SDCInterpreter

This repository contains the code and data for "Path-based graph neural network for drug synergy prediction and interpretation".

Overview

Requirements

• Please follow the links below to install PyTorch and DGL with proper CUDA versions
  • PyTorch https://download.pytorch.org/whl/torch_stable.html
  • DGL https://data.dgl.ai/wheels/cu116/repo.html
• Our code has been tested with
  • python == 3.7.16
  • numpy == 1.21.6
  • pandas ==1.3.5
  • pytorch == 1.13.1+cu116
  • dgl == 1.1.2+cu116
  • packaging == 23.1
  • pyYAML == 6.0.1
  • matplotlib == 3.5.3
  • scikit-learn ==1.0.2

The repository is organized as follows

• Data/ contains the datasets used in the SDCInterpreter model;
• Model/ contains the implementation of the SDCInterpreter model;
• Utils/ contains the universal tool functions for prediction and interpretation;

---

Usage

# predict drug synergy
python train.py
# interpret synergy
python interpret.py

Results

Quantitative

• Evaluate saved explanations

python valid_path.py
python eval_explanations.py --emb_dim=64 --hidden_dim=64 --out_dim=64 

Concat

Author: Shuo Wang Mail: 2023120666@mail.scuec.edu.cn

Corresponding author: Xian-gan Chen Mail: chenxg@mail.scuec.edu.cn

Date: 2025-12-5

School of Biomedical Engineering, South-Central Minzu University, China

Feel free to cite this work if you find it useful to you !

@article{SDCInterpreter,
    title = Path-based graph neural network for drug synergy prediction and interpretation,
    author = {Shuo Wang, Hongchuan Yuan, Zhengcheng Hong, Xian-gan Chen, Xiaofei Yang},
    year = {2026},
    volume = {66},
    issue = {1},
    page = {337-348},
    journal = {Journal of Chemical Information and Modeling},
    doi = {10.1021/acs.jcim.5c02569},
}