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Readme.md

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

πŸ“‚ Folder Structure

drugcombpred/
β”œβ”€β”€ DrugCombDB/
β”‚   β”œβ”€β”€ drug/
β”‚   β”œβ”€β”€ cell/
β”‚   └── predictor/
β”‚       └── cross_validation.py
β”œβ”€β”€ ONeil2016/
β”‚   β”œβ”€β”€ drug/
β”‚   β”œβ”€β”€ cell/
β”‚   └── predictor/
β”‚       └── cross_validation.py

🧠 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
    O'Neil Model

  • Model on DrugCombDB Dataset
    DrugCombDB Model


βš™οΈ Requirements

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

Install dependencies:

pip install -r requirements.txt

πŸš€ 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

πŸ“ Output

Results and logs are saved in:

OUTPUT_DIR/cv_<suffix>

Make sure OUTPUT_DIR is defined or configurable in your script.


πŸ“š Citation

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