---
license: cc-by-nc-4.0
tags:
  - biology
  - bioinformatics
  - peptide
  - anti-aging
  - skin
  - protein
  - protbert
  - classification
library_name: pytorch
---

# iASAP-Fuse Weights

Pre-trained weights for **iASAP-Fuse**, a deep-learning model that predicts the
anti-skin-aging activity of peptides by fusing ProtBERT contextual embeddings
with engineered Z-scale physicochemical descriptors.

The companion code, CLI and local web UI are released as the Python package
**`iasapfuse`** on the project's GitHub repository.

## Model summary

- **Task**: binary classification (anti-skin-aging peptide / non anti-skin-aging peptide)
- **Backbone**: [Rostlab/prot_bert](https://huggingface.co/Rostlab/prot_bert) (frozen feature extractor)
- **Head**: fusion network combining ProtBERT [CLS] embeddings + Z-scale descriptors
- **Training**: 10-fold cross-validation, Stochastic Weight Averaging (SWA) per fold
- **Ensemble**: prediction-time average over 10 folds, calibrated with the saved `stats.json`

## Files

```
.
├── stats.json              # ensemble normalisation / threshold metadata
├── fold_1/
│   ├── best_swa.pt         # SWA model weights
│   └── metrics_final.json  # held-out fold metrics
├── fold_2/
│   ├── best_swa.pt
│   └── metrics_final.json
├── ...
└── fold_10/
    ├── best_swa.pt
    └── metrics_final.json
```

All `.pt` files are PyTorch state dicts intended to be loaded by
`iasapfuse.inference`.

## How to use

### Option 1 — via the `iasapfuse` CLI (recommended)

```bash
pip install iasapfuse  # or install from source
iasapfuse weights download \
  --repo-id YudoX/iASAP-Fuse-weights \
  --target-dir ./weights
iasapfuse predict examples/predict_sequences.csv --device cpu
```

### Option 2 — via `huggingface_hub`

```python
from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="YudoX/iASAP-Fuse-weights",
    local_dir="./weights",
    allow_patterns=["fold_*/*.pt", "fold_*/*.json", "stats.json"],
)
```

## License

The weights are released under **CC BY-NC 4.0** (Attribution–NonCommercial 4.0
International). Academic and non-commercial research use is permitted with
attribution. Commercial use requires separate permission from the authors.

## Citation

A formal BibTeX entry will be added once the paper is published.

## Disclaimer

These weights are intended for research only. They are **not** validated for
clinical, diagnostic, cosmetic-product or any production use.