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# 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](./Images/DrugCombPred1.png)

- **Model on DrugCombDB Dataset**
![DrugCombDB Model](./Images/DrugCombPred2.png)

---

## βš™οΈ Requirements

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

Install dependencies:
```bash
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
```bash
cd DrugCombDB/predictor
python cross_validation.py --epoch 300 --batch 128 --gpu 0 --suffix drugcomb_test
```

### Example: Run O'Neil 2016 Model
```bash
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:

- **DrugCombDB**:
Zagidullin et al., *Nucleic Acids Research*, 2019
[https://doi.org/10.1093/nar/gky1144](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](https://doi.org/10.1016/j.cels.2016.08.015)

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DrugCombPred - Drug Combination Prediction Models
=================================================

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/
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


Requirements:
-------------
- 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

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