| --- |
| license: other |
| tags: |
| - protein |
| - antibody |
| - immunology |
| - foundation-model |
| - antibody-antigen |
| --- |
| |
| # 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](https://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| Two other repositories are **not** this model: `SFM-BIIE-ETHZ/abSFM-dev-archive` holds superseded |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 |
|
|
| ```bash |
| 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). |
|
|
