csung7 / DeepDTAF

a deep learning architecture for protein-ligand binding affinity prediction

About DeepDTAF

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/. The DeepDTAF model is available in ./src/. And the result will be generated in ./runs/. See our paper for more details.

Requirements:

• 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

Training & Evaluation

to train your own model

cd ./src/
python main.py

to see the result

tensorboard ../runs/DeepDTAF_<datetime>_<seed>/

contact

Kaili Wang: [e-mail]