Multisyn: Accurate prediction of synergistic drug combination using a multi-source information fusion framework

This repository contains the official implementation of our paper:
Multisyn integrates pharmacophore structure, protein-protein interaction (PPI) networks, and cell line omics to predict synergistic anti-cancer drug combinations.

You can find full documentation here: https://HuazeLoong.github.io/MultiSyn/

1. Introduction

Multisyn represents molecules as heterogeneous molecular graphs and predicts drug combination synergy using graph neural networks.
It provides substructure-level attention and integrates multi-source data, including PPI and cell lines omics profiles.

Paper Link: Coming soon...

1.1 Features

• Drug heterogeneous molecular graph construction based on BRICS fragments
  
• Dual-view cell line integration: expression + PPI fusion features
  
• Multi-modal attention-based GNN architecture
  

1.2 File Structure

text multisyn/ ← Project root directory ├── setup.py ← Packaging and installation configuration ├── requirements.txt ← Dependency management ├── README.md ← Project description └── src/ └── multisyn/ ← Python package (contains all core source code) ├── __init__.py ├── model.py ├── train.py ├── utils.py ├── dataset.py ├── const.py └── prepare_data.py

1.3 Citation

If you find this repository helpful, please cite our work:

```bibtex

```

2. Usage

2.1 Requirements

We recommend the following Python environment: ```bash

---- Core Deep Learning Framework ----

torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0

⚠ torch-scatter must match your PyTorch and CUDA version.

Manual installation is recommended (see notes below).

---- GNN Packages ----

torch-geometric==2.4.0 dgl==1.1.2 # or dgl==1.1.2+cu118 depending on your CUDA version

---- Chemistry Toolkit ----

rdkit==2022.9.5 # from conda or RDKit wheels

---- ML + Data Processing ----

scikit-learn>=1.2.0 numpy>=1.24.0 pandas>=1.3.0 scipy>=1.7.0

---- Optional Utilities ----

tqdm matplotlib ```

Install core dependencies using:

bash pip install -r requirements.txt

Notes on Specific Dependencies

⚠ torch-scatter torch-scatter requires a PyTorch- and CUDA-matching build. Use the following command to install a compatible version: bash pip install torch-scatter -f https://data.pyg.org/whl/torch-2.1.0+cu118.html You can find more options at: PyG Installation Guide

⚠ rdkit rdkit is not available on PyPI; it is recommended to install via conda: bash conda install -c rdkit rdkit==2022.9.5

2.2 Preprocessing

To preprocess the drug combination dataset:

bash python prepare_data.py

Processed files will be saved to multisyn\datas\processed.

2.3 Train the Model

To train the Multisyn model: bash python train.py Results will be saved to the multisyn\datas\results directory.