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---
title: Antibody Non-Specificity Predictor
emoji: 🧬
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: "5.0.0"
app_file: app.py
pinned: false
license: mit
tags:
- antibody
- protein
- ESM
- gradio
- polyreactivity
- machine-learning
---
# 🧬 Antibody Non-Specificity Predictor
Predict antibody polyreactivity (non-specificity) from Variable Heavy (VH) or Variable Light (VL) sequences using ESM-1v protein language models.
## Model
- **Architecture:** ESM-1v (650M parameters) + Logistic Regression
- **Training Data:** Boughter dataset (914 antibodies, ELISA polyreactivity)
- **Methodology:** Sakhnini et al. (2025) - Prediction of Antibody Non-Specificity using PLMs
## Usage
1. Paste your antibody VH or VL amino acid sequence
2. Click "🔬 Predict Non-Specificity"
3. Get prediction (specific vs non-specific) + probability
## Supported Input
- **Valid characters:** Standard amino acids (ACDEFGHIKLMNPQRSTVWY)
- **Max length:** 2000 amino acids
- **Auto-cleaning:** Lowercase automatically converted to uppercase
## Examples
The app includes example sequences:
- Standard VH (128aa)
- Standard VL (107aa)
- Short VH (Herceptin-like)
## Citation
If you use this tool in your research, please cite:
```bibtex
@article{sakhnini2025antibody,
title={Prediction of Antibody Non-Specificity using Protein Language Models},
author={Sakhnini, et al.},
year={2025}
}
```
## Repository
Full source code: [antibody_training_pipeline_ESM](https://github.com/The-Obstacle-Is-The-Way/antibody_training_pipeline_ESM)
## License
MIT License - See repository for details
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