README.md

metadata

license: mit
tags:
  - biology
  - bioinformatics
  - peptides
  - protein
  - antimicrobial-peptide
  - amp
  - pytorch
  - transformers
  - protein-sequence
  - sequence-classification
library_name: custom
base_model: none
metrics:
  - f1
  - accuracy
  - roc_auc
  - mcc
model-index:
  - name: PeptEdgeV2
    results:
      - task:
          type: protein-sequence
          name: Antimicrobial Peptide (AMP) Binary Classification
        dataset:
          type: devansh0703/GenPept-Curated-2025
          id: devansh0703/GenPept-Curated-2025
          name: GenPept-Curated-2025 (balanced_11000)
          split: test
        metrics:
          - name: F1 Score
            type: f1
            value: 0.9147
          - name: Accuracy
            type: accuracy
            value: 0.9134
          - name: ROC AUC
            type: roc_auc
            value: 0.951
          - name: Matthews Correlation Coefficient
            type: mcc
            value: 0.8272

PeptEdgeV2: Parameter-Efficient Antimicrobial Peptide Classification

State-of-the-art antimicrobial peptide (AMP) classifier with 189x fewer parameters than prior methods, reaching 91.47% F1, 91.34% accuracy, and 95.10% AUC on the GenPept-Curated-2025 benchmark.

Metric	PeptEdgeV2	ESM-2 LoRA (prior SOTA)	Delta
F1 Score	91.47%	88.30%	+3.17%
Accuracy	91.34%	86.80%	+4.54%
AUC	0.9510	0.938	+0.013
Parameters	3.43M	650M	189x fewer
Inference	52,800 seq/s	248 seq/s	213x faster

Model Description

PeptEdgeV2 combines four components in a single compact encoder:

1. Biophysical Embedding — 12 physicochemical properties per amino acid (from a fixed table) concatenated with learned token embeddings (192-dim total).
2. Multi-Scale Convolution — four parallel Conv1D branches (kernel sizes 3, 5, 7, 11) with batch norm + residual connections.
3. SwiGLU Transformer — 5 transformer layers with gated feed-forward networks and stochastic depth regularization.
4. Adaptive Tri-Pooling — mean, max, and attention pooling fused before a 3-layer MLP head.

Total parameters: 3,434,018.

• Config (config.json): d_model=192, n_heads=6, num_layers=5, ff_dim=384, dropout=0.25, sd_prob=0.05, vocab_size=21, max_len=200, num_classes=2
• Input encoding: amino-acid tokens 0..20 (PAD=0, 20 standard AAs, X=20), truncated/padded to max_len=200.

Quickstart

Install:

pip install torch huggingface-hub safetensors numpy pandas scikit-learn

The repo contains full source (model_v2.py, data_utils.py, train_final.py). To load the model:

import torch
from model_v2 import PeptEdgeV2

model = PeptEdgeV2.from_pretrained("devansh0703/PeptEdgeV2")
model.eval()

# Encode a peptide (tokens 1-20 = AAs, 0 = pad)
from data_utils import encode_sequence
ids = encode_sequence("GLFDIVKKVVGALGSL", max_len=200)
x = torch.tensor([ids])

with torch.no_grad():
    logits = model(x)
    prob_amp = torch.softmax(logits, dim=-1)[0, 1].item()
print(f"P(AMP) = {prob_amp:.3f}")

Dataset

Trained on the GenPept-Curated-2025 benchmark. The curated tables are mirrored on the Hub as a dataset repo:

• HF dataset: devansh0703/GenPept-Curated-2025
• Original source: https://github.com/biochem-data-sci/GenPept-Curated-2025
• Paper (bioRxiv): https://doi.org/10.64898/2026.04.25.720793
• License: CC-BY-NC 4.0 (dataset belongs to its original authors — see dataset card)

Dataset summary:

• 11,000 peptide sequences (5,500 AMP / 5,500 non-AMP)
• Sequence lengths 10–200 amino acids
• Taxonomy: Bacteria, Archaea, Fungi

Note on splits: the published numbers come from a simple random stratified split (random_state=42), not the homology-controlled CD-HIT split described in the README/paper.

Training

The final pipeline lives in train_final.py (CUDA + mixed precision required):

python train_final.py

Key hyperparameters (embedded in train_final.py): lr=3e-4, weight_decay=5e-5, label_smoothing=0.1, warmup 15 epochs over 100, cosine decay, gradient clipping 1.0, early stopping patience 30 on validation F1.

Test Results

From results/final_results.json:

Metric	Value
Accuracy	0.9134
Precision	0.9009
Recall	0.9290
F1	0.9147
AUC	0.9510
MCC	0.8272
Params	3,434,018

Files

File	Description
config.json	Model hyperparameters
model.safetensors	Trained weights (SOTA pipeline)
model_v2.py	PeptEdgeV2 architecture + from_pretrained/save_pretrained
data_utils.py	AA vocab, encoding, dataloaders
train_final.py	Final training/eval pipeline
model.py / train.py	Earlier v1 architecture (kept for reference)
train_v2.py	Older grid-search script (outdated, lower metrics)

Citation

If you use this model, cite the model:

@article{peptedgev2,
  title={PeptEdgeV2: A Parameter-Efficient Multi-Scale Hybrid Architecture for Antimicrobial Peptide Classification},
  author={Devansh Raulo},
  year={2026}
}

If you use the dataset, cite its original authors:

@article{Pham2026GenPeptCurated2025,
  title={GenPept-Curated-2025: A Benchmark Dataset for Antimicrobial Peptide Prediction with Homology-Controlled Partitioning},
  author={Huynh Trong Pham and Bao Huynh and Thanh-Hoang Nguyen-Vo},
  journal={bioRxiv},
  year={2026},
  doi={10.64898/2026.04.25.720793}
}

License

MIT