---
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](https://huggingface.co/IlPakoZ/DLRNA-BERTa9700).