README_spaces.md

metadata

title: Drug-Target Interaction Predictor
emoji: 🧬
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 4.0.0
app_file: app.py
pinned: false
license: mit

Drug-Target Interaction Predictor

An interactive application for predicting drug-target interactions using deep learning. This model uses a novel cross-attention architecture to model the interactions between drug molecules (represented as SMILES) and target RNA sequences.

Features

• 🔮 Prediction Interface: Input RNA sequences and drug SMILES to get binding affinity predictions
• ⚙️ Model Management: Load and configure different model checkpoints
• 📊 Interpretability: Visualize attention weights to understand model decisions
• 🧬 Scientific Accuracy: Based on state-of-the-art deep learning architectures

How to Use

1. Load Model: Go to the "Model Settings" tab and specify the path to your trained model
2. Make Predictions:
   • Enter a target RNA sequence
   • Enter a drug SMILES string
   • Click "Predict Interaction" to get binding affinity score
3. Explore Examples: Try the provided examples to see the model in action

Model Architecture

The model combines:

• Target protein encoder for processing amino acid sequences
• Drug encoder for processing molecular SMILES representations
• Cross-attention mechanism to capture drug-target interactions
• Regression head for binding affinity prediction

Input Format

• Target Sequence: Standard amino acid single-letter codes (e.g., "AUGCUAGCUAGUACGUA...")
• Drug SMILES: Simplified Molecular Input Line Entry System notation (e.g., "CC(C)CC1=CC=C(C=C1)C(C)C(=O)O")

Example Usage

Try these example inputs:

• Target: AUGCGAUCGACGUACGUUAGCCGUAGCGUAGCUAGUGUAGCUAGUAGCU
• Drug: C1=CC=C(C=C1)NC(=O)C2=CC=CC=N2

Technical Details

• Built with Transformers and PyTorch
• Uses Gradio for the interactive interface
• Supports GPU acceleration when available
• Includes attention visualization for model interpretability

For more details, see the model documentation.