LS-MolGen

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

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

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

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

LS-MolGen includes three sub-modules:

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

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

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

Name		Name	Last commit message	Last commit date
Latest commit History 15 Commits
data		data
figure		figure
metrics		metrics
result		result
LICENSE		LICENSE
README.md		README.md
calc_metrics.py		calc_metrics.py
data_structs.py		data_structs.py
environment.yml		environment.yml
eval.ipynb		eval.ipynb
get_eval_results.pbs		get_eval_results.pbs
model.py		model.py
plot.ipynb		plot.ipynb
pre_train.py		pre_train.py
process_data.py		process_data.py
reinforcement_learning.py		reinforcement_learning.py
requirements.txt		requirements.txt
run.pbs		run.pbs
sample.py		sample.py
transfer_learning.py		transfer_learning.py
utils.py		utils.py