S2DTA is a graph-based deep learning model for predicting drug-target affinity (DTA) by fusing sequence and structural knowledge.
The benchmark dataset can be found in ./data/. The pretrained models are available in ./data/pretrained_models/. The S2DTA models is in ./. For more details, please read our paper.
- python 3.9
- numpy 1.24.0
- biopython 1.8
- torch 1.13.1
- cudnn 11.6
- scikit-learn 1.2.0
- scipy 1.9.3
- pandas 1.5.2
- torch-geometric 2.2.0
In this module you have to provide .pdb file for protein and pocket, .sdf or .mol file for compund.
If you want to training the model with your data, you should running
python create_data.py
python training.py.
If you only use our model to evaluat your own data, you need to run
python create_data.py
python run_pretrained_model.py.
Xin Zeng: hbzengxin@163.com