{
  "abstract": "T cell receptor (TCR) recognition prediction and receptor generation are traditionally modelled separately, leaving vast TCR sequence collections disconnected from smaller TCR-peptide-MHC datasets. Here we present OmniTCR, a 113-million-parameter autoregressive foundation model pretrained on 328 million formatted human immune-sequence records. Sequence-type tokens and complementary component orders enable joint learning from individual TCR chains and partial or complete TCR-pMHC associations. On unseen epitopes, OmniTCR achieved AUPRCs of 0.7009 for peptide-TCR{beta}; recognition and 0.8235 for TCR-pMHC interaction prediction, exceeding the strongest evaluated comparators by 0.3396 and 0.3451, respectively. It distinguishes cancer from healthy repertoires across 11 independent pan-cancer cohorts (mean AUROC, 0.9436). The model achieved the highest sequence recovery on internal and external generation benchmarks. Structural modelling supported the plausibility of selected pMHC-conditioned CDR3{beta}; candidates. OmniTCR bridges heterogeneous immune sequence data, providing a foundation for computational immunology and receptor design.",
  "abstract_shown": true,
  "affiliations": [
    "West China Biomedical Big Data Center, West China Hospital, Sichuan University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "F. Zeng"
    },
    {
      "name": "D. Feng"
    },
    {
      "name": "D. Song"
    },
    {
      "name": "L. Ding"
    },
    {
      "name": "Z. Tan"
    },
    {
      "name": "Q. Lei"
    },
    {
      "name": "W. Lei"
    },
    {
      "affs": [
        0
      ],
      "name": "A.-Y. Guo"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bmq6y3jaae34m.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.10.750588",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.10.750588v1.full",
  "id": "bmq6y3jaae34m",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.10.750588v1",
  "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": "W7212456586",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.10.750588v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bmq6y3jaae34m.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:21:54Z",
      "native_id": "10.64898/2026.09.10.750588",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.10.750588v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "OmniTCR: a foundation model unifying T cell receptor recognition prediction and conditional sequence generation",
  "title_text": "OmniTCR: a foundation model unifying T cell receptor recognition prediction and conditional sequence generation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:21:54Z",
  "url": "https://research.draconitic.com/paper/bmq6y3jaae34m",
  "venue": "bioRxiv",
  "versions": []
}