---
license: other
license_name: sfm-research-preview-license-v1.0
license_link: LICENSE.md
tags:
  - immunology
  - mhc
  - peptide
  - antigen-presentation
  - vc-sfm
  - biology
  - bioinformatics
pipeline_tag: feature-extraction
---

# mhcSFM — Peptide ↔ MHC Class I Binding Specificity Foundation Model

**Paper:** Vibe Coding Specificity Foundation Models · doi: [10.64898/2026.06.04.730134](https://doi.org/10.64898/2026.06.04.730134)
**All VC-SFM models:** [huggingface.co/SFM-BIIE-ETHZ](https://huggingface.co/SFM-BIIE-ETHZ)
**Code:** [github.com/SFM-BIIE-ETHZ/Vibe-Coding-SFMs](https://github.com/SFM-BIIE-ETHZ/Vibe-Coding-SFMs)


---

## What it does

This SFM learns a joint embedding space for **peptides** and **MHC class I alleles** via contrastive learning on binding-affinity data. Given a peptide, retrieve the MHC alleles most likely to present it — pan-allele prediction without per-allele fine-tuning.

| Component | Model |
|-----------|-------|
| Agent encoder | ESM-2 |
| Target encoder | ESM-2 (34-aa HLA pseudo-sequence) |
| Training data | NetMHCpan 4.1 binding-affinity corpus (38,837 peptides / 105 alleles; 168,710 pairs) |
| Split | Sequence-identity 100% holdout · fold 0 |

---

## Performance — pool-512 retrieval (from the paper)

Evaluated by **pool-512 retrieval**: each test pair's true target is placed in a pool
of 512 candidates (1 positive + 511 random negatives), scored by cosine similarity,
over 100 random trials at the best-validation checkpoint. Random baseline = 0.2%.
Values are the **5-fold cross-validated mean ± SD** (folds 0–3; fold 4 excluded for
split degeneracy) reported in the paper for this SFM. The released checkpoint is the
**fold-0, identity-100** model trained with the identical configuration, data, and split.


| Direction | R@1 (%) | R@5 (%) | R@10 (%) |
|-----------|---------|---------|----------|
| peptide → HLA | 65.1 ± 1.9 | 78.4 ± 1.6 | 81.8 ± 2.2 |
| HLA → peptide | 93.3 ± 2.6 | 99.1 ± 1.1 | 99.5 ± 0.6 |

*This released checkpoint is the original fold-0 model; its own pool-512 eval gives **66.1% (peptide→HLA) / 92.1% (HLA→peptide)** R@1, matching the paper fold-0 (64.6 / 95.0) within eval noise. On a held-out rare-allele (zero-shot) set, HLA→peptide R@1 = **95.4%**.*

---

## Quick start

```python
from huggingface_hub import hf_hub_download
import torch, torch.nn.functional as F

ckpt_path = hf_hub_download("SFM-BIIE-ETHZ/mhcSFM_VC-SFM", "model.pth")

# Load with the Vibe-Coding-SFMs codebase
# (https://github.com/SFM-BIIE-ETHZ/Vibe-Coding-SFMs)
from calm.encoder.model import CALMEncoder
model = CALMEncoder.from_pretrained(ckpt_path)
model.eval()

agent_emb  = model.encode_query("GILGFVFTL")                  # peptide
target_emb = model.encode_target("YFAMYQENVAQTDVDTLYIIYRDAQTFQV")  # HLA pseudo-seq

score = F.cosine_similarity(agent_emb, target_emb, dim=-1)
```

---

## Files in this repo

| File | Description |
|------|-------------|
| `model.pth` | Released checkpoint · fold 0 · identity-100 split |
| `results_train_val_test.csv` | Per-epoch training/validation/test logs (training-time batch metrics, **not** the pool-512 numbers above) |

---

## Citation

```bibtex
@article{reddy2026vcsfm,
  title   = {Vibe Coding Specificity Foundation Models},
  author  = {Reddy, Sai T.},
  journal = {bioRxiv},
  year    = {2026},
  doi     = {10.64898/2026.06.04.730134}
}
```


## License

Released under the **SFM Research Preview License v1.0-preview** (see `LICENSE.md`).
Free for research use — academic, non-profit, government, and industry research. The specific
molecules disclosed in the accompanying preprints are dedicated to the public. Commercial-use
and patent-licensing terms are deferred and being arranged with ETH Zürich / BIIE; the SFM
architectures and training methods are the subject of pending patent applications.
For commercial enquiries: sai.reddy@ethz.ch