12Deep Learning
Drug-Protein Interaction Prediction System
An AI-powered system that predicts drug-protein interactions using deep learning with attention mechanisms to accelerate drug discovery.
Project preview01 / 01
The idea behind it
An AI-powered system for predicting drug-protein interactions using deep learning. It combines molecular analysis, protein feature extraction, and attention-based neural networks to predict binding likelihood between drugs and proteins. The system features a comprehensive web interface for real-time predictions, 3D molecular visualization, and detailed biochemical analysis, designed to assist researchers in drug discovery and development by identifying potential drug targets and assessing binding interactions.
What it does
- AttentionDTI model with character-level embeddings and CNN layers
- Real-time drug-protein binding prediction with confidence scores
- Interactive 3D visualization of proteins and drug molecules
- Comprehensive protein analysis with amino acid composition and biochemical properties
- Molecular descriptor calculation for drugs (LogP, molecular weight, fingerprints)
- UniProt and AlphaFold database integration for protein structures
- REST API for programmatic access and batch processing
- SMILES string processing and validation
Challenges & solutions
- Implementing attention mechanisms for cross-modal drug-protein feature fusion
- Processing and validating complex SMILES strings and protein sequences
- Integrating multiple databases (UniProt, AlphaFold) for comprehensive analysis
- Optimizing model performance for real-time inference
- Balancing model complexity with prediction accuracy and speed
Outcomes & impact
- Successfully trained AttentionDTI model on Davis, DrugBank, and KIBA datasets
- Deployed dual-interface system (Streamlit UI + FastAPI)
- Achieved real-time predictions with confidence scoring
- Developed at Watson Codefest 2024
- Integrated 25+ protein features and comprehensive molecular descriptors