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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

Figure2