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LS-MolGen

A ligand-and-structure dual-driven deep reinforcement learning method for target-specific molecular generation.

Paper: https://pubs.acs.org/doi/10.1021/acs.jcim.3c00587

Installation

You can use the environment.yml file to create a new conda environment with all the necessary dependencies for LS-MolGen:

git clone https://github.com/songleee/LS-MolGen.git
cd LS-MolGen
conda env create -f environment.yml
conda activate LS-MolGen

Dependences

To run this code, especially the reinforcement learning part, the following softwares are required:

1. LeDock: the program used to get the docking score. Download from here.

2. Open Babel: the software used to prepare the ligand. Download from here.

After download the release packages of them, remember to add the PATH of them to the environment variables.

Usage

LS-MolGen includes three sub-modules:

1. pre_train.py: Used for pre-training on big dataset.

2. transfer_learning.py: Used for fine-tuning the pre-trained neural network on a small dataset of molecules with known bioactivity.

3. reinforcement_learning.py: Used for generating new molecules with high affinity and novelty using the reinforcement learning algorithm.

Example of running the command:

python pre_train.py

python transfer_learning.py

python reinforcement_learning.py

Credits

LS-MolGen was developed by Song Li as part of a research project at Shanghai Jiao Tong University.

License

LS-MolGen is released under the Apache License. See the LICENSE file for details.

songlee@sjtu.edu.cn