From 1f3c9b253fe0c830a15ed7378db8cc66761e9e00 Mon Sep 17 00:00:00 2001 From: Larry Bowden Date: Sun, 24 Oct 2021 20:47:20 -0500 Subject: [PATCH 1/7] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 606aa9b..bd01b39 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ We use a Graph Convolutional Network followed by a Feed Forward Network to predi ### Setup ```bash - conda install numpy pandas matplotlib + conda install numpy pandas matplotlib hyperopt conda install pytorch torchvision cudatoolkit=10.2 -c pytorch conda install -c rdkit rdkit ``` @@ -15,7 +15,7 @@ We use a Graph Convolutional Network followed by a Feed Forward Network to predi We first try to find optimal hyperparameters using Bayesian Optimization. Currently, the hyperparameters that can be found using this are 1) depth of the GCN encoder 2) the dimensions of the message vectors 3) the number of layers in the Feed Forward Network 4) and the Dropout constant. The code can be run as follows:

`python hyperparameter_optimization.py --data_path --dataset_type --num_iters --config_save_path `

where \ is the path to csv file where the smiles and the corresponding property scores are stored, \ can be regression or dopamine which corresponds to using mse loss and adaptive robust loss respectively, \ is the number of epochs and \ is the path to json file where the configurations are to be saved. For example:

-`python hyperparamter_optimization.py --data_path data/dopamine_nodup.csv --dataset_type dopamine --num_iters 100 --config_save_path config.json` +`python hyperparameter_optimization.py --data_path data/dopamine_nodup.csv --dataset_type dopamine --num_iters 100 --config_save_path config.json` ### Training We can use the configurations obtained from Hyperparameter Optimization or directly train the model by running the following code:

From 4c74611c59b24a86ef95f877d395a3c4cba5e998 Mon Sep 17 00:00:00 2001 From: Larry Bowden Date: Sun, 24 Oct 2021 21:04:03 -0500 Subject: [PATCH 2/7] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index bd01b39..0b3acc0 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ We use a Graph Convolutional Network followed by a Feed Forward Network to predi ### Setup ```bash - conda install numpy pandas matplotlib hyperopt + conda install numpy pandas matplotlib hyperopt tensorboardX torch_dct conda install pytorch torchvision cudatoolkit=10.2 -c pytorch conda install -c rdkit rdkit ``` From f501e976a0f24c7ccecdc92da5a3b3a62b1a501e Mon Sep 17 00:00:00 2001 From: Larry Bowden Date: Sun, 24 Oct 2021 21:06:22 -0500 Subject: [PATCH 3/7] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 0b3acc0..1572d60 100644 --- a/README.md +++ b/README.md @@ -37,7 +37,7 @@ We use Proximal Policy Optimization (PPO) as the Reinforcement Learning pathway ```bash pip install tensorflow conda install mpi4py -pip install networkx=1.11 +pip install networkx==1.11 ``` - Install OpenAI baseline dependencies ```bash From 8536a2cf0c9aa18d9c75bf98896425288e21d76b Mon Sep 17 00:00:00 2001 From: Larry Bowden Date: Sun, 24 Oct 2021 21:07:16 -0500 Subject: [PATCH 4/7] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1572d60..6898cf8 100644 --- a/README.md +++ b/README.md @@ -47,7 +47,7 @@ pip install -e - Install customized gym molecule env ```bash cd gym-molecule -pip install -e +pip install -e . ``` ### Run Experiments This section contains the code to run the 5 experiments as presented in the paper. The general command line argument for running the code is as follows:

From e589fb4522227d14d7d6a7a723d6f4c776a2c1d2 Mon Sep 17 00:00:00 2001 From: Larry Bowden Date: Sun, 24 Oct 2021 21:08:12 -0500 Subject: [PATCH 5/7] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 6898cf8..f2859f7 100644 --- a/README.md +++ b/README.md @@ -42,7 +42,7 @@ pip install networkx==1.11 - Install OpenAI baseline dependencies ```bash cd rl-baselines -pip install -e +pip install -e . ``` - Install customized gym molecule env ```bash From b4ab7ca3c1670b725f14a939c1a707fc87431f0c Mon Sep 17 00:00:00 2001 From: Larry Bowden Date: Sun, 24 Oct 2021 21:18:49 -0500 Subject: [PATCH 6/7] Update README.md --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index f2859f7..c143fdd 100644 --- a/README.md +++ b/README.md @@ -41,6 +41,7 @@ pip install networkx==1.11 ``` - Install OpenAI baseline dependencies ```bash +sudo apt-get install libosmesa6-dev cd rl-baselines pip install -e . ``` From 06e79edfa78da829c3a7be3cb44c1954168901da Mon Sep 17 00:00:00 2001 From: Larry Bowden Date: Mon, 25 Oct 2021 22:16:09 -0500 Subject: [PATCH 7/7] Update README.md --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index c143fdd..2938e30 100644 --- a/README.md +++ b/README.md @@ -55,12 +55,12 @@ This section contains the code to run the 5 experiments as presented in the pape `mpirun -np python run_molecule.py --is_conditional --reward_type --dataset --model_path [--model2_path --sa_ratio --gan_step_ratio --gan_final_ratio --conditional ]`

where \ is the number of parallel processes to be run, \ is 1 when the generative process is initialized with a molecule else 0, \ is pki for single-objective optimization else multi for multi-objective optimization, \ is zinc if taking ZINC as the expert dataset and dopamine if taking dopamine BindingDB as the expert dataset, \ is the path to trained model (.pt file), \ is the path to second trained model (.pt file, only valid when reward type is "multi"), \ is the weight of SA Score in the final reward, \ is the weight of stepwise discriminator reward in the final reward, \ is the weight of final discriminator reward in the final reward and \ is dopamine_25 or dopamine_15 which is valid only when \ is 1. - Experiment 1
-`mpirun -np 8 python run_molecule.py --is_conditional 0 --reward_type pki --dataset zinc --model_path --sa_ratio 2 --gan_step_ratio 2 --gan_final_ratio 3`

+`mpirun -np 6 python run_molecule.py --is_conditional 0 --reward_type pki --dataset zinc --model_path --sa_ratio 2 --gan_step_ratio 2 --gan_final_ratio 3`

- Experiment 2
-`mpirun -np 8 python run_molecule.py --is_conditional 0 --reward_type pki --dataset dopamine --model_path --sa_ratio 2 --gan_step_ratio 2 --gan_final_ratio 3`

+`mpirun -np 6 python run_molecule.py --is_conditional 0 --reward_type pki --dataset dopamine --model_path --sa_ratio 2 --gan_step_ratio 2 --gan_final_ratio 3`

- Experiment 3
-`mpirun -np 8 python run_molecule.py --is_conditional 1 --conditional dopamine_25 --reward_type pki --dataset zinc --model_path --sa_ratio 2 --gan_step_ratio 2 --gan_final_ratio 3`

+`mpirun -np 6 python run_molecule.py --is_conditional 1 --conditional dopamine_25 --reward_type pki --dataset zinc --model_path --sa_ratio 2 --gan_step_ratio 2 --gan_final_ratio 3`

- Experiment 4
-`mpirun -np 8 python run_molecule.py --is_conditional 1 --conditional dopamine_15 --reward_type pki --dataset zinc --model_path --sa_ratio 2 --gan_step_ratio 2 --gan_final_ratio 3`

+`mpirun -np 6 python run_molecule.py --is_conditional 1 --conditional dopamine_15 --reward_type pki --dataset zinc --model_path --sa_ratio 2 --gan_step_ratio 2 --gan_final_ratio 3`

- Experiment 5
-`mpirun -np 8 python run_molecule.py --is_conditional 1 --conditional dopamine_25 --reward_type multi --dataset zinc --model_path --model2_path --sa_ratio 2 --gan_step_ratio 2 --gan_final_ratio 3`

+`mpirun -np 6 python run_molecule.py --is_conditional 1 --conditional dopamine_25 --reward_type multi --dataset zinc --model_path --model2_path --sa_ratio 2 --gan_step_ratio 2 --gan_final_ratio 3`