About CGraphDTA

CGraphDTA is a fusion-based deep learning architecture for detecting drug-target binding affinity using target sequence and structure.

The benchmark dataset can be found in ./data/. The CGraphDTA model is available in ./src/. And the result will be generated in ./results/. See our paper for more details.

[IMPORTANT] We provide the input file in the release page. Please download it to ./data/.

Software and database requirement

To run the full version of CGraphDTA, you need to install the following three software and download the corresponding databases:
BLAST+ and UniRef90
HH-suite and Uniclust30
DSSP Mol2vec Besides, RDKit 2019.09.3 is also need to change the format of drugs.

Requirements:

• python 3.7.11
• pytorch 1.9.0
• scikit-learn 0.24.2
• dgl 0.9.1.post1
• tqdm 4.62.2
• ipython 7.27.0
• numpy 1.20.3
• pandas 1.3.2
• numba 0.53.1
• scipy 1.7.1
• einops 0.6.0
• loguru 0.6.0

Installation

In order to get CGraphDTA, you need to clone this repo:

git clone https://github.com/KailiWang1/CGraphDTA
cd CGraphDTA

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 CGraphDTA

Predict

to use our model

cd ./src/
python predict.py

Training

to train your own model

cd ./src/
python train.py

contact

Kaili Wang: [email protected]