Integrating concept of pharmacophore with Graph Neural Networks for chemical property prediction and interpretation
This repository contains the source code and the data.
Dependencies:
- python 3.7
- pytorch = 1.7.1
- torch-cluster = 1.5.9
- torch-geometric = 1.7.2
- torch-scatter = 2.0.7
- torch-sparse = 0.6.9
- torch-spline-conv = 1.2.1
- RDkit = 2021.03.3
- numpy
- pandas
The data sets are provided as .csv files in a directory called 'data', including benchmark datasets and kinase datasets used in this work.
1.MyNet_Classification generates input, train and test classification models. For example,
python MyNet_Classification.py \
--epochs 100 \
--dataset BACE \
--model RGNN
2.MyNet_Regression generates input, train and test regression models. For example,
python MyNet_Regression.py \
--epochs 100 \
--dataset Lipo \
--model RGNN
Yue Kong
Aixia Yan