abSFM v1.0 — Antibody–Antigen Specificity Foundation Model

Reddy Lab — Lab for Systems & Synthetic Immunology — ETH Zurich / Botnar Institute of Immune Engineering (BIIE), Basel

abSFM scores antibody–antigen specificity. Given an antibody (VH/VL) and an antigen sequence it returns a binding-compatibility score and localises the paratope and the epitope — from sequence, without building a structure. Built on the binderSFM v2.5 protein–protein foundation (two frozen ESM-2 towers fused by a trainable cross-attention encoder).

Code, configs, evaluation and audit records: github.com/Reddy-BIIE-ETHZ/abSFM

Preprint-stage. The manuscript is in preparation; the orthogonal AI audit and the wet-lab validation of the SFM-display panel are still outstanding. Treat the numbers below accordingly.

This release

File	abSFM-v1.0.pth (55.9 MB)
SHA-256	8c732efb5c442ccb5349c0587f9d3619d09289c87fa35c057db9dcee7c61b3b7
Trainable parameters	13.96 M (cross-attention encoder + 3 heads; ESM-2 frozen)
Training corpus	8,499 Ab–Ag pairs, 1,213 antigen clusters (AbSD + SAbDab2 + IEDB), public data only
Training cost	~2 h on a single GPU, 10 epochs

Configs ship alongside the weights (config_model.yaml, config_data.yaml, config_train.yaml), so the checkpoint loads without the code repo.

Two other repositories are not this model: SFM-BIIE-ETHZ/abSFM-dev-archive holds superseded development checkpoints, and Reddy-BIIE-ETHZ/abSFM is an older personal-namespace copy. Verify the hash.

Quick start

hf download SFM-BIIE-ETHZ/abSFM abSFM-v1.0.pth --local-dir ./weights
shasum -a 256 weights/abSFM-v1.0.pth   # must match the SHA-256 above

git clone https://github.com/Reddy-BIIE-ETHZ/abSFM.git && cd abSFM && pip install -e .
python scripts/absfm/score_pairs.py \
  --antibodies my_abs.tsv --antigens my_antigens.fasta \
  --checkpoint weights/abSFM-v1.0.pth --out scores.tsv

Reading the score — read this before ranking anything

• Not comparable across antigens. Each antigen carries its own offset; whole-library median cosine ranged from −0.04 (IL-2) to +0.23 (HA). Rank within an antigen, or convert to a percentile against a fixed background library. An absolute cutoff across targets is meaningless.
• Don't take only the extreme tip. Across 7 antigens and ~21,000 genuinely held-out known binders, no antigen had a validated binder in its top 10 of 2.59 M. Enrichment peaks around ranks 50–500 and is flat there — sample a band.
• No headroom above real binders. For every antigen tested, the best validated binder scored below the median of our selected candidates. Pushing the score higher is extrapolation.
• Does not predict structural-verifier confidence (Spearman ρ ≈ −0.03 vs Boltz-2 iPTM). Use abSFM and a co-folder as independent filters, never combined into one ranking.
• Construct-specific. On full-length dengue E the best known DENV2 binder ranks 864,708 of 2.59 M; on the EDIII domain it ranks 1. Score the construct you will actually express, and prefer the epitope-bearing domain over a full ectodomain.

Performance (held-out)

Whole antibody-clonotype and antigen clusters are held out, then graded by novelty. R@10, antibody → antigen against a 507-antigen pool (chance 2.0%): 58.0% Class A (pair seen, memorisation ceiling), 14.8% B, 20.6% C (one partner novel), 8.0% D (both novel), 15% overall. Reverse direction, pool 678 (chance 1.5%): 12% overall. Paratope AUROC 0.950, epitope 0.746.

Known limitations

• Known-binder scores are inflated by memorisation (10×–2,500×, antigen-specific); any calibration on known binders must exclude training pairs or it measures recall.
• Large multi-domain antigens score and verify poorly — truncate to the epitope-bearing domain. Oligomerising the antigen does not help and generally hurts.
• The contact head is ~76% antigen-determined, so the predicted epitope is not usable as a binder filter on its own.
• Public training data only; behaviour on antigen classes absent from SAbDab/AbSD/IEDB is unknown.

Citation

Reddy ST. An Antibody–Antigen Specificity Foundation Model for Discovery and Engineering. In preparation (2026).