GenScore

GenScore is a generalized protein-ligand scoring framework extended from RTMScore, and it exhibits balanced scoring, ranking, docking and screening powers on multiple datasets.

Requirements

mdanalysis==2.0.0
pandas==1.0.3
prody==2.1.0
python==3.8.11
pytorch==1.11.0
torch-geometric==2.0.3
torch-scatter==2.0.9
rdkit==2021.03.5
openbabel==3.1.0
scikit-learn==0.24.2
scipy==1.6.2
seaborn==0.11.2
numpy==1.20.3
pandas==1.3.2
matplotlib==3.4.3
joblib==1.0.1

conda create --prefix xxx --file ./requirements_conda.txt      
pip install -r ./requirements_pip.txt

Datasets

PDBbind
CASF-2016
docking poses for DEKOIS2.0 and DUD-E
CSAR NRC-HiQ benchmark
Merck FEP benchmark
PDBbind-CrossDocked-Core

Examples for using the trained model for prediction

cd example

# input is protein (need to extract the pocket first)

python genscore.py -p ./1qkt_p.pdb -l ./1qkt_decoys.sdf -rl ./1qkt_l.sdf -gen_pocket -c 10.0 -e gt -m ../trained_models/GT_0.0_1.pth

# input is pocket

python genscore.py -p ./1qkt_p_pocket_10.0.pdb -l ./1qkt_decoys.sdf -e gatedgcn -m ../trained_models/GatedGCN_0.5_1.pth

# calculate the atom contributions of the score

python genscore.py -p ./1qkt_p_pocket_10.0.pdb -l ./1qkt_decoys.sdf -e gatedgcn -ac -m ../trained_models/GatedGCN_ft_1.0_1.pth

# calculate the residue contributions of the score

python genscore.py -p ./1qkt_p_pocket_10.0.pdb -l ./1qkt_decoys.sdf -e gatedgcn -rc -m ../trained_models/GatedGCN_ft_1.0_1.pth