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Readme.md

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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
+```
+
+---
+
+## 📂 Folder Structure
+
+```
+drugcombpred/
+├── DrugCombDB/
+│   ├── drug/
+│   ├── cell/
+│   └── predictor/
+│       └── cross_validation.py
+├── ONeil2016/
+│   ├── drug/
+│   ├── cell/
+│   └── predictor/
+│       └── 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)
+
+---
+
+## ⚙️ Requirements
+
+- Python 3.7+
+- PyTorch
+- CUDA (optional, for GPU support)
+
+Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+---
+
+## 🚀 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
+```
+
+---
+
+## 📁 Output
+
+Results and logs are saved in:
+```
+OUTPUT_DIR/cv_<suffix>
+```
+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](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)

Readme.txt

@@ -1,75 +0,0 @@
-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/
-├── DrugCombDB/
-│   ├── drug/
-│   ├── cell/
-│   └── predictor/
-│       └── cross_validation.py
-├── ONeil2016/
-│   ├── drug/
-│   ├── cell/
-│   └── predictor/
-│       └── 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