This code repository is the supporting material in the paper. In this paper, we propose a novel approach called GTextSyn, which leverages the integration of chemical structure data and gene expression data to predict the synergistic effects of drug combinations.

The third-party dependencies required for model running are listed in environment.yaml. Specifically, you can use the following command to create an environment based on conda and pip:

conda create -n GTextSyn python=3.7
conda activate GTextSyn
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
conda install -c dglteam/label/cu117 dgl
conda install -c conda-forge rdkit==2018.09.3
pip install dgllife

All data used in this paper are public and accessible. The relevant dataset has been stored in Cloud Drive and can be downloaded to the ./data/raw/ folder. Please refer to the DATA README for the source of each file.

After downloading the relevant dataset and place it in the ./data/raw/ folder you can generate the training set and test set by running

python dataproc.py

After generating the traning set and test set (ONEIL_train.pkl、ONEIL_test.pkl) you can start training the model by running

python main.py