---
license: cc-by-4.0
tags:
  - peptide
  - generation
  - HELM
  - antimicrobial-peptide
  - drug-discovery
library_name: pytorch
pipeline_tag: text-generation
language:
  - en
---

# PepForge — Model Weights

Pre-trained model weights for [PepForge](https://github.com/wqx1999/PepForge), a hierarchical deep learning framework for generating peptides with special connections using HELM notation.

## Architecture

PepForge uses a **three-stage cascade** (Layout → Content → Connection) for generation and a **4-model MCC-weighted ensemble** for AMP activity prediction. The prediction ensemble was retrained 2026-04-28/29 on CLSI MIC-only DBAASP data with members selected by validation MCC (test set never consulted at the selection step).

### Generation Models

| Stage | File | Architecture | Test PPL / Metric |
|-------|------|-------------|-------------------|
| Layout | `Generation/Layout/260210_GPT.pt` | GPT (d=64, L=1) | PPL = 2.24 |
| Content (autoregressive, default) | `Generation/Content/GPT_L_260226.pt` | GPT (d=768, L=12) | PPL = 6.61 |
| Content (masked, infilling) | `Generation/Content/BERT_L_260301.pt` | BERT (d=768, L=12) | PPL = 9.15 |
| Connection | `Generation/Connection/GAT_L_260226.pt` | GAT (d=768, L=6) | Exist F1 = 0.971, Type Macro-F1 = 0.912 |

### Prediction Models — AMP Ensemble (260428/260429)

Each member is the best of its (encoding, model-type) quadrant by **validation MCC**.

| File | Type | Encoding | Test Acc | Test Macro-F1 | Test MCC | Weight (val MCC) |
|------|------|----------|---------:|--------------:|---------:|-----------------:|
| `Prediction/AMP/LSTM_L_260428_SMILES.pt` | LLM | SMILES | 0.7167 | 0.5663 | 0.5871 | **0.6121** |
| `Prediction/AMP/LSTM_M_260429_HELM.pt`   | LLM | HELM   | 0.7058 | 0.5811 | 0.5717 | **0.6021** |
| `Prediction/AMP/GCN_L_260429_HELM.pt`    | GNN | HELM   | 0.6355 | 0.5047 | 0.4844 | **0.5136** |
| `Prediction/AMP/GCN_L_260428_SMILES.pt`  | GNN | SMILES | 0.6165 | 0.4478 | 0.4630 | **0.4791** |

Held-out ensemble performance (test split, 2,206 samples; full report in `ensemble_test_eval.json`):

| Strategy | Acc | Macro-F1 | Weighted-F1 | MCC |
|----------|----:|---------:|------------:|----:|
| `soft_vote` (uniform 0.25 each)            | 0.7393 | 0.6049 | 0.7377 | 0.6175 |
| `weighted_vote` (val-MCC weights, **default**) | **0.7421** | **0.6092** | **0.7403** | **0.6216** |

The weighted ensemble exceeds the best single member (LSTM/L SMILES, MCC 0.5871) by +0.0345.

## Quick Start

```bash
git clone https://github.com/wqx1999/PepForge.git
cd PepForge
python install.py          # Installs env + downloads all models & data
```

```bash
# Generation + AMP prediction in one cascade call
python Pipelines/Inference.py --num_samples 100 --predict amp
```

For details, see the [GitHub repository](https://github.com/wqx1999/PepForge).

## File Structure

```
pepforge-model/
├── Generation/
│   ├── Layout/260210_GPT.pt              (534 KB)
│   ├── Content/GPT_L_260226.pt           (1.0 GB)
│   ├── Content/BERT_L_260301.pt          (1.0 GB)
│   ├── Connection/GAT_L_260226.pt        (606 MB)
│   └── MODEL_REGISTRY.md
└── Prediction/AMP/
    ├── ensemble_config.json
    ├── ensemble_test_eval.json
    ├── LSTM_L_260428_SMILES.pt           (812 MB, LLM, SMILES)
    ├── LSTM_M_260429_HELM.pt             (270 MB, LLM, HELM)
    ├── GCN_L_260429_HELM.pt              (545 MB, GNN, HELM)
    ├── GCN_L_260428_SMILES.pt            (1.3 GB, GNN, SMILES)
    └── MODEL_REGISTRY.md
```

Total size: ~5.5 GB

## Related Resources

- **Code**: [wqx1999/PepForge](https://github.com/wqx1999/PepForge)
- **Training data**: [pepforge-training-data](https://huggingface.co/datasets/qingxin1999/pepforge-training-data)
- **Generated library**: [pepforge-generated-data](https://huggingface.co/datasets/qingxin1999/pepforge-generated-data)
- **Figure data**: [pepforge-fig-data](https://huggingface.co/datasets/qingxin1999/pepforge-fig-data)

## Citation

```bibtex
@article{wang2026pepforge,
  title={PepForge: Hierarchical HELM-Based Peptide Generation},
  author={Wang, Qingxin and Süssmuth, Roderich D.},
  journal={bioRxiv},
  year={2026},
  doi={10.64898/2026.05.29.728379},
  url={https://www.biorxiv.org/content/10.64898/2026.05.29.728379v1}
}
```

## License

CC-BY-4.0