FGNN: Ligand binding affinity prediction with fusion of graph neural networks and 3D structure-based complex graph

FGNN is a novel deep fusion graph neural networks framework named FGNN to learn the protein–ligand interactions from the 3D structures of protein–ligand complexes.

More information is published in the paper.(https://pubs.rsc.org/en/content/articlelanding/2023/cp/d3cp03651k)

Usage of FGNN

After download FGNN, you need to do these firstly:

mkdir data/cache

mkdir data/data_cache

mkdir pdbbind2016/testset

1. Environment

conda env create -f environment-data.yml

conda env create -f environment-model.yml

2. Data preprocessing

conda activate data

python preprocess_pdbbind.py

3. Traing models

conda activate model

python train.py

4. Test and predict

conda activate model

python predict.py