| --- |
| 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 |
|
|
