This SFM learns a joint embedding space for drug molecules (SMILES) and target protein sequences via contrastive learning on binding-affinity data. Given a drug, retrieve the most likely protein targets — or score drug–target pairs directly.

Component	Model
Agent encoder	MoLFormer-XL (SMILES)
Target encoder	ESM-2 (protein language model)
Training data	DAVIS + KIBA + BindingDB (10,338 drugs / 1,358 targets; 121,548 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 (%)
drug → target	27.7 ± 3.3	42.6 ± 2.2	46.7 ± 1.9
target → drug	60.5 ± 8.7	70.3 ± 8.8	74.9 ± 8.2

Paper fold-0 pool-512 R@1 (drug→target) = 23.3%. Note: in the paper, dtSFM is the one domain where OOD retrieval exceeds in-distribution (drug→target R@1 = 46.0% at mmseqs_080), a signature predicted by the molecular-recognition framework.

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/dtSFM_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("CC1=CC=C(C=C1)S(=O)(=O)N")    # drug SMILES
target_emb = model.encode_target("MTEYKLVVVGAGGVGKSALTIQLIQNHFV...")  # target protein

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