update dpcs

Author: liugangcode

docs/requirements.txt

@@ -1,3 +1,26 @@
+# Install PyTorch with CUDA 11.8
+-f https://download.pytorch.org/whl/cu118/torch_stable.html
+torch==2.2.0+cu118
+
+# Install PyTorch Geometric and related packages
+-f https://data.pyg.org/whl/torch-2.2.0+cu118.html
+torch_geometric==2.6.1
+torch_cluster
+torch_scatter
+
+# Other dependencies
+huggingface_hub
+joblib==1.3.2
+networkx==3.2.1
+pandas==2.2.3
+PyYAML==6.0.2
+rdkit==2023.9.5
+scikit_learn==1.4.1.post1
+scipy==1.14.1
+tqdm==4.66.2
+
+optuna
+# docs
 sphinx
 furo
 sphinx-gallery

docs/source/api.rst

@@ -3,29 +3,6 @@ API Reference
 
 This section documents the main modules and classes in `torch_molecule`.
 
-Base Modules
-------------
-
-.. automodule:: torch_molecule.base
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-.. automodule:: torch_molecule.base.encoder
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-.. automodule:: torch_molecule.base.generator
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
-.. automodule:: torch_molecule.base.predictor
-   :members:
-   :undoc-members:
-   :show-inheritance:
-
 
 Predictors
 ----------
@@ -181,3 +158,26 @@ Utilities
    :members:
    :undoc-members:
    :show-inheritance:
+
+Base Modules
+------------
+
+.. automodule:: torch_molecule.base
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+.. automodule:: torch_molecule.base.encoder
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+.. automodule:: torch_molecule.base.generator
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+.. automodule:: torch_molecule.base.predictor
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/source/index.rst

@@ -6,10 +6,11 @@
 torch-molecule documentation
 ============================
 
-Add your content using ``reStructuredText`` syntax. See the
-`reStructuredText <https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html>`_
-documentation for details.
+Welcome to the Torch-Molecule documentation. Torch-Molecule is an actively developed package designed to facilitate molecular discovery through deep learning. It offers a user-friendly interface similar to `sklearn`, and includes model checkpoints for efficient deployment and benchmarking across various molecular tasks. The package currently focuses on three main components:
 
+- Predictors: Molecular property prediction models that make predictions based on molecular graphs.
+- Generators: Molecular graph generators that generate new molecules.
+- Encoders: Molecular graph encoders that convert molecular graphs into a vector representation.
 
 .. toctree::
    :maxdepth: 2