GATESynergy: integrating molecular global-local aggregator and hierarchical gene-gated encoder for drug synergy prediction

1. Overview

The code for paper "GATESynergy: integrating molecular global-local aggregator and hierarchical gene-gated encoder for drug synergy prediction". The repository is organized as follows:

• data/ contains the datasets used in the paper;
• code/train.py contains training and testing code;
• code/process_data.py contains the preprocess of data;
• code/dataset.py contains the dataset construction and preprocessing for drug graph data.
• code/model.py contains GATESynergy's model layer;

2. Dependencies

• Python 3.9.12
• PyTorch 2.1.0 + CUDA 12.1
• torch-geometric 2.5.1
• NumPy 1.24.1
• Pandas 2.2.1
• SciPy 1.12.0
• scikit-learn 0.24.2
• RDKit 2023.9.3

3. Quick Start

Here we provide a example to predict drug synergy:

1. Download and upzip our data and code files
2. Run "train.py"

4. Contacts

If you have any questions, please email Ding Jiana (dingjn24@mails.jlu.edu.cn)

5. Workflow of GATESynergy