{
  "abstract": "Antibodies recognized as foreign can elicit anti-drug antibodies that compromise efficacy and safety, making humanness scoring, estimating how human-like a sequence appears, an early, repeated step in antibody development. Widely used scores reduce a variable domain to a single framework-dominated number, leaving the complementarity-determining regions (CDRs) grafted during humanization, diversified by generative models weakly supervised and rarely audited. Here we introduce IgScope, a 23-million-parameter antibody language model trained from scratch that scores humanness at chain, IMGT-region, and residue resolution; a region-isolated attention module confines attention to within-region pairs, and chimeric-sequence training supplies region-level supervision. On 2.38 million held-out chains, masking all framework tokens leaves CDR accuracy unchanged, and back-mutated murine residues in human frameworks are detected at 93.4% accuracy. At chain level, IgScope matches the strongest published correlation with clinical anti-drug-antibody incidence (r = 0.567, n = 217) and outperforms a same-protocol recomputation. Region-level scoring exposes what chain-level scoring conceals: antibodies designed de novo by four generative models pass whole-chain humanness (0.966-0.988), while their CDRs score far below natural (P < 1e-30) in every chain class. Quality control turns the same resolution to the mirror-image question: in humanized-mouse platforms, whose domains are human by design, IgScope scores the domain as human and, in HUGO-Nano chimeric VHHs, isolates only the FR2 signal of four camelid hallmark residues deliberately retained to stabilize the VHH. IgScope gives antibody humanization a verified foundation and generative design an audit.",
  "abstract_shown": true,
  "affiliations": [
    "School of Pharmacy, Faculty of Medicine & Laboratory of Drug Discovery from Natural Resources and Industrialization, Macau University of Science and Technology,"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "H. Liu"
    },
    {
      "name": "P. Chen"
    },
    {
      "name": "S. Zhou"
    },
    {
      "name": "B. Wei"
    },
    {
      "name": "S. Deng"
    },
    {
      "name": "L. Zhang"
    },
    {
      "affs": [
        0
      ],
      "name": "X. Liu"
    },
    {
      "name": "S. Zhou"
    },
    {
      "name": "L. Han"
    },
    {
      "name": "L. Ye"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1defp6s1pfc3a.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.23.761395",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.23.761395v1.full",
  "id": "1defp6s1pfc3a",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.23.761395v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "2741|Monoclonal and Polyclonal Antibodies Research"
    ]
  ],
  "openalex": "W7224085232",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.23.761395v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1defp6s1pfc3a.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:30:16Z",
      "native_id": "10.64898/2026.10.23.761395",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.23.761395v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Multi-resolution humanness scoring of antibodies via a region-isolated language model",
  "title_text": "Multi-resolution humanness scoring of antibodies via a region-isolated language model",
  "type": "preprint",
  "updated_at": "2026-10-31T00:20:57Z",
  "url": "https://research.draconitic.com/paper/1defp6s1pfc3a",
  "venue": "bioRxiv",
  "versions": []
}