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年09月3日 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