{
  "abstract": "Antimicrobial peptides (AMPs) often act against multiple pathogen classes, making multi-label activity prediction a more realistic screening target than binary antimicrobial classification. The ESCAPE benchmark formalizes this setting, but leading approaches typically rely on multimodal, structure-conditioned deep models that are costly to train and tune. We show that a simple, sequence-only pipeline can match and surpass these methods by combining 330 interpretable sequence descriptors with TabPFN, a tabular foundation model that performs in-context prediction in a single forward pass without gradient-based training or hyperparameter search. On ESCAPE (82,359 peptides; five labels), a label-powerset TabPFN model achieves mAP-5=77.8%, improving on the previously best reported 72.1%. A probabilistic classifier chain is the first method to match or exceed the best published average precision on each of the five labels simultaneously. The gains persist under the prior state-of-the-art single-fold training protocol, indicating they are not a training-set-size artefact, and are largest for remote homologues (+11.2 points below 30% sequence identity). Ablations further show that predicted structure is unnecessary at inference and that performance is not driven by any single descriptor family: ten global physicochemical scalars recover 91% of full-feature performance. Finally, explicitly modelling label dependence yields targeted benefits for scarce activities and supports ranking which activity to assay next from partial positive evidence.",
  "abstract_shown": true,
  "affiliations": [
    "IIT Roorkee"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "A. Pal"
    },
    {
      "name": "R. Kumar"
    },
    {
      "name": "D. Solanki"
    },
    {
      "name": "P. Pareek"
    },
    {
      "name": "J. Singh"
    },
    {
      "affs": [
        0
      ],
      "name": "J. Singla"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/67pwrdwk7rze3.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.08.27.747591",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.08.27.747591v2.full",
  "id": "67pwrdwk7rze3",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.08.27.747591v2",
  "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",
      "2404|Antimicrobial Peptides and Activities"
    ]
  ],
  "openalex": "W7204620294",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.08.27.747591v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-08-27",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/67pwrdwk7rze3.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:45Z",
      "native_id": "10.64898/2026.08.27.747591",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.08.27.747591v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Coarse composition suffices: tabular in-context learning for multi-activity antimicrobial peptide profiling",
  "title_text": "Coarse composition suffices: tabular in-context learning for multi-activity antimicrobial peptide profiling",
  "type": "preprint",
  "updated_at": "2026-10-30T12:19:45Z",
  "url": "https://research.draconitic.com/paper/67pwrdwk7rze3",
  "venue": "bioRxiv",
  "versions": []
}