---
title: "Active learning with Tanimoto-kernel GPs for antibody binding affinity prediction"
id: "biorxiv-23-active-learning-framework-for-antibody-binding-affinity-prediction"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-23-active-learning-framework-for-antibody-binding-affinity-prediction"
content_type: "clinical_feed_article"
specialty: "Pharmacology"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.10.17.759787v1?rss=1"
published_at: "2026-10-22T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Active learning with Tanimoto-kernel GPs for antibody binding affinity prediction
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-23-active-learning-framework-for-antibody-binding-affinity-prediction
- **Specialty:** [Pharmacology](https://medichelpline.com/clinical-feed/pharmacology.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.10.17.759787v1?rss=1)
- **Published At:** 2026-10-22T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- The authors developed an active-learning benchmark using **Gaussian process** (GP) surrogates to predict antibody–antigen binding affinity from sequence encodings. - The framework embeds paired VH/VL sequences with **ESM-2 650M** and trains GP models on compressed protein-language-model representations. - Eight public AbBiBench assays were evaluated, covering influenza hemagglutinin antibodies CR6261 and CR9114 and AAYL-series scFv libraries against the SARS-CoV-2 Spike HR2 peptide. - The benchmark used a fixed held-out evaluation split, a label-blind seed, and a uniform labelling budget equal to 10% of each full pool. - Multiple kernel and acquisition function combinations were tested; investigators reported that Tanimoto+UCB produced the highest mean **Recall@2%** point estimate across tested configurations. - Reported mean **Recall@2%** for Tanimoto+UCB was 57.8% across five random seeds, rising to 68.3% when scored against the acquisition-eligible pool; random selection achieved 9.6% by comparison. - The authors performed statistical comparison with a paired Wilcoxon test across datasets and report a significant gap versus random selection (p=0.0078); they also noted Tanimoto+UCB was not significantly different from RQ+UCB or Tanimoto+EI at five seeds. - In influenza libraries, the configuration recovered large fractions of top binders (values reported in the abstract for 3gbn_h1, 3gbn_h9, 4fqi_h3). - The AAYL combinatorial landscapes (SARS-CoV-2 HR2 scFv libraries) produced lower and more variable recall, indicating harder optimisation targets in those datasets. - An additional representation ablation (three seeds, under the original protocol before evaluation corrections) ranked **ESM-2 650M** highest among tested encodings. - Structure-mapped Integrated Gradients and KernelSHAP attribution analyses recovered CDR-region patterns consistent with contact-derived paratope geometry, but threshold-free (AUROC) residue-level discrimination was not strong. - The code repository for the project is provided in the preprint footnotes: https://github.com/harshitsinghsnu/AntibodyAL. - This report is a preprint and has not been peer reviewed; the authors also note evaluation corrections and that some ablation runs were not repeated after those corrections.
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Chundru Sharath Krishna 1 Shiv Nadar University, Delhi-NCR, India; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Chundru%2BSharath%2BKrishna%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Krishna%20CS&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AChundru%2BSharath%2BKrishna%2B) Harshit Singh 2 Shiv Nadar University, Delhi-NCR * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Harshit%2BSingh%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Singh%20H&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3AHarshit%2BSingh%2B) * [ORCID record for Harshit Singh](http://orcid.org/0009-0005-9105-7559 "Open in new tab") Satya Pratik Srivastava 1 Shiv Nadar University, Delhi-NCR, India; * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=Satya%2BPratik%2BSrivastava%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=Srivastava%20SP&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ASatya%2BPratik%2BSrivastava%2B) RAJEEV KUMAR SINGH 2 Shiv Nadar University, Delhi-NCR * [Find this author on Google Scholar](https://www.biorxiv.org/lookup/google-scholar?link_type=googlescholar&gs_type=author&author%5B0%5D=RAJEEV%2B%2BKUMAR%2BSINGH%2B "Open in new tab") * [Find this author on PubMed](https://www.biorxiv.org/lookup/external-ref?access_num=SINGH%20R&link_type=AUTHORSEARCH "Open in new tab") * [Search for this author on this site](https://www.biorxiv.org/search/author1%3ARAJEEV%2B%2BKUMAR%2BSINGH%2B) * [ORCID record for RAJEEV KUMAR SINGH](http://orcid.org/0000-0003-1369-5377 "Open in new tab") * For correspondence: rajeev.kumar@snu.edu.in * [Abstract](https://www.biorxiv.org/content/10.64898/2026.10.17.759787v1)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_art/node:5894235/1) * [Info/History](https://www.biorxiv.org/content/10.64898/2026.10.17.759787v1.article-info)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_info/node:5894235/1) * [Metrics](https://www.biorxiv.org/content/10.64898/2026.10.17.759787v1.article-metrics)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_metrics/node:5894235/1) * [Data/Code](https://www.biorxiv.org/content/10.64898/2026.10.17.759787v1.external-links)[](https://www.biorxiv.org/panels_ajax_tab/article_tab_data_code/node:5894235/1) * [ Preview PDF](https://www.biorxiv.org/content/10.64898/2026.10.17.759787v1.full.pdf+html)[](https://www.biorxiv.org/panels_ajax_tab/biorxiv_tab_pdf/node:5894235/1) ![Loading](https://www.biorxiv.org/sites/all/modules/contrib/panels_ajax_tab/images/loading.gif) ## Abstract Antibody engineering campaigns must prioritise variants from large combinatorial libraries under limited assay budgets. We present a Gaussian process (GP) active-learning benchmark for antibody-antigen binding affinity prediction across eight public AbBiBench assays spanning influenza hemagglutinin antibodies CR6261 and CR9114 and AAYL-series scFv libraries against the SARS-CoV-2 Spike HR2 peptide. The framework embeds variable heavy and light (VH/VL) sequences with ESM-2 650M, trains GP surrogates over compressed protein-language-model representations, and evaluates kernel, acquisition, representation, classical featurisation, and labelling-budget choices under a unified budget of 10\% of each full pool with a label-blind seed and a fixed held-out evaluation split. Across the benchmark, Tanimoto+UCB has the highest mean Recall@2\% point estimate among tested kernel-acquisition configurations, achieving \textbf{57.8\%} mean Recall@2\% across five random seeds (68.3\% when scored against the acquisition-eligible pool), compared with 9.6\% for random selection -- a gap that is significant by a paired Wilcoxon test across datasets ($p=0.0078$); its point estimate is not significantly different from RQ+UCB or Tanimoto+EI at five seeds. We adopt the continuous Tanimoto kernel of \citet{gessner2024}. The same configuration recovers the largest fractions of top binders in the influenza libraries (74.2\% on \texttt{3gbn\\_h1}, 82.7\% on \texttt{3gbn\\_h9}, and 84.9\% on \texttt{4fqi\\_h3}) while exposing harder AAYL combinatorial landscapes where recall remains lower and more variable. An additional three-seed representation ablation, run under the original protocol and not repeated after the evaluation corrections, ranked ESM-2 650M highest (mean Recall@2\% 0.673 $\pm$ 0.28) among ProtBert, AntiBERTy, AbLang2, ProGen2, and classical encodings. On BindingGYM, the same configuration matches or exceeds ALLM-Ab on two of three datasets using the 100-sample held-out test metric. Structure-mapped Integrated Gradients and KernelSHAP analyses recover CDR-region attribution patterns consistent with contact-derived paratope geometry, though threshold-free (AUROC) analysis shows this qualitative agreement is not matched by strong residue-level discrimination. These results support Tanimoto-kernel GP active learning as a simple, reproducible strategy for label-efficient antibody affinity optimisation with qualitative structural validation. ### Competing Interest Statement The authors have declared no competing interest. ## Footnotes * Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a [CC-BY 4.0 International license](http://creativecommons.org/licenses/by/4.0/). bioRxiv and medRxiv thank the following for their generous financial support: > The Chan Zuckerberg Initiative, Cold Spring Harbor Laboratory, the Sergey Brin Family Foundation, California Institute of Technology, Centre National de la Recherche Scientifique, Fred Hutchinson Cancer Center, Imperial College London, Massachusetts Institute of Technology, Stanford University, The University of Edinburgh, University of Washington, and Vrije Universiteit Amsterdam. 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[ Download PDF](https://www.biorxiv.org/content/10.64898/2026.10.17.759787v1.full.pdf) Print/Save Options [Download PDF](https://www.biorxiv.org/content/biorxiv/early/2026/10/22/2026.10.17.759787.full.pdf)Full Text & In-line FiguresXML [More Info](https://www.biorxiv.org/about/FAQ#PrintOptions "More Information on Print/Save Options") [ Data/Code](https://www.biorxiv.org/content/early/2026/10/22/2026.10.17.759787.external-links) [ Email](https://www.biorxiv.org/ "Email this Article") [ Share](https://www.biorxiv.org/) Active Learning framework for Antibody Binding Affinity Prediction Chundru Sharath Krishna, Harshit Singh, Satya Pratik Srivastava, RAJEEV KUMAR SINGH bioRxiv 2026.10.17.759787; doi: https://doi.org/10.64898/2026.10.17.759787 This article is a preprint and has not been certified by peer review [[what does this mean?](https://www.biorxiv.org/about/FAQ#unrefereed)]. 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