Drug-Drug Interaction Prediction Using Graph Analytics

This project predicts drug-drug interactions (DDIs) using graph analytics and machine learning. It leverages data from DrugBank, PubChem, and STRING to build a heterogeneous graph and uses Graph Neural Networks (GNNs) for prediction.

Repository Structure

• data/: Contains raw and processed datasets.
• notebooks/: Jupyter notebooks for exploration and prototyping.
• src/: Source code for data preprocessing, graph construction, and model training.
• frontend/: Streamlit app for user interaction.
• models/: Trained models and weights.

Installation

1. Clone the repository:

   git clone https://github.com/chintu2781/drug-drug-interaction.git