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

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:

2. Usage

2.1 Requirements

We recommend the following Python environment:

# ---- 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:

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:

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:

conda install -c rdkit rdkit==2022.9.5

2.2 Preprocessing

To preprocess the drug combination dataset:

python prepare_data.py

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

2.3 Train the Model

To train the Multisyn model:

python train.py

Results will be saved to the multisyn\datas\results directory.