This SFM learns a joint embedding space for enzyme protein sequences and substrate small molecules (SMILES) via contrastive learning on reaction data. Given an enzyme, retrieve its most likely substrate — or score enzyme–substrate pairs directly.

Component	Model
Agent encoder	ESM-2 (protein language model)
Target encoder	MoLFormer-XL (SMILES)
Training data	ReactZyme (177,389 enzymes / 2,646 substrates; 177,442 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 (%)
enzyme → substrate	86.3 ± 1.5	95.3 ± 1.0	97.1 ± 0.8
substrate → enzyme	61.8 ± 0.4	88.2 ± 0.2	93.5 ± 0.5

Released checkpoint = fold 0; per-fold values are not separately tabulated in the paper (mean reported).

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/eSFM_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("MKALLIVLGLVSSVSQASST...")   # enzyme protein
target_emb = model.encode_target("OC(=O)c1ccccc1")            # substrate SMILES

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