{
  "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": [],
  "article_type": null,
  "arxiv": "2608.30337",
  "author_total": 6,
  "authors": [
    {
      "name": "Anuj Pal"
    },
    {
      "name": "Raunak Kumar"
    },
    {
      "name": "Dhruvi Solanki"
    },
    {
      "name": "Parikshit Pareek"
    },
    {
      "name": "Juhi Singh"
    },
    {
      "name": "Jitin Singla"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dc6t27q1c6hc9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2608.30337",
  "fulltext_url": null,
  "id": "dc6t27q1c6hc9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2608.30337",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "q-bio.BM"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2608.30337",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-06",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/dc6t27q1c6hc9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:20:02Z",
      "native_id": "2608.30337",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2608.30337"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "arxiv:q-bio.BM",
      "subfield": "biophysics"
    }
  ],
  "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": "2026-10-30T12:20:02Z",
  "url": "https://research.draconitic.com/paper/dc6t27q1c6hc9",
  "venue": "arXiv",
  "versions": []
}