---
language: en
license: mit
tags:
- biology
- protein-language-model
- peptide
- antimicrobial
- classification
- esm2
- lora
pipeline_tag: text-classification
base_model: facebook/esm2_t33_650M_UR50D
---

# AMP GenPept Binary Classifier (ESM-2 650M + LoRA)

Fine-tuned ESM-2 650M with LoRA for binary antimicrobial peptide (AMP) classification.

## Performance

- **F1**: 0.883 (88.3%)
- **Accuracy**: 0.868 (86.8%)
- **Benchmark**: GenPept-Curated-2025 (11K sequences, 80/20 split)
- **Training**: 5 epochs, A6000 48GB, ~40 min

## Usage

```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
from peft import PeftModel
import torch

model = AutoModelForSequenceClassification.from_pretrained("facebook/esm2_t33_650M_UR50D", num_labels=1)
model = PeftModel.from_pretrained(model, "null-phnix/amp-genpept-esm2-650m-lora")
tokenizer = AutoTokenizer.from_pretrained("facebook/esm2_t33_650M_UR50D")

def predict_amp(sequence: str) -> float:
    """Return AMP probability for a peptide sequence."""
    inputs = tokenizer(sequence, return_tensors="pt", truncation=True, padding="max_length", max_length=200)
    with torch.no_grad(): logits = model(**inputs).logits
    return torch.sigmoid(logits).item()

print(predict_amp("GLFDVIKKVAGALGSLVK"))
```

## Architecture

- Base: ESM-2 650M (33 transformer layers, 1280 hidden dim)
- Adapter: LoRA r=16, alpha=32, target_modules=["query","value"]
- Head: Single sigmoid output for binary classification

## Data

Trained on GenPept-Curated-2025, a balanced, leakage-free AMP benchmark.

## Links

- GitHub: https://github.com/Null-Phnix/amp-discovery
- Base model: https://huggingface.co/facebook/esm2_t33_650M_UR50D
- Dataset: https://github.com/biochem-data-sci/GenPept-Curated-2025