DrugCombPred - Drug Combination Synergy Prediction

This project focuses on predicting drug combination synergy using deep learning. Combination therapies are a promising strategy in treating complex diseases like cancer, and accurate prediction of drug synergy can help identify effective combinations faster and more cost-effectively. Our models aim to support this effort by learning from large-scale pharmacogenomic datasets.

Two datasets are supported:

1. DrugCombDB
2. O’Neil et al. (2016)

Each dataset has its own folder and model implementation. The main training script is:

predictor/cross_validation.py

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📂 Folder Structure

drugcombpred/
├── DrugCombDB/
│   ├── drug/
│   ├── cell/
│   └── predictor/
│       └── cross_validation.py
├── ONeil2016/
│   ├── drug/
│   ├── cell/
│   └── predictor/
│       └── cross_validation.py

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🧠 Project Overview

The models process chemical structures (using RDKit), drug-target interactions, and cell line features (from gene expression and mutation data). Drug features are combined and passed into a neural network to predict synergy scores. Two architectures are used:

• Model on O’Neil Dataset
  

• Model on DrugCombDB Dataset
  

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⚙️ Requirements

• Python 3.7+
• PyTorch
• CUDA (optional, for GPU support)

Install dependencies:

pip install -r requirements.txt

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🚀 Running the Models

Both models use the same script interface: cross_validation.py

Arguments

Argument	Description	Default
--epoch	Number of training epochs	500
--batch	Batch size	256
--gpu	GPU device ID to use (None for CPU)	None
--patience	Early stopping patience	100
--suffix	Suffix for output folder	Current timestamp
--hidden	Hidden layer sizes (list of ints)	[2048, 4096, 8192]
--lr	Learning rate(s) to try (list of floats)	[1e-3, 1e-4, 1e-5]

Example: Run DrugCombDB Model

cd DrugCombDB/predictor
python cross_validation.py --epoch 300 --batch 128 --gpu 0 --suffix drugcomb_test

Example: Run O’Neil 2016 Model

cd ONeil2016/predictor
python cross_validation.py --epoch 300 --batch 128 --gpu 0 --suffix oneil_test

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📁 Output

Results and logs are saved in:

OUTPUT_DIR/cv_<suffix>

Make sure OUTPUT_DIR is defined or configurable in your script.

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📚 Citation

If you use this code or data in your research, please cite the original datasets:

• DrugCombDB:
  Zagidullin et al., Nucleic Acids Research, 2019
  https://doi.org/10.1093/nar/gky1144

• O’Neil et al., 2016:
  O’Neil et al., Cell Systems, 2016
  https://doi.org/10.1016/j.cels.2016.08.015