PeptEdgeV2

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.

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:

Files

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