DrugCombPred - Drug Combination Prediction Models
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This repository contains deep learning models for predicting drug combination synergy scores.
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

Folder Structure:
-----------------
drugcombpred/
├── DrugCombDB/
│   ├── drug/
│   ├── cell/
│   └── predictor/
│       └── cross_validation.py
├── ONeil2016/
│   ├── drug/
│   ├── cell/
│   └── predictor/
│       └── cross_validation.py


Requirements:
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- Python 3.7+
- PyTorch
- CUDA (optional, for GPU support)

Install dependencies:
> pip install -r requirements.txt

Running the Models:
-------------------

Both models use the same script interface: cross_validation.py

Available arguments:
  --epoch      : Number of training epochs (default: 500)
  --batch      : Batch size (default: 256)
  --gpu        : GPU device ID to use (default: None)
  --patience   : Early stopping patience (default: 100)
  --suffix     : Suffix for output folder (default: current timestamp)
  --hidden     : Hidden layer sizes (default: 2048 4096 8192)
  --lr         : Learning rate(s) (default: 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

Output:
-------
Results and logs are saved in:
  OUTPUT_DIR/cv_<suffix>

NOTE: Make sure OUTPUT_DIR is defined or configurable in your script.

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