EGFR_DGMG

A pretrained model for molecules generation

In this repo, we show how to fine-tune a pre-trained model and build your own model for molecules generation.

For more information, click Here

Installation

Requirements

conda create my_dgl python=3.7
conda activate my_dgl
conda install pytorch torchvision torchaudio cpuonly -c pytorch
conda install -c rdkit rdkit==2018.09.3
conda install -c dglteam dgl
pip install dgllife

Verifying successful installation

import dgllife
import dgl
import torch
import rdkit

print(torch.__version__)
# 1.7.0
print(dgl.__version__)
# 0.6.1
print(rdkit.__version__)
# 2020.09.1
print(dgllife.__version__)
# 0.2.8

More information about installation, please check:

Install pytorch

Install dgl

Install dgllife

Install rdkit

Usage

step 1: Preprocessing your own data

Preprocessing additional data for DGMG model.

python preprocess.py -d EGFR -m ZINC -tf ./EGFR_data/EGFR_train.txt -vf ./EGFR_data/EGFR_val.txt

Step 2: Training or fine-tuning

Training or fine-tuning DGMG model for molecule generation.

The script will save model each 50 epochs!

python fine_tune.py -d EGFR -m ZINC -o canonical -tf ./EGFR_data/EGFR_DGMG_train.txt -vf ./EGFR_data/EGFR_DGMG_val.txt

Step 3: Generating molecules

Generate molecules with pretrained model or fine-tuned model.

Just use a pre-trained model:

python generate_mols.py

python generate_mols.py -m ZINC

Use a fine-tuning model

python generate_mols.py -d EGFR -p ./saved_model/EGFR/50_checkpoint.pth -s ./saved_model/EGFR/settings.txt

Cite

@article{,
    title={Discovery of Novel Epidermal Growth Factor Receptor (EGFR) Inhibitors Using Computational Approaches},
    author={Huo, Donghui;Wang, Shiyu;Kong, Yue;Qin, Zijian;Yan, Aixia},
    year={2022},
    journal={Journal of Chemical Information and Modeling}
}

Reference

DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science

Dgl:DEEP GRAPH LIBRARY