---
title: GenScore
canonical_url: "https://www.modelscope.cn/models/Peekaboozzz/GenScore"
md_url: "https://www.modelscope.cn/models/Peekaboozzz/GenScore.md"
repository: Peekaboozzz/GenScore
last_updated: 2026-10-21
license: mit
language:
  - zh
  - en
downloads: 0
stars: 0
tags:
  - biology
  - biosciences
  - official-source-migration
  - source-code
---

# GenScore

> GenScore - Peekaboozzz 在 ModelScope 开源的模型。GenScore official-source migration with validation evidence; weights not included

Peekaboozzz/GenScore 是 ModelScope 魔搭社区上的机器学习模型，采用 mit 许可。

- **Repository**: Peekaboozzz/GenScore
- **License**: mit
- **Tags**: biology, biosciences, official-source-migration, source-code
- **Downloads**: 0
- **Stars**: 0
- **Last updated**: 2026-10-21

Source: https://www.modelscope.cn/models/Peekaboozzz/GenScore

---

# 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.

<div align=center>
<img src="https://github.com/sc8668/GenScore/blob/main/zzz-3.jpg" width="600px" height="300px">
</div> 



### 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](http://www.pdbbind.org.cn)       
[CASF-2016](http://www.pdbbind.org.cn)    
[docking poses for DEKOIS2.0 and DUD-E](https://www.zenodo.org/record/6859325)   
[CSAR NRC-HiQ benchmark](http://www.csardock.org/)    
[Merck FEP benchmark](https://github.com/MCompChem/fep-benchmark)   
[PDBbind-CrossDocked-Core](https://www.zenodo.org/record/5525936)         

### 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
```