{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "Shiv Nadar University, Delhi-NCR"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "name": "C. S. Krishna"
    },
    {
      "name": "H. Singh"
    },
    {
      "name": "S. P. Srivastava"
    },
    {
      "affs": [
        0
      ],
      "name": "R. K. SINGH"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/20anxy469db76.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.17.759787",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.17.759787v1.full",
  "id": "20anxy469db76",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.17.759787v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1312|vaccines and immunoinformatics approaches"
    ]
  ],
  "openalex": "W7223242690",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.17.759787v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-17",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/20anxy469db76.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:29:14Z",
      "native_id": "10.64898/2026.10.17.759787",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.17.759787v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Active Learning framework for Antibody Binding Affinity Prediction",
  "title_text": "Active Learning framework for Antibody Binding Affinity Prediction",
  "type": "preprint",
  "updated_at": "2026-10-30T12:29:14Z",
  "url": "https://research.draconitic.com/paper/20anxy469db76",
  "venue": "bioRxiv",
  "versions": []
}