzHTE+9Zo4KZEzroSAEdbT+fPnceDAgRgbG6sQg46U4dChQ/G///7TrQntAypWbPbs2brpUkZyvmm0ripQoIBSHjt2nn/+eTx79mzKpGH5ztPo++67TxsEmt9yC/7vtdcCl8X1e++94K1jYIm2TCEEjJBaePr1wQcfYP78+ZXOyNPDzp07I3sxA4Wff/4Za9eurRGIthoCZUkVf+7cOXzhhReQHTvsLc2XL5/pFte7UJ4GswNJLU/Pk+udDxMSECfTei5vXs80lWS4OQgBI6Dd7777DitVqqRZAZ4K/vXXXwFL3rVrF7Zt21bLdwtZj3nz5gXMp5eA9hPxzjvv1GTedtttNBM0OBX0UZDqyWXCMdFD8eTS/gl5M8t7iMdTVaon7t7toxT33BIChlGXmzZtwrvvvlvr7GXKlMGFCxcGLPHff//FPn36YKZMmZS82clp8cYbbyA9/RAwb7AJaEMfixUrpshna/zUU0/h8ePHg83uMx3LpMMCWnt56hmUJ3fDBsRm5Ey5uT7EcuUQv/nGZxluuykEDINGjx07ht26dUN6d4PSGXPnzo0TJkxAOr+pWxrHv/POO5gnTx4lH+dnbybLC0e4dOkS7QYM0dajbLUmTpxIs0CaBoYQ2II2bNhQIx7vR7LTJVA4RvuUm9kZQ+1UyEftpn0UJKACZXVNvBDQRFWyhRo5ciSyxeLpF1uwF198Ec+cOROwlEWLFmHZsmW1Tty8efOIeSx30zSvdevWWtmVK1fG5cuXB6wzHYfDRx99VNvuKEgb6R9//HFATy7jxBa9BzmimHiJ5BjCvn0RyfJHWxACmqBx9jR++umn2pSOyffggw8GdRJl3bp12KRJE63zV6xYERcvXmxCrUIXwWtVniZz/fnToUMHPHjwYCpB9EgU7ZsPpn30zEo69uS+8sorQXly2YvLa1mWTz8sgdNo3/Mk7YVGaxACplHzv/zyC7IjQ+20tWrVQj4WFigcPnwYu3TpolkP9kq+//77IU//ApUTajx7WEePHo10yFtpEz19jyNGjNDOodIjUdqWBq8dH3/8cZ8kTVmutyeXsWKvLt+L9iAENNgDeNrWrl07jXh82mTmzJnI1lAv8LGwV199Fbljc0fk/cABAwYgbxPYKaQ85H3rrbfilClTtPY2btwYeaM/UGBPrjdObP3YCgbCKZBct8QLAUPUJB+M7kvrFT5nyQRiSzF8+HBkh4Ze4BMuvCFeuHBhrRPTc3y4d+9evWyWx6U85F2OPJRsqQMF9uS+9NJLyXAy25MbqA5OiBcChqAlJp/6iBB7KJ9++mk8evRoQAk//fQTVq9eXSNevXr18H//+1/AfHZJwIcIJtPmuPch74ceesjnaRr25PKBcG9PbrA42aW9kayHEDAEtHfs2KGQiJ0OG3jvKkCgB2qTeRdLlChhi0PRAartNzrlIW92NHmHb2jvji2kuh6mp/WDwslbRrRdCwFD0PiePXuUzsUPveoFtpS96NS/egyLn+8bM2YM0k8K6mVzTBxbNCZZv379lDqvX78+2ema8nSaxXEPBVuEvvwwL/WkYAPpSElK3j+/WcgyAh31AnKqAE1XoUePHkBrRKDD0H7zOC1CbQs5kIBO0AA5VehXJG5A3rx54fXXX1faTIOP05plSX0FpRBgD4aATDx6sgHoiXR4++23gc6AhlCCM5Kqv01KhweAf7eGHFLQu3dvoKc6gNaJzmiETWopBAxBETzKcyAHjN9cTZs2BfKIAlsHtwaVgPXr1wc6RAC0jQK0TeHW5oa1XULAEOANxgKyODeTj9unEpA8u0BH7fiWBIMI+B/KDQp0c7ZgCehmDLhttC2hNJHXuBLShoAQMAT81CmonhMmBHGOTapaQHG0pF2FQsAQMFQtoN4aMARxjk2qElAsYNpVKAQMAUOxgB6whIAhdJoASYWAAQDyjlYtYLRPQWUN6N0r0nYtBAwBP5WAMgW9rqAma8AQOo+fpEJAP8D4ui1TUA8qMgX11TuM3RMChoCbagGjfQoqBAyh0wRIKgQMAJB3tErAaJ+CyhrQu1ek7VoIGAJ+MgX1gKVaQFkDhtB5/CQVAvoBxtdt1QLKFNTjhJF9QF+9JLR7QsAQ8FItIB3HDiGX+5KqFlAImHbdRndPChG/I0c8GfbujW7YZA0YYsfRSS5PQ+iAkzIqV64qdKsVxMU9kDIqqr6rFlDWgGlXuxAwBAxjY+mnZGExFC4cQiYXJlUJKFPQtCs3uudSIeJ38xcpQOcXKUKU6MzkQkDz9CYEDAFLIaAHLFkDhtBpAiQVAgYAyDv65i9S0E9SeN+NvmvVAsoaMO26j/KuFBqAYgE9eKkElDVgaP3HV2ohoC9U/NwTC+gBRgjop4MYuC0EDAE0sYAesGQNGEKnCZBUCBgAIO9oIaAHDdUCyhrQu3cYuxYChoBbtmyexDlzhpDJhUlVAsoaMO3KlY34EDBs2BDg228BatcOIZMLkwoBzVOqEDBELNu0CTGDC5PLGtA8pcoUNACW5ejYZ9b6AOu3p0743a8AMXUAvlqeOs7Nd1QLKGvAtGtZCBgAwxv0QqQE+iHo7iPoF6E9j8FpOdgpo360m1FwoRJQ1oBpV3bIBKRXD8MDDzwAX375ZdpLd5CETTsB3p7loAqHsapCQPPADZmA/AoqevkifP/99+bVwuaSatMbxsoWBxg5FWDHfptXNg3V4weOZ8yYAU888QTwQOsvyBrQHzKh3w+JgFu2bIGPP/4YeO4/aNAg5aWM7777Lvz999+hl+ygHJljAD5+FeBaAsAzNBVV9wMd1ISAVV25ciXcdtttQG+/VV64uWnTJr95VAsoa0C/EAUdERIBX3rpJeXVVM8995zy4smPPvoIXnjhBRg2bFjQBTo1YeOatA5sB7B6I8CH853aitT13rVrF9C73qFZs2bKQFq8eHGYM2cO8HsO/QWVgLIG9IdQCPfph4aCCt98843yXvA8efLgmTNn8OzZs5gvXz7l3ldffaXIIAup3A9KoEMSlW6D2KSrp7LnLyKWuBcx1+2IB44h/t/PiBlrI85f5pDGeFWTpphIAypmypRJ0WH27Nlx5MiRAd9jn5CQgEQ8JQ+9CdhLolwaQYCmU4FDfHw8li1bVgF98uTJSgZWHvEcaaRUvv/2229Iv5eJbdpQj3VR8CYgN0slXeveiN86kIDXrl3DSZMmIQ+krD/WWdeuXfHYMRpRAoTvvvsOK1SooOTjvOXKlcPFixcHyCXReggERUB617kCesWKFZFHwB07digjJytvw4YNSIt3rF27tpJm+PDheuU5Li4lAbkBjw32WL5HBjrLApLzTCENk4c/d911F27cuDGgTjZv3ox33323Rrz8+fMjf1Q5HMezHwmhIxCQgCdPnsS4uDgF7B9++EEpoXXr1sr37t27K9+nT5+ufC9WrBjS+9HxyJEjWLRoUXz22WdDr5HNcvgi4IkziAXu9JDPCVNQHiRpjacRpnz58shkDBROnDih6FCdcubOnRvHjx+PbEX5M3HiROR7TERO07NnTzx16lQgsRLvhUBAAvbu3VsBuFWrVkq2H3/8UfmeM2dOZAXxOqBQoULKvc8//1xJ06VLF+X7448/7lWU/S937kR8/nnE339PqqsvAnLsrP9LTUBeEg0YgEgQ2SLwtJKnlzxTYZLkzZsXeQnBsxi9cOXKFXzzzTcxR44cSj7ydiL3A177pwx873kCjdNwGTxYM0l52SIhMAIBCTiAehSPcjztZMVVqlRJAfqtt95SpA8ePFj53qBBA+X72rVrkX45GmNjY3Hv3r2KImhfCT/44IPAtbEoxYULiC+/jBgT4znX4m24y9CStmk33xW7t5eHhAt+8sQz8agPKp8HH0Rqv+984b57+fJlHDFiBLJjhUkRQw3jNTs7zgIFOmCBJUqUUPJxXp7t/PPPP4Gy4bZt27Bly5ZaPvYZsONOgj4CAQnonf13Mg2slDJlyijEYoIx0Zhwf/zxh5KUichpaJ9Q+c5E5e+3306uQ5sFWrri7NmIhQt7SEOGgiwG0khvrKIsj5bLZDk88jJnRhw2DJH4EJHAa/HPPvsMeSnAmPOnbdu2SFsNActfs2YNqrrjfNWrV8fly5cHzJcywZIlS5B9BWr5PPUNZp2ZUk60fA+JgHQCAt9//33cunWrgk+nTp0UoHnKyeGLL75QvvOU9AKZFZ6i8lSVlaGuH7mDsMfU6vDnn0gdzkMUqp5yzffMCEePIhI01G7PhwwK3typMUO8TxmMaZ06dRSsGe9atWrhqlWrfKb1vrl//3589NFHtXysu08++QRpr887WUjXPFOiAxrKlJfrwlPgZ555RukPIQmKgsQhETAlHv3798dSpUopThee9qgjLztlODDorAB1/cgk5O/VqlVLKSpi32lMUKwcWzuqimL92Aqy9TI78DhTs2YSEZs3R5qqmVsKz0I6dOigEahIkSI4c+ZMxTOtVxIPkEOGDMHMZKZZJ1myZMFXXnlFWdPr5QsljvcaX3zxRW2vkQfjcePGKbOnUOS4OW2aCOgNDE9XWJG8HcGjJ3veeOTjjV5/60cefSPlNeNRuXv3r8lZcFEhHq/3eN0X7r1kNiRTpiDtu3mImD59IjZpshTPnz/vDV/I1+fOnaP6v6wsARj3rFmz4muvvaZ4ofWEsW7YwhUsWFDRFy8fHnvsMTx48KBetjTF8RqSB2GuJ39uvfVWXLhwYZpkuiWzaQTk6emMGTPwwIEDCjbqVgUv/jnwlITB914/cqepUaOGEh/OP0uXLr25LuH9TJ5unkH2eEYynD6N5NLnh5uu02e/4jmeNWtWQEvlr46MG+PJBOIlwOHDh/0l1e4vW7YMq1atquTjvI0aNdLW7lqiMF6wHqpUqaKV36RJE6RzxGEs0f6iTSNgyqb269dPsYbseeOpiHryQvWMtWvXTlHEk08+mTKrad95ekbnHDWFlylTET/6aJVp8o0I+uab7YSLp+1MgoYNG+K6detCFkXncRWv419//RUw7/bt25NZIF42zJ07N2C+cCTggZo94uoxRp4lBXsSJxz1sVpm2Ajo3TDe9OXO1pwXQRRWrlypfM+WLZuyfuTNex7R6TlD72yGr1kePTalrW94P2vs2LG2WXuwt3I2LTzV/VPuhD169MDTbCZNDCzPe4+O12C8v3f16lUTSzEmiqfQffv2VbZIuG+wjsaMGWOLuhlrkbFcESEgj3rz5s3T1nuNGzdWCMiHfznw2kW1BsaakZSL97FUZxBPzzp37oxH2S1pw8COEJ4pqAeieZbA1iEtHkhuJm+C82Z4rly5FFz5lAoTnE812S3wFgkPvKx//pQsWVLpK3arZ7jqExECpqw8d7p77rlHOXl/6NAhxYHA4P/6669KUnYujBo1KmU23e/s9Lnjjjs0RdKzbbh69WrdPHaJ5Cmi91lLng0Y3arhJ1NKly6t4dCiRQtHnNNkJx57x1Ui8iAdzPTaLjo0Wg9LCOhdWbZYDPojjzyi3OYOxN/puTTvZH6v+SgUr4fU84oFChTAqVOnptmK+C0wjBHcdrYAaifkfdZgrTd3Vu8BqHLlysib4k4KbPnpGVNkHTIGPINhH0GwGDiprWpdLScgg86PuajPlqlesinsu6fAe4fsPUsZ1EMBqnOHzyL26dMHeW3h5MD7qTwlV/fneG3Ee2d8+NlXYO8ne0G5s3Kn5acUeBrL+Dg18BaN9xYL+wr4aB1j47ZgOQFTAspnT/lkBncgPl/IxOK1jHfgEx7e0xWevnFaN4V9+/Yl8+DyEwx8EF4NFy9eVIjKWzlMPD4SyJ02rfuLqnw7/N+zZw+q3nJuI8+K6Gl9O1TNtDrYjoDeLZtB+4oMPHvyOPA67/7771fu8X12py9atMg7i+uumXTeD8Hytgp7MvnEC2PAHz4Jw1subg084NakI0Vqe3nrRj177PQ225qAPD39+eefte0D9ZEXHvV5SsKPzURD4OknH2pXHw9SO2LdunUNO2uchhv3hWnTpmlbN4wBHzR3erA1AVOCy/tlDDxPz6IxsDNCfcSIPcm8nxhtgbdu2rdvr/QDfuTJ6SGkX0Wjzm9pIE+nUj49bW9pPawqvHDhwkCPCSnF0+gP5HixqiqWlUuzACDvsFI+PZtqWT3MKthRBDSr0SLH2QiQ1VMa4IYBSAjosL7ops5nFHo3YSAENNoLJJ9lCAgBLYNeCnZT59PTJjnagH7aAujZxVTJ3ISBWMBU6pUbdkCAHtECepAX6BRUquoIAVNBIjcihYCbOp8eZnrt1IvTk2nHOLGAdtSK1In3pxUUfHk69eKcBp0Q0GEac1Pn04Ner516cXoy7RgnBLSjVqROYgGlD9gTATeN/noI67VTL05Pph3jxALaUStSJ7GA0gfsiYCbRn89hPXaqRenJ9OOcWIB7agVqZNYQOkD9kTATaO/HsJ67dSL05NpxzixgHbUitRJLKD0AXsi4KbRXw9hvXbSg8hKVnpAW0+EI+Kc3wJHwCyVDBUBPQLqxYVajtXphYBWayDE8t3U+fSarmfl3ISBEFCvF0icZQjokUwvzrIKGyxYCGgQOKuyuanz6WGo1069OD2ZdowTAtpRK1In8YJKH7AnAm4a/fUQ1munXpyeTDvGiQW0o1akTmIBpQ/YEwE3jf56COu1Uy9OT6Yd48QC2lErUiexgNIH7ImAm0Z/PYT12qkXpyfTjnFiAe2oFamTWEDpA/ZEwE2jvx7Ceu3Ui9OTacc4sYB21IrUSSyg9AF7IuCm0T8YhH098eAmDMQCBtMLJE3EEaDXjkPTpk2BXlGdqmw3ETBjqtbJDVsjEEznW7FiBeTOnRvotc62bote5ei13LBy5UqfSfSelPCZwcY3xQLaWDlGqkZvkIXmzZtD7dq14amnnoITJ04YEWPrPMEMQrZugFflhIBeYDjhMlDny5kzJwwbNgxiYmJg5syZQK9xhrfffhsSEhKc0Lyg6hgIg6CE2CSRowioAm8T7CypxsWLFwOW+/rrr8PWrVuhTZs28N9//8GAAQOgSpUq8P333wfM64QEaj/w9d4IJ9Q/WR2pMY4JBDi/sQOpY+GuXbscU28zKnr69Gns2bOn0n7GoFq1arh+/fqAon/88Uek9ZSWr1WrVrhz586A+eyaYO/evViiRAmlPfnz53d0Wxhj3m9xTOjSpQtmzpxZAZ+mWNi/f388f/68Y+pvpKI0dcRJkyYhOVWUdpNbHjNlyqRdP/PMM3jq1Cld0deuXcMJEyZgXFychh1ZRaT1om4+O0WyngcOHIixsbFKG3gQ4g/3g5dfftlRbfHG1VEE5IofP34cn376aeSOyAooUKAAfvzxx3j9+nXvdrni+ocffkB6S6zW4e655x6kqaUy6PTt21cjYq5cuXDy5MnIZNUL5JDBrl27atgVKlQIaZ2I5FXUy2ZpHOuV9ct6Zn3zLKhz5864YcOGZG0pXLgwzp4929Zt8QWk4wioNoKnX7fffrvWOatXr47ktlajHf1/x44d2Lp1a61t5EjBb7/9NlWb6A2y2LJlSy1dpUqVcNmyZanSpbzx119/YcOGDbV8devWxd9//z1lMsu/L1++XJlqq9aucePG+OeffyarF7elQYMGWlvq16+fKk2yDDb74lgCqjjOmzcPS5YsqSmANm5xz549arSj/p87dw779eunWTbyaOJbb72F8fHxuu1gcpYpU0bDoG3btshrpUDhs88+w6JFiyr52LI88cQTeOzYsUDZwh7Pa9QHHnhAaw/rd+7cuX7LZQvO1o+toGol2dKzxbd7cDwBGeArV67gyJEjMXv27IoCeJ0waNAgx6wL1GkWOxW4A/H0ulu3biF1ICbpm2++iTly5NAweOWVV5C8prp9kOMHDx6sra04/7hx4wKSXleowcizZ8/iSy+9pA1AXJfRo0fj1atXg5JIHl9lPcjrQsaR17y89g00NQ9KeJgSuYKAKjZHjx5VRnHVW8prnGnTptl6fbhq1SqsUaOG0mG409xxxx1BeTfVNqf8zxjwGknFgC3cF198kTJZqu88a/C2OjztXbx4cap04bjBBHnvvfcwb968yQYgXu8bCWxB2dvLePKH19HsDbZjcBUBVYB5neC9xqlVqxb+8ssvarQt/u/btw/bt2+vdRJ2rfN02qywZs0arFOnjia/UaNGuG7duoDily5dmszxc++99yKvScMVlixZkqy8Zs2aKQ4WM8qjfU8sV66chgEPMHZbnriSgKry5syZg8WKFdMU0KFDB9y/f78abcl/nvLx1FDdTsmWLRuOGDFCmUabXSFeG02fPh0LFiyoYMBT2+7du+PJkyd1i2KLNHHiRGTvKlsQ3vYwe8uHvbneDiS2uF9//bVuvYxE8tScp9ShTs2NlGUkj6sJyIBcvnwZhw8fjlmzZsW+1JnoHAmeoE5IiyMjeBnPQ2RImDEDS2fMqHRqniJ26tQJDx8+bFxmkDl5D83bucPEeueddwKujZioTFh1y4eJrOcMCaY6fKCgV69emPEmDlyX8ePHI+9VhjOwc4mdTMrUPBYw+7Ds+Oqvr4azyKBku56AKgqHDh3CmeRup/fq0PED+hQpgrQJhrRxpCYJ33928derh4lUbi36NKS1Dk8RIx14KslTSu+1EU85AwWeuvIUlvMxYYwE9TCAalWZgHyyJ9AhAiNl6eXh7ZZKHSohrKP1IX0a72iMf1/+Wy9LWOOihoAaitzxiQwKCZmItE7C//1Pizb14sgRJI8I0rCbRPpZsyJDep2GsHOFp3wqEYNdGy1cuNCQM4Onlt7l8dSTp6BWBZ6a91vVD/NvIK8zkTD9uvT47IFn8XTC6YhXKfoIyBCz1fv0U6RNsCQiPvoo4sGD+go4d4bSBLHHSNsitC+CSOs7heh0fA6HDEEkN7ldAq+Nxo4dm2xtNITqGGjbIpT682kVdqqoRGdvJDtG7BLOJ57HvofoRNF6OtpHRMy9ITe+e/JdTLyRGLEqRicBVXgvXUIcNgwxSxYPUfj/b7+psZ7/iXS8693XEW8nspYjuPjTmKav/TshblqbPC1/4z2r8uWTiP3QQ4jk8bRrSLY2ohlBEZqa8wZ9WgJvH3ivHXl74d133w245kxLmWnJu/3Kdmyxq4U2La2yrQquuLAiLSKDzhvdBFRhOnAA8ZFHkE72Inqvia7RCZQONF1l0jUrhfjWQMT330Ds1ASxYkbE22g9tHOzKsXzn60fndzAmjWRzsYlj7Pxtz/++AP5SJpqrXgbh495hRJ4w3zMmDGaVWXvaZ8+fZA32J0Qvjn3DZbeUlojYvs97fFUgueg+7ar2zDdxnR+PyW3k84NBCGgN2iJKaYen4z1kO+5B8idmsJr+g1NYVVreMrYhrF30Xa45rURH87mAwxMxFBO5KQ8Enj//fc78lGhqzeu4qhjozDb39kUIo45NkZRzfrL6xE2AubZkgfb7GuT6tPzcE9DKhQC+oONp541cyDWyI548qjvVMN7eUjIZHRR4MeU+HGllEe6fG0V8KEHPiStWk5+TvGnn35yPBqHrx3G8SfG48kEz56pSsAO+zuY2jYhoD84D+z2kKtHG38pEH+l401sBUf09p/GwTEpj3Txg738iBQH3r/k5zPVI29ufiyM2xsuAsqvotHQ7TPs3+m5XbSkz2jlZsUanrjNf/pP4+AY2joA2rIAOi4GtJYDevwJaAsB6KkDoCc3gA7BAx18V+LIgwr8ezRuD9fhOsRjfKpmxqaLTXUvmBuO+k2YYBpkWpp06Tyi0mfwLzI2s/84F8XQ5j1s2bIF6NEohXDkOVXIR2dZYfv27UBPYUQF+VilC88vhMybM6f6XL5x2ZDGxQL6g63izd/UPHrAXwqAg3s8cdXq+U/jkhjyaAKdBwU60gd0lAzq1asH8+fPd0nrgm9Grgy5oGHWhskyZEyXEWLSxSS7F+wXIaA/pPIVBLilFMBvSwFOHAEoWDR1ygXTPPdq1E8d59I7tKentKxUKcImCsPd2e+GeSXmmdZymYLqQflYT4ArlwBe6QZw8ULylCsXA3z5EUD+wgD1myWPc/E32qpQWkdbFC5uZeSaJhZQD+uu/QEO7wP44gOAB8jh0rI9QO78AJvWAiz9CiAuD8CMZQB5C+hJcVUcOQSV9pD301XtsqoxQsBAyL/6LkCR4gCrvgOY+Q5AIv3CNE9P730YoNvLAGUrB5LgqnixgOaqUwgYCE+eanUf6PlcpqcJTx0DKFE2UC7Xxke7BYxNb2y7wV+HEAL6Q8bX/azZo5p8DIlqAaNtClozS036OXLP9NtX1zB6T1bSRpGL0nyqBRQnjDkdQAhoDo5RI0UlYLRZwHApWAgYLmRdKjdap6DhUqcQMFzIulSuagFlCmqOgoWA5uAYNVLEApqraiGguXi6XppYQHNVLAQ0F0/XSxMLaK6KhYDm4ul6aaoFFC+oOaoWApqDY9RIUS2gOGHMUbkQ0Bwco0aKWEBzVS1H0czF0/XSzp6l43gwC86coTOxEtKMgBAwzRBGl4Bdu/jpj7qwf//v0dXwMLVWpqBhAtatYmvWrKM07bbbPP/d2s5ItUsIGCmkXVIOvWlGaUmmTDo/VuWStkaiGULASKDsojJuPhAP8kC8OUoVApqDY9RIufmTMCA/CWOOyoWA5uAYNVLEApqraiGguXi6XpoQ0FwVCwHNxdP10mQKaq6KhYDm4ul6aWIBzVWxENBcPF0vTSVgBtmFMEXXchLGFBijR8j99wP8+ivAgw9GT5vD2dJ0dLjW/N9aC2eNRbYg4CIEZArqImVKU5yHgBDQeTqLaI3//gcgho591ugIcI1+lT9lePo1T3zK+/I9OASEgMHhFLWprtPLkHiRspVehTh2RmoYblCcLGJS4xLsHSFgsEhJOniTCLhtrwBhJgJCQDPRdLGsjvfQi6Gu0ztq3uD3Q7i4oRFumhAwwoA7tbj61QB60hvZ1m4BeO9Lp7bCfvUWAtpPJ7at0Qh6YXAJeiHwsCkA+4/atpqOqpgQ0FHqsray2bMCvD8Y4NIVgB4jra2LW0oXArpFkxFqR4uGAI/fB7Cc3tI969sIFeriYoSALlZuuJo2vh9A/twAA+iN3cdPh6uU6JArBIwOPQfdyis0vfztN/29vbxxABP6A5y94LGE3sLZQ7pzp/cdudZDQAioh06Uxc2dC1C+PMDttwPMm6ff+EdaANzXOHWaiRM9Mu66i/YMt6WOlzvJERACJscjKr+tX+8h3SOPABw6BFCrFkCjRsmh8PUbMO+RQyYHOWa84+rVA8iXD2DFCoDq1QH69gW4QJZSgh8E+GkICdGJwIkTiN26IaZPz4fJEAsUQJw6FfH69bTh8e+/iD17ImbI4JFbsCDirFmIN26kTa4bcxPsEqINgfh4xLfeQsyZ00OQmBjE/v0Rz583F4m//0Zs3NhTBhO8YUPEdevMLcPp0oSATtdgiPUfO3YJFiw48+YRasTWrRF37gxRSIjJP/0UsXBhDxHZ2rZpswL37SMzKQGFgFHSCbZt24YtWrQg4oHyKVfuFP7wQ+Qaf+ECYr9+iBkzrlDKj43Ng1OmTKHpbhrnu5FrQlhKEgKGBVb7CD179iy++OKL1PEzKh0/R47c+MQTH2BCgjV1/OOPc3jLLXdrA0GtWrVw9erV1lTGBqUKAW2ghHBUITExUbEw+fLlUzp7BvKI9OjRA0+fPh2O4kKWuWDBAixevLhSN3rbLg0KT+Dx48dDluP0DEJAp2vQR/1XrFiB1apV06zMnXfeiZs2bfKR0tpbly9fxqFDh2JsbKxS15zkFZowYQJZZ4vMswVwCAEtAD1cRe7duxfbtWunEa9UqVL41Vdfhas40+Tu2bOHHDNttHpXrlwZly9fbpp8OwsSAtpZO0HW7eLFizhkyBDNkmTPnh1HjRqFV69eDVKCPZJ99913WLZsWY2IHTp0wIMHD9qjcmGqhRAwTMBGQuwN2tmePXs2FilSROm0vJbq3LkzHjlyJBLFh6WMeNqkHD16NGbLlk1pU9asWR05mAQLjhAwWKRslu7333/HevXqadaCr/meW8KhQ4fw4Ycf1tpXpkwZXLx4sVuap7VDCKhB4YyLffv2KVaOrR3v6bH1YyvI1tCNgR1KvCZU9y9b08mB3bt3u6apQkAHqXLHjh2oEo89h7zu4/Wf2wN7RSdOnIhxcXEKEXlLha2jG4L8NL2fQ+p2vL1mzRpo2LAh5MqVC9bTIwzk5bRjNcNWpxMnTkDv3r1h/vz5QAMQkJMpbGVFSrA8jhQppE0oJ0uWLIqUkiVLRh35uOEFCxaEcePGKRgUKFBA+e/0P0JAB2mQ1nlKbWka6qBam1tVmnYqAulonbmCLZImBLQIeCPFqp0vvfcTsEYEOTiPOgi5BQMhoIM6o0pAsYAAbsFACOggArpt9DcCvdswEAIa6QUW5RELyM/Ve9aAYgEt6oTRXKzbRn8juhQCGkFN8piCgNs6nxFQ3DYIyRTUSC+wKI/bOp8RGN02CAkBjfQCi/K4rfMZgdFtg5AQ0EgvsCiPEFCcMBZ1PSmWEXDb6G9Eq27DQCygkV5gUR6xgGIBLep6Uiwj4LbR34hW1UFIjqIZQU/ypAkBtfO5ZRPaCBjqIOQWDGQKaqQXWJRH7XxuGf2NwOi2QUgIaKQXWJTHbZ3PCIxuG4SEgEZ6gUV51M7nlumXERjdNggJAY30AovyqJ0vmqeg6iDkFgyEgBaRyUixKgHFAsrzgEb6j+RJIwJuG/2NwOE2DMQCGukFFuURCygb8RZ1PSmWEXDb6G9Eq24bhMQCGukFFuVxW+czAqPbBiEhoJFeYFEet3U+IzC6bRASAhrpBRblcVvnMwKj2wYhIaCRXmBRHiGgOGEs6npSLCPgttHfiFbdhoFYQCO9wKI8YgHFAlrU9aRYRsBto78RraqDkBxFM4Ke5EkTAmrni+ajaOog5BYMZAqaJkpENrPa+dwy+htBz22DkBDQSC+wKI/bOp8RGN02CAkBjfQCi/Konc8t0y8jMLptEBICGukFFuVRO180T0HVQcgtGAgBLSKTkWKvX7+uZIuPjzeS3RV51EHILbMAIaCDuuWGDRuU2i5YsAAmTJgACQkJDqp92qt66dIlmDNnjiJo165daRdoAwlCQBsoIdgq9OvXD2699Va4du0a8HXVqlVhyZIlwWZ3bDqeds6YMQPKli0LPPhwKFeunGPbk6ziZNIlOAyB77//HsuXL89vqlQ+rVu3xp07dzqsFcFVd9WqVVizZk2trfXq1cPVq1cHl9kBqfiNoxIciABZQRw/fjzGxcUpnTMmJgYHDBiAFy5ccGBrUld59+7d2LZtW414xYoVw88//xzJGqZO7OA7QkAHK4+rfuLECezatSuSV1DprAULFsTp06c7tqOePXsWaXqNPKCwhc+ePTuOGDECL1++7HBN+a6+ENA3Lo67u27dOmzUqJFmMerUqYNr1qxxTDvIoYTvv/8+5suXT2kDDyhPPfUUHj161DFtMFJRIaAR1Gych6dpt9xyi9KJyVWPnTp1wiNHjti4xojkSMJKlSppg0fTpk1x/fr1tq6zWZUTApqFpI3kXLx4EYcOHYqZM2dWOjVP40aPHo1Xr161US0Rt2zZgi1atNCIV6ZMGVy0aJGt6hjuyggBw42whfL37duH7dq10zo4bWHYooOfOnUKn3vuOcyQIYNSt1y5cikOJTpgYCFa1hQtBLQG94iWunz5cqQ9Q+rsOemzA4sX34Zbt0a0CkphzK+nn16MGTN6PLcZM2bEXr164enTpyNfGZuUKAS0iSLCXY3ExEQcN+4T8paeIhIikQCxd2/Ef/8Nd8ke+V99hVi6NFLZA+gDWL/+fbht27bIFG7jUoSANlZOOKp2+vQNsjpI0z8mA2LevIhTpiASP8MSyDmLd9zhKYvLq1QpgbZJ3HlowAiAQkAjqLkgz6ZNiM2aJRGjenVEOnRiWmDH65NPIpIjViF6/vzhJbppFY+wICFghAG3W3E8NSxZMomIHTogHjiQVMtBkxAz3Yb46vtJ97yv4hojPj446Q7vlw8fjpgtm0cm7afTCR3Ec+eS0shVEgJyGDvZydjo+9KuHcD27QAjRgBkywYwfz5AvXpJOCgLNvrz1iyATTuT7qtXifSEFH/U0KoVwGuvAdCDC/DQQx7Z48YBxMWpKeS/NwJCQG80ovQ6c2aAoUMB/vkH4OmnATp2TA0Ek6w7kfT6jdRx3ndq1QJo0gTgl1+AnlwAenrDO1auUyKQMeUN+R69CNxyC8C0ab7b/2hLgDk/ALzzGUC/Lr7T8N233/YfJzGpERALmBoTueMDgdd7AJQqCjD8I4A9h3wkkFuGEBACGoIt+jJloWnqh68AXKFfw3h2JG8oRB8G4WixEDAcqLpUZrO6AE/eD/DzOpqqLnJpIyPcLCFghAF3enHj+gAUygswaDLAkZNOb4319RcCWq8DR9Ugd06ASQMBzl8E6P2mo6puy8qKF9SWarF3pdo1A3jwToCvV9q7nk6onVhAJ2jJhnWcTFYwVw4bVsxhVRICOkxhka5u+nS+SyycD2Dsi77j5G7wCNBRWXEoBw+XpBQEzEVALKC5eIo0QSAkBISAIcEliQ8cADh8WHAwCwGZgpqFZBTI4cVKXtoD5MPbR49GQYMj0EQhYARAdksR/C6YmBiATJmA3k/hllZZ2w6ZglqLv6NKp3ekKCG99BrT9CZQmgal+wWp/vJ0frYm3I+A+S0UApqPqWsligU0X7VCQPMxda1EsYDmq1YIaD6mrpUoFtB81QoBzcfUtRLFApqvWiGg+Zi6VqIQ0HzVCgHNx9S1Ej1T0CchPv5V17Yx0g2TjfhII+7g8o4f/xcKF84LGTLkhcTE0w5uiX2qLhbQPrqwfU0yZvTsxOfKJRuBZilLCGgWklEgR31yLb2/hwSjAAOzmygENBtRF8u7cXMfgt7f7uJWRrZpgmRk8XZ0aaoFpHfPO7oddqq8ENBO2rB5XcQCmq8gIaD5mLpWolhA81UrBDQfU9dKFAKar1ohoPmYulaiTEHNV60Q0HxMXStRLKD5qhUCmo+payWKBTRftUJA8zF1rUSxgOarVghoPqaulSgW0HzVCgHNx9S1EsUCmq9aIaD5mLpWolhA81UrBDQfU9dKFAtovmqFgOZj6lqJQkDzVSsENB9T10qUKaj5qhUCmo+payWKBTRftUJA8zF1rcQSJUpAlSpVoEWLFq5tY6QbJr8JE2nEpTxBwAsBsYBeYMilIBBpBISAkUZcyhMEvBAQAnqBIZeCQKQREAJGGnEpTxDwQkAI6AWGXAoCkUZACBhpxKU8QcALASGgFxhyKQhEGgEhYKQRl/IEAS8EhIBeYMilIBBpBISAkUZcyhMEvBAQAnqBIZeCQKQREAJGGnEpTxDwQkAI6AWGXAoCkUZACBhpxKU8QcALASGgFxhyKQhEGgEhYKQRl/IEAS8EhIBeYMilIBBpBISAkUZcyhMEvBAQAnqBIZeCQKQREAJGGnEpTxDwQuD/AWAvQmerY2wsAAAAAElFTkSuQmCC", "text/plain": [ "" ] }, - "execution_count": 3, + "execution_count": 5, "metadata": { "image/png": { "width": 300 @@ -253,48 +253,6 @@ "Image('test.png',width = 300)" ] }, - { - "cell_type": "markdown", - "metadata": { - "id": "16SZ5WoWwdjm" - }, - "source": [ - "\n", - "### OpenEye's `oenotebook` toolkit interfaces with Jupyter notebooks to display molecules\n", - "\n", - "This is super easy as long as you are inside a Jupyter notebook (see [the OpenEye notebook examples](https://www.eyesopen.com/notebooks-directory) for more related examples).\n", - "\n", - "(Currently we do not have `oenotebook` working on Colab because of version incompatibilities; in some cases it can locally be installed via `pip install -i https://pypi.anaconda.org/openeye/label/oenotebook/simple openeye-oenotebook`)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 217 - }, - "executionInfo": { - "elapsed": 188, - "status": "ok", - "timestamp": 1639684063936, - "user": { - "displayName": "Danielle Bergazin", - "photoUrl": "https://lh3.googleusercontent.com/a-/AOh14GiOdJdx7etc0W4Yfmh-4-g-P0aJMsIonVchL0BInA=s64", - "userId": "10589367849843705173" - }, - "user_tz": 480 - }, - "id": "lWikL0VMtlL3", - "outputId": "78a158e3-2ace-4cdf-f3ae-bad688ad867d" - }, - "outputs": [], - "source": [ - "import oenotebook as oenb\n", - "oenb.draw_mol(mol)" - ] - }, { "cell_type": "markdown", "metadata": { @@ -325,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -357,12 +315,12 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": { "image/png": { "width": 300 @@ -448,9 +406,9 @@ "source": [ "#Here, we look at cis- vs trans-difluoroethene\n", "mol3 = OEMol()\n", - "OEParseSmiles( mol3, 'F/C=C/F')\n", + "OEParseSmiles( mol3, r'F/C=C/F')\n", "mol4 = OEMol()\n", - "OEParseSmiles( mol4, 'F/C=C\\F')\n", + "OEParseSmiles( mol4, r'F/C=C\\F')\n", "print(\"Mol3:\", OECreateCanSmiString(mol3), OECreateIsoSmiString(mol3))\n", "print(\"Mol4:\", OECreateCanSmiString(mol4), OECreateIsoSmiString(mol4))\n", "OEPrepareDepiction(mol3)\n", @@ -504,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", @@ -532,7 +490,7 @@ "" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": { "image/png": { "width": 150 @@ -558,7 +516,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -582,7 +540,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "0.0\n" + "0.02222222276031971\n" ] } ], @@ -592,7 +550,7 @@ "mol2 = OEMol()\n", "\n", "#Load compounds by their names\n", - "OEParseIUPACName(mol1, '')\n", + "OEParseIUPACName(mol1, 'ibuprofen')\n", "OEParseIUPACName(mol2, 'caffeine')\n", "\n", "#Initialize a lingo search object with molecule 1 as the query\n", @@ -616,7 +574,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -661,7 +619,7 @@ "cutoff = 0.3\n", "\n", "#Specify our database - what compounds do we want to look at?\n", - "names = ['tylenol', 'phenol', 'toluene', 'benzene', 'naphthalene', 'ibuprofen', 'naproxen', 'acetic acid', 'ammonia', 'biphenyl']\n", + "names = ['tylenol', 'phenol', 'toluene', 'benzene', 'naphthalene', 'ibuprofen', 'naproxen', 'acetic acid', 'aniline', 'biphenyl']\n", "\n", "# Optionally add some from SMILES\n", "#smiles = ['c1ccccc1OCCNO']\n", @@ -685,6 +643,130 @@ " print(\"Similarity of %s to %s is %.2f\" % (queryname, name, sim))\n", " #More generally, you could dump image files of all molecules matching, or write them out to a file, or..." ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "#OERenderMolecule('test3.png', mol3)\n", + "#OEPrepareDepiction(mol4)\n", + "#OERenderMolecule('test4.png', mol4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Let's load a group of molecules from ZINC and check them for similarity\n", + "\n", + "I've already included a set of molecules here, \"CAAA.smi\", downloaded from ZINC. Let's check them for similarity.\n", + "\n", + "(You may have to do appropriate bookkeeping to ensure this file is in the directory you're working in.)\n", + "\n", + "We'll just repurpose the same strategy from above and print out those with similarity above some cutoff." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Similarity of caffeine to 1075510 is 0.49\n", + "Similarity of caffeine to 618254481 is 0.39\n", + "Similarity of caffeine to 12505415 is 0.30\n", + "Similarity of caffeine to 369431 is 0.70\n", + "Similarity of caffeine to 12541508 is 0.68\n", + "Similarity of caffeine to 33762 is 0.34\n", + "Similarity of caffeine to 1867630 is 0.79\n", + "Similarity of caffeine to 1646905 is 0.82\n", + "Similarity of caffeine to 517161 is 0.64\n", + "Similarity of caffeine to 26423221 is 0.40\n", + "Similarity of caffeine to 534656009 is 0.40\n", + "Similarity of caffeine to 6082801 is 0.68\n", + "Similarity of caffeine to 71283781 is 0.30\n", + "Similarity of caffeine to 40448 is 0.74\n", + "Similarity of caffeine to 182570 is 0.74\n", + "Similarity of caffeine to 517162 is 0.66\n", + "Similarity of caffeine to 40477861 is 0.33\n", + "Similarity of caffeine to 40498833 is 0.32\n", + "Similarity of caffeine to 12232557 is 0.36\n", + "Similarity of caffeine to 339454 is 0.34\n", + "Similarity of caffeine to 518508 is 0.72\n", + "Similarity of caffeine to 12598698 is 0.72\n", + "Similarity of caffeine to 12232559 is 0.36\n", + "Similarity of caffeine to 221 is 0.68\n", + "Similarity of caffeine to 8401377 is 0.35\n", + "Similarity of caffeine to 4783935 is 0.45\n", + "Similarity of caffeine to 68031 is 0.68\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Warning: Problem parsing SMILES:\n", + "Warning: smiles zinc_id\n", + "Warning: ^\n", + "\n", + "Warning: Error reading molecule \"\" in Canonical stereo SMILES format.\n" + ] + } + ], + "source": [ + "# Get file for reading, initiate empty oemol to read to\n", + "ifile = oemolistream('CAAA.smi')\n", + "mol = OEMol()\n", + "\n", + "# Take a molecule to use as query\n", + "querymol = OEMol()\n", + "queryname = 'caffeine'\n", + "OEParseIUPACName(querymol, queryname)\n", + "\n", + "#Set up our lingo search based on the query\n", + "lingo = OELingoSim(querymol)\n", + "\n", + "#Specify a cutoff we'll use for filtering\n", + "cutoff = 0.3\n", + "\n", + "# Loop over all the molecules in the file, reading them.\n", + "# This file is currently formatted in a way OEChem automatically understands, with the first entry being a \n", + "# SMILES and the second entry being a (in this case numerical) title.\n", + "# You will encounter a warning because the first line has headers, which OEChem does not expect.\n", + "while OEReadMolecule(ifile, mol):\n", + "\n", + " sim = lingo.Similarity(mol)\n", + " #print(mol.NumAtoms())\n", + " if sim > cutoff:\n", + " print(\"Similarity of %s to %s is %.2f\" % (queryname, mol.GetTitle(), sim))\n", + " #print(mol.GetTitle(), sim)\n", + "ifile.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Add saving/depiction of similar molecules, generate a sorted list\n", + "\n", + "**As an exercise**, store and/or depict those molecules which are similar to your reference molecule.\n", + "\n", + "You may also wish to consider generating a sorted list of molecules which are similar to your reference molecule, from most similar to least similar.\n", + "\n", + "(One usage note: The code above reads all molecules into the same OEMol object, `mol`, overwriting one another. If you wish to save molecules, you will want to generate a new OEMol of those you want to save and store it into a data structure, e.g. via `newmol = OEMol(mol)` and then saving newmol somewhere.)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -695,9 +777,9 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python [conda env:drugcomp] *", + "display_name": "drugcomp", "language": "python", - "name": "conda-env-drugcomp-py" + "name": "drugcomp" }, "language_info": { "codemirror_mode": { @@ -709,7 +791,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.12.8" }, "toc": { "nav_menu": {}, diff --git a/uci-pharmsci/lectures/library_searching/README.md b/uci-pharmsci/lectures/library_searching/README.md new file mode 100644 index 0000000..99a103f --- /dev/null +++ b/uci-pharmsci/lectures/library_searching/README.md @@ -0,0 +1,9 @@ + +## Manifest + + +### Utilities (used in putting together lecture slides) +- "visualize_molecules.py": Adapted from OpenFF; generates 2D grid PDF of molecules given an input file with SMILES strings. +- "CAAA.smi": First entry tranche of molecules from ZINC lead-like in-stock dataset as of Feb 2022. +- "AAAA.SMI": First entry tranche of molecules from ZINC fragment-like in stock dataset as of Feb 2022. +- "AHAA.smi": An entry of molecules from ZINC shards in stock dataset as of Feb. 2022 diff --git a/uci-pharmsci/lectures/library_searching/visualize_molecules.py b/uci-pharmsci/lectures/library_searching/visualize_molecules.py new file mode 100644 index 0000000..7d060e2 --- /dev/null +++ b/uci-pharmsci/lectures/library_searching/visualize_molecules.py @@ -0,0 +1,51 @@ +#!/usr/bin/env python + +""" +Generate a PDF of all small molecules in the JSON dataset. +""" + +import gzip + +# Read the input SMILES +with open('AHAA.smi', 'r') as infile: + smiles_list = infile.readlines() +smiles_list = [ smiles.strip() for smiles in smiles_list ] + +# Build OEMols +from openeye import oechem +oemols = list() +for smiles in smiles_list: + oemol = oechem.OEMol() + oechem.OESmilesToMol(oemol, smiles) + oemols.append(oemol) + +# Generate a PDF of all molecules in the set +pdf_filename = 'CAAA_structures.pdf' + +from openeye import oedepict +itf = oechem.OEInterface() +PageByPage = True +suppress_h = True +rows = 10 +cols = 6 +ropts = oedepict.OEReportOptions(rows, cols) +ropts.SetHeaderHeight(25) +ropts.SetFooterHeight(25) +ropts.SetCellGap(2) +ropts.SetPageMargins(10) +report = oedepict.OEReport(ropts) +cellwidth, cellheight = report.GetCellWidth(), report.GetCellHeight() +opts = oedepict.OE2DMolDisplayOptions(cellwidth, cellheight, oedepict.OEScale_Default * 0.5) +opts.SetAromaticStyle(oedepict.OEAromaticStyle_Circle) +pen = oedepict.OEPen(oechem.OEBlack, oechem.OEBlack, oedepict.OEFill_On, 1.0) +opts.SetDefaultBondPen(pen) +oedepict.OESetup2DMolDisplayOptions(opts, itf) +for i, mol in enumerate(oemols): + cell = report.NewCell() + mol_copy = oechem.OEMol(mol) + oedepict.OEPrepareDepiction(mol_copy, False, suppress_h) + disp = oedepict.OE2DMolDisplay(mol_copy, opts) + oedepict.OERenderMolecule(cell, disp) + #oedepict.OEDrawCurvedBorder(cell, oedepict.OELightGreyPen, 10.0) + +oedepict.OEWriteReport(pdf_filename, report) diff --git a/uci-pharmsci/lectures/ligand_based_design/ligand_based_design.ipynb b/uci-pharmsci/lectures/ligand_based_design/ligand_based_design.ipynb index 597259e..4faa710 100644 --- a/uci-pharmsci/lectures/ligand_based_design/ligand_based_design.ipynb +++ b/uci-pharmsci/lectures/ligand_based_design/ligand_based_design.ipynb @@ -409,6 +409,22 @@ }, "outputs": [], "source": [] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Additional exercise: Use this to search a database\n", + "\n", + "Last lecture, our sandbox included an option to search a small ZINC subset based on a query ligand (using Lingo search). Here, do the same (reusing the file stored in that directory if you like) but using shape search." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -419,9 +435,9 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python [conda env:drugcomp] *", + "display_name": "drugcomp", "language": "python", - "name": "conda-env-drugcomp-py" + "name": "drugcomp" }, "language_info": { "codemirror_mode": { diff --git a/uci-pharmsci/lectures/ligand_based_design/ligand_based_design_slides.key b/uci-pharmsci/lectures/ligand_based_design/ligand_based_design_slides.key index bdd563d..8a84386 100644 Binary files a/uci-pharmsci/lectures/ligand_based_design/ligand_based_design_slides.key and b/uci-pharmsci/lectures/ligand_based_design/ligand_based_design_slides.key differ diff --git a/uci-pharmsci/lectures/solvation/.gitignore b/uci-pharmsci/lectures/obsolete/solvation/.gitignore similarity index 100% rename from uci-pharmsci/lectures/solvation/.gitignore rename to uci-pharmsci/lectures/obsolete/solvation/.gitignore diff --git a/uci-pharmsci/lectures/solvation/README.md b/uci-pharmsci/lectures/obsolete/solvation/README.md similarity index 88% rename from uci-pharmsci/lectures/solvation/README.md rename to uci-pharmsci/lectures/obsolete/solvation/README.md index e0d1a9a..a4fbc83 100644 --- a/uci-pharmsci/lectures/solvation/README.md +++ b/uci-pharmsci/lectures/obsolete/solvation/README.md @@ -1,6 +1,7 @@ # Solvation models This provides materials for the lecture on solvation models. +As of 2023 we are (at least temporarily) removing this lecture because OpenFF at present doesn't work with GB models. ## Manifest ### Primary files diff --git a/uci-pharmsci/lectures/solvation/images/1W2I.png b/uci-pharmsci/lectures/obsolete/solvation/images/1W2I.png similarity index 100% rename from uci-pharmsci/lectures/solvation/images/1W2I.png rename to uci-pharmsci/lectures/obsolete/solvation/images/1W2I.png diff --git a/uci-pharmsci/lectures/solvation/images/ProtAssocRates.png b/uci-pharmsci/lectures/obsolete/solvation/images/ProtAssocRates.png similarity index 100% rename from uci-pharmsci/lectures/solvation/images/ProtAssocRates.png rename to uci-pharmsci/lectures/obsolete/solvation/images/ProtAssocRates.png diff --git a/uci-pharmsci/lectures/solvation/images/born.html b/uci-pharmsci/lectures/obsolete/solvation/images/born.html similarity index 100% rename from uci-pharmsci/lectures/solvation/images/born.html rename to uci-pharmsci/lectures/obsolete/solvation/images/born.html diff --git a/uci-pharmsci/lectures/solvation/images/born.png b/uci-pharmsci/lectures/obsolete/solvation/images/born.png similarity index 100% rename from uci-pharmsci/lectures/solvation/images/born.png rename to uci-pharmsci/lectures/obsolete/solvation/images/born.png diff --git a/uci-pharmsci/lectures/solvation/images/debye_huckel.png b/uci-pharmsci/lectures/obsolete/solvation/images/debye_huckel.png similarity index 100% rename from uci-pharmsci/lectures/solvation/images/debye_huckel.png rename to uci-pharmsci/lectures/obsolete/solvation/images/debye_huckel.png diff --git a/uci-pharmsci/lectures/solvation/images/extended.png b/uci-pharmsci/lectures/obsolete/solvation/images/extended.png similarity index 100% rename from uci-pharmsci/lectures/solvation/images/extended.png rename to uci-pharmsci/lectures/obsolete/solvation/images/extended.png diff --git a/uci-pharmsci/lectures/solvation/images/image_charge.png b/uci-pharmsci/lectures/obsolete/solvation/images/image_charge.png similarity index 100% rename from uci-pharmsci/lectures/solvation/images/image_charge.png rename to uci-pharmsci/lectures/obsolete/solvation/images/image_charge.png diff --git a/uci-pharmsci/lectures/solvation/images/rings.png b/uci-pharmsci/lectures/obsolete/solvation/images/rings.png similarity index 100% rename from uci-pharmsci/lectures/solvation/images/rings.png rename to uci-pharmsci/lectures/obsolete/solvation/images/rings.png diff --git a/uci-pharmsci/lectures/obsolete/solvation/molecule.mol2 b/uci-pharmsci/lectures/obsolete/solvation/molecule.mol2 new file mode 100644 index 0000000..46897fd --- /dev/null +++ b/uci-pharmsci/lectures/obsolete/solvation/molecule.mol2 @@ -0,0 +1,50 @@ +@MOLECULE +***** + 21 21 0 0 0 +SMALL +NO_CHARGES + +@ATOM + 1 O1 0.8036 -2.7039 -5.9633 O.3 1 UNL1 nan + 2 C1 0.2992 -2.5729 -4.7095 C.2 1 UNL1 nan + 3 C2 0.5910 -1.1933 -4.1767 C.3 1 UNL1 nan + 4 C3 0.0448 -0.9981 -2.7731 C.3 1 UNL1 nan + 5 C4 0.6372 0.2135 -2.0602 C.3 1 UNL1 nan + 6 C5 -0.3396 0.4881 -0.9209 C.3 1 UNL1 nan + 7 C6 -1.6799 -0.1059 -1.3575 C.3 1 UNL1 nan + 8 C7 -1.4494 -0.6928 -2.7462 C.3 1 UNL1 nan + 9 O2 -0.3005 -3.4610 -4.1225 O.2 1 UNL1 nan + 10 H1 0.6282 -3.5938 -6.3370 H 1 UNL1 nan + 11 H2 0.1358 -0.4581 -4.8486 H 1 UNL1 nan + 12 H3 1.6762 -1.0475 -4.1671 H 1 UNL1 nan + 13 H4 0.2422 -1.9014 -2.1809 H 1 UNL1 nan + 14 H5 1.6518 0.0270 -1.6943 H 1 UNL1 nan + 15 H6 0.6757 1.0813 -2.7303 H 1 UNL1 nan + 16 H7 0.0009 0.0008 0.0000 H 1 UNL1 nan + 17 H8 -0.4171 1.5603 -0.7140 H 1 UNL1 nan + 18 H9 -1.9815 -0.8931 -0.6567 H 1 UNL1 nan + 19 H10 -2.4750 0.6463 -1.3687 H 1 UNL1 nan + 20 H11 -1.7112 0.0502 -3.5096 H 1 UNL1 nan + 21 H12 -2.0610 -1.5832 -2.9214 H 1 UNL1 nan +@BOND + 1 1 2 1 + 2 2 3 1 + 3 3 4 1 + 4 4 8 1 + 5 4 5 1 + 6 5 6 1 + 7 6 7 1 + 8 7 8 1 + 9 2 9 2 + 10 1 10 1 + 11 3 11 1 + 12 3 12 1 + 13 4 13 1 + 14 5 14 1 + 15 5 15 1 + 16 6 16 1 + 17 6 17 1 + 18 7 18 1 + 19 7 19 1 + 20 8 20 1 + 21 8 21 1 diff --git a/uci-pharmsci/lectures/solvation/solvation.ipynb b/uci-pharmsci/lectures/obsolete/solvation/solvation.ipynb similarity index 99% rename from uci-pharmsci/lectures/solvation/solvation.ipynb rename to uci-pharmsci/lectures/obsolete/solvation/solvation.ipynb index 2256afc..36abdbb 100644 --- a/uci-pharmsci/lectures/solvation/solvation.ipynb +++ b/uci-pharmsci/lectures/obsolete/solvation/solvation.ipynb @@ -685,7 +685,9 @@ "from openff.toolkit.topology import Topology, Molecule\n", "from openff.toolkit.typing.engines.smirnoff import ForceField\n", "\n", - "from simtk import openmm, unit\n", + "from simtk import openmm\n", + "from openff.units import unit\n", + "from simtk import unit as omm_unit\n", "import os\n", "from datetime import datetime\n", "import parmed\n", @@ -730,11 +732,11 @@ " \n", " # Energy minimize\n", " # Even though we're just going to minimize, we still have to set up an integrator, since a Simulation needs one\n", - " integrator = openmm.VerletIntegrator(2.0*unit.femtoseconds)\n", + " integrator = openmm.VerletIntegrator(2.0*omm_unit.femtoseconds)\n", " # Prep the Simulation using the parameterized system, the integrator, and the topology\n", " simulation = openmm.app.Simulation(omm_topology, system, integrator)\n", " # Copy in the positions\n", - " simulation.context.setPositions( positions) \n", + " simulation.context.setPositions( positions.to_openmm()) \n", "\n", " # Get initial state and energy; print\n", " state = simulation.context.getState(getEnergy = True)\n", @@ -749,7 +751,7 @@ " newpositions = state.getPositions()\n", " \n", " from mdtraj.reporters import NetCDFReporter\n", - " integrator = openmm.LangevinIntegrator(300*unit.kelvin, 1./unit.picosecond, 2.*unit.femtoseconds)\n", + " integrator = openmm.LangevinIntegrator(300*omm_unit.kelvin, 1./omm_unit.picosecond, 2.*omm_unit.femtoseconds)\n", "\n", " # Prep Simulation\n", " simulation = openmm.app.Simulation(omm_topology, system, integrator)\n", @@ -757,7 +759,7 @@ " simulation.context.setPositions(newpositions)\n", "\n", " # Initialize velocities to correct temperature\n", - " simulation.context.setVelocitiesToTemperature(300*unit.kelvin)\n", + " simulation.context.setVelocitiesToTemperature(300*omm_unit.kelvin)\n", " # Set up to write trajectory file to NetCDF file in data directory every 100 frames\n", " netcdf_reporter = NetCDFReporter(os.path.join('.', '%s.nc' % outprefix), 100) #Store every 100 frames\n", " pdb_reporter = openmm.app.PDBReporter(\"%s_trajectory.pdb\" % outprefix, 100) #store every 100 steps\n", @@ -926,7 +928,9 @@ "## Now let's make an alternate function which does the same thing, but in implicit solvent\n", "\n", "\n", - "Basically we just need the same function as above, but with the addition of Generalized Born (GB) implicit solvent parameters." + "Basically we just need the same function as above, but with the addition of Generalized Born (GB) implicit solvent parameters.\n", + "\n", + "**The below is now obsolete, as of mid-2022, as OpenFF does not currently export GB parameters to OpenMM via Interchange.**" ] }, { @@ -1008,26 +1012,26 @@ " \n", " # Energy minimize\n", " # Even though we're just going to minimize, we still have to set up an integrator, since a Simulation needs one\n", - " integrator = openmm.VerletIntegrator(2.0*unit.femtoseconds)\n", + " integrator = openmm.VerletIntegrator(2.0*omm_unit.femtoseconds)\n", " # Prep the Simulation using the parameterized system, the integrator, and the topology\n", " simulation = openmm.app.Simulation(omm_topology, system, integrator)\n", " # Copy in the positions\n", - " simulation.context.setPositions( positions) \n", + " simulation.context.setPositions( positions.to_openmm()) \n", "\n", " # Get initial state and energy; print\n", " state = simulation.context.getState(getEnergy = True)\n", - " energy = state.getPotentialEnergy() / unit.kilocalories_per_mole\n", + " energy = state.getPotentialEnergy() / omm_unit.kilocalories_per_mole\n", " #print(\"Energy before minimization (kcal/mol): %.2g\" % energy)\n", "\n", " # Minimize, get final state and energy and print\n", " simulation.minimizeEnergy()\n", " state = simulation.context.getState(getEnergy=True, getPositions=True)\n", - " energy = state.getPotentialEnergy() / unit.kilocalories_per_mole\n", + " energy = state.getPotentialEnergy() / omm_unit.kilocalories_per_mole\n", " #print(\"Energy after minimization (kcal/mol): %.2g\" % energy)\n", " newpositions = state.getPositions()\n", " \n", " from mdtraj.reporters import NetCDFReporter\n", - " integrator = openmm.LangevinIntegrator(300*unit.kelvin, 1./unit.picosecond, 2.*unit.femtoseconds)\n", + " integrator = openmm.LangevinIntegrator(300*omm_unit.kelvin, 1./omm_unit.picosecond, 2.*omm_unit.femtoseconds)\n", "\n", " # Prep Simulation\n", " simulation = openmm.app.Simulation(omm_topology, system, integrator)\n", @@ -1035,7 +1039,7 @@ " simulation.context.setPositions(newpositions)\n", "\n", " # Initialize velocities to correct temperature\n", - " simulation.context.setVelocitiesToTemperature(300*unit.kelvin)\n", + " simulation.context.setVelocitiesToTemperature(300*omm_unit.kelvin)\n", " # Set up to write trajectory file to NetCDF file in data directory every 100 frames\n", " netcdf_reporter = NetCDFReporter(os.path.join('.', '%s.nc' % outprefix), 100) #Store every 100 frames\n", " pdb_reporter = openmm.app.PDBReporter(\"%s_trajectory.pdb\" % outprefix, 100) #store every 100 steps\n", @@ -1871,9 +1875,9 @@ "provenance": [] }, "kernelspec": { - "display_name": "Python [conda env:drugcomp] *", + "display_name": "drugcomp23", "language": "python", - "name": "conda-env-drugcomp-py" + "name": "drugcomp23" }, "language_info": { "codemirror_mode": { @@ -1885,7 +1889,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.9.15" }, "livereveal": { "height": 900, diff --git a/uci-pharmsci/lectures/proteins/proteins.key b/uci-pharmsci/lectures/proteins/proteins.key index 7a821c4..fbb73ce 100644 --- a/uci-pharmsci/lectures/proteins/proteins.key +++ b/uci-pharmsci/lectures/proteins/proteins.key @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:ea923e79a81223acdb110b55931750e3f27f30672377952ee329788bd0a90855 -size 35244405 +oid sha256:abd64477f56da2b0f171ce0b79d8f97b7d37af5d1e27a63a8fabfc4efd8263ed +size 28096805 diff --git a/uci-pharmsci/lectures/proteins/proteins.pdf b/uci-pharmsci/lectures/proteins/proteins.pdf index 5863924..5137f45 100644 Binary files a/uci-pharmsci/lectures/proteins/proteins.pdf and b/uci-pharmsci/lectures/proteins/proteins.pdf differ diff --git a/uci-pharmsci/lectures/visualization/BACE1.svg b/uci-pharmsci/lectures/visualization/BACE1.svg new file mode 100644 index 0000000..d51e36f --- /dev/null +++ b/uci-pharmsci/lectures/visualization/BACE1.svg @@ -0,0 +1,3988 @@ + + + + + + + + + + + Interaction Map + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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+ + + + + LEU + 33 + + + + + + + ASH + 35 + + + + + + + GLY + 37 + + + + + + + SER + 38 + + + + + + + VAL + 72 + + + + + + + PRO + 73 + + + + + + + TYR + 74 + + + + + + + THR + 75 + + + + + + + GLN + 76 + + + + + + + PHE + 111 + + + + + + + ILE + 113 + + + + + + + TRP + 118 + + + + + + + ILE + 121 + + + + + + + TYR + 190 + + + + + + + LYS + 216 + + + + + + + ILE + 218 + + + + + + + ASH + 220 + + + + + + + GLY + 222 + + + + + + + THR + 223 + + + + + + + THR + 224 + + + + + + + ARG + 227 + + + + + + + THR + 321 + + + + + + + VAL + 324 + + + + + + + + + + + diff --git a/uci-pharmsci/lectures/visualization/README.md b/uci-pharmsci/lectures/visualization/README.md index e8f1c33..e6b3898 100644 --- a/uci-pharmsci/lectures/visualization/README.md +++ b/uci-pharmsci/lectures/visualization/README.md @@ -12,4 +12,5 @@ As of the 2017-2018 school year we are shifting to primarily instructing on use - `pymol_files`: Supplemental files relating to the PyMol lecture (e.g. sample movie files referenced therein) - `pymol_tutorial.md`: Tutorial content relating to PyMol, useful if you choose to use PyMol rather than VMD for the visualization assignment. - `BACE1.pdb`: PDB file from Lea El Khoury and Sukanya Sasmal (Mobley lab) of a BACE1 inhibitor bound to BACE, from the D3R 2018 grand challenge. Can be used with OpenEye's example code https://docs.eyesopen.com/toolkits/cookbook/python/visualization/activesitemaps.html to generate a 2D interaction map, as shown in the file `interaction_map.svg` here. +- `visualize_interactions.ipynb`: Jupyter notebook to generate 2D interaction maps - `interaction_map.svg`: Sample interaction map, as noted in previous point. diff --git a/uci-pharmsci/lectures/visualization/VMD.key b/uci-pharmsci/lectures/visualization/VMD.key index f0ec0fa..cd0199d 100644 --- a/uci-pharmsci/lectures/visualization/VMD.key +++ b/uci-pharmsci/lectures/visualization/VMD.key @@ -1,3 +1,3 @@ version https://git-lfs.github.com/spec/v1 -oid sha256:0f3778f73383d754ec5483f3931c36e7265d0d4db7ef7836665c4c5f99a7f6a2 -size 217748560 +oid sha256:9707aa63921ce8d060ed05dc466d916301daa042288b5e2679cf5d3251e652ae +size 265272956 diff --git a/uci-pharmsci/lectures/visualization/pymol_files/Week3_Lec1.pse b/uci-pharmsci/lectures/visualization/pymol_files/Week3_Lec1.pse new file mode 100644 index 0000000..336a53b Binary files /dev/null and b/uci-pharmsci/lectures/visualization/pymol_files/Week3_Lec1.pse differ diff --git a/uci-pharmsci/lectures/visualization/visualize_interactions.ipynb b/uci-pharmsci/lectures/visualization/visualize_interactions.ipynb new file mode 100644 index 0000000..4fdb258 --- /dev/null +++ b/uci-pharmsci/lectures/visualization/visualize_interactions.ipynb @@ -0,0 +1,117 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c762bb6d", + "metadata": {}, + "source": [ + "# Visualizing binding site interactions in 2D\n", + "\n", + "Here's a simple example (based on an example from OpenEye) of depicting 2D interactions between a receptor and a binding site. The output is an interactive svg best visualized in a web browser or similar." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e41c5e02", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from openeye import oechem\n", + "from openeye import oedepict\n", + "from openeye import oegrapheme\n", + "# Example based on https://docs.eyesopen.com/toolkits/cookbook/python/_downloads/activesitemaps2img.py\n", + "\n", + "# Input and output file\n", + "ifile = 'BACE1.pdb'\n", + "ofile = 'BACE1.svg'\n", + "#Width and height\n", + "width = 900\n", + "height = 600\n", + "\n", + "# Set split for molecular complex\n", + "#oechem.OEConfigureSplitMolComplexOptions(itf, oechem.OESplitMolComplexSetup_LigName | oechem.OESplitMolComplexSetup_CovLig)\n", + "\n", + "# Configure input and output\n", + "ifs = oechem.oemolistream()\n", + "if not ifs.open(ifile):\n", + " raise Exception(\"Error: Cannot open input file.\")\n", + "\n", + "\n", + "# Initialize protein and ligand\n", + "protein = oechem.OEGraphMol()\n", + "ligand = oechem.OEGraphMol()\n", + " \n", + "# Read input\n", + "complexmol = oechem.OEGraphMol()\n", + "if not oechem.OEReadMolecule(ifs, complexmol):\n", + " raise Exception(\"Unable to read complex from %s\" % ifile)\n", + " \n", + "# Ensure it has residues\n", + "if not oechem.OEHasResidues(complexmol):\n", + " oechem.OEPerceiveResidues(complexmol, oechem.OEPreserveResInfo_All)\n", + " \n", + "# Split molecular complex\n", + "water = oechem.OEGraphMol()\n", + "other = oechem.OEGraphMol()\n", + "\n", + "sopts = oechem.OESplitMolComplexOptions()\n", + "pfilter = sopts.GetProteinFilter()\n", + "wfilter = sopts.GetWaterFilter()\n", + "sopts.SetProteinFilter(oechem.OEOrRoleSet(pfilter, wfilter))\n", + "filter = oechem.OEMolComplexFilterCategory_Nothing\n", + "sopts.SetWaterFilter(oechem.OEMolComplexFilterFactory(filter))\n", + "\n", + "oechem.OESplitMolComplex(ligand, protein, water, other, complexmol, sopts)\n", + "\n", + "# Depict image\n", + "image = oedepict.OEImage(width, height)\n", + "asite = oechem.OEInteractionHintContainer(protein, ligand)\n", + "if not asite.IsValid():\n", + " oechem.OEThrow.Fatal(\"Cannot initialize active site!\")\n", + "asite.SetTitle(ligand.GetTitle())\n", + "\n", + "oechem.OEPerceiveInteractionHints(asite)\n", + "\n", + "# depiction\n", + "\n", + "oegrapheme.OEPrepareActiveSiteDepiction(asite)\n", + "oegrapheme.OERenderActiveSiteMaps(image, asite)\n", + "oedepict.OEDrawCurvedBorder(image, oedepict.OELightGreyPen, 10.0)\n", + "oedepict.OEWriteImage(ofile, image)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python [conda env:drugcomp] *", + "language": "python", + "name": "conda-env-drugcomp-py" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}