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MolDQN-pytorch

PyTorch implementation of MolDQN as described in Optimization of Molecules via Deep Reinforcement Learning by Zhenpeng Zhou, Steven Kearnes, Li Li, Richard N. Zare and Patrick Riley.

Installation

<a name="source"></a>From source:

1. Install rdkit.
   conda create -c rdkit -n my-rdkit-env rdkit
conda activate my-rdkit-env
conda install -c conda-forge rdkit

2. Clone this repository.
   git clone https://github.com/aksub99/MolDQN-pytorch.git
cd MolDQN-pytorch

3. Install the requirements given in requirements.txt.
   pip install -r requirements.txt

4. Install baselines.
   pip install "git+https://github.com/openai/baselines.git"

From Docker:

Using a docker image requires an NVIDIA GPU. If you do not have a GPU please follow the directions for installing from source In order to get GPU support you will have to use the nvidia-docker plugin.

# Build the Dockerfile in Dockerfiles/Dockerfile to create a Docker image.
cd Dockerfiles
docker build -t moldqn_pytorch:latest .

# This will create a container from the image we just created.
nvidia-docker run -[Options] moldqn_pytorch:latest python path/to/main.py

Please remember to modify the TB_LOG_PATH variable in main.py depending on where you wish to store your tensorboard runs file.

Training the MolDQN:

python main.py

A simple example to train the model on a single property optimization task can be seen in examples/MolDQN-pytorch.ipynb.

Results:

The following was the reward curve obtained when the model was trained for 5000 episodes on a single property optimization task (QED in this case).

References:

The original tensorflow implementation can be found at https://github.com/google-research/google-research/tree/master/mol_dqn This repository re-uses some code from the original implementation.