DeepDTAF is a deep learning architecture, which integrates local and global features to predict the binding affinity between ligands and proteins.
The benchmark dataset can be found in ./data/
. Data preprocessing can be referred to ./prepare/
. The DeepDTAF model is available in ./src/
. And the result will be generated in ./runs/
. See our paper for more details.
- python 3.7
- cudatoolkit 10.1.243
- cudnn 7.6.0
- pytorch 1.4.0
- numpy 1.16.4
- scikit-learn 0.21.2
- pandas 0.24.2
- tensorboard 2.0.0
- scipy 1.3.0
- numba 0.44.1
- tqdm 4.32.1
The easiest way to install the required packages is to create environment with GPU-enabled version:
conda env create -f environment_gpu.yml
conda activate DeepDTAF_env
Then, install the apex in the DeepDTAF_env environment:
git clone https://github.com/NVIDIA/apex
cd apex
python setup.py install
Since the codes for apex package in the above website have been updated, you can also install it using the uploaded package. The apex.tar can be found in ./src/
.
to train your own model
cd ./src/
python main.py
to see the result
tensorboard ../runs/DeepDTAF_<datetime>_<seed>/
Wang K, Zhou R, Li Y, et al. DeepDTAF: a deep learning method to predict protein–ligand binding affinity. Briefings in Bioinformatics, 2021, 22(5), bbab072.
Kaili Wang: kailiwang@dhu.edu.cn