PharmaPredict is a cutting-edge deep learning model for predicting Drug-Drug Interactions (DDIs) based on molecular structural information. This project aims to provide accurate predictions of potential interactions between different drugs, helping healthcare professionals make informed decisions about drug combinations.
Drug-Drug Interactions (DDIs) occur when two or more drugs interact with each other in a way that affects the effectiveness or safety of one or both drugs. Predicting DDIs is crucial for ensuring patient safety and optimizing drug therapies. PharmaPredict uses advanced machine learning techniques to analyze the molecular structures of drugs and predict potential interactions.
- Accurate prediction of 86 different types of DDIs.
- Utilizes Simplified Molecular Input Line Entry System (SMILES) notation for drug representation.
- Hyperparameter tuning for optimizing model performance.
- User-friendly Streamlit web application for interactive DDI prediction.