{
  "abstract": "Cryptic ligand-binding pockets are not apparent in experimentally determined apo structures, making them difficult to identify from unbound receptor geometry. A complementary challenge is to make the structural measurements and learned evidence behind each prediction directly inspectable. We introduce a supervised algebraic counting field (ACF) for predicting cryptic-pocket residues from apo structures. ACF compiles explicit geometric, physicochemical, and topological features into compact, integer-weighted lookup tables. Each prediction score can be reconstructed from feature values, training counts, table weights, and spatial aggregation, without sequence search, structural-template transfer, or a protein language model at inference. We evaluate ACF on CryptoBench and two locked external collections, separating ranking performance from the effects of residue-calling budgets. On an external set of 57 post-CryptoBench apo-holo units, ACF exceeded P2Rank by +0.044 in mean paired ROC-AUC (multiplicity-adjusted 95% CI [+0.010, +0.079]). The advantage was dataset-dependent: official-fold ROC-AUC and matched-budget F1 differences against P2Rank remained unresolved, and a second external evaluation did not confirm gains from added structural features. ACF thus provides a compact predictor with externally validated signal and an inspectable path from structural measurements and training counts to residue scores.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.00988",
  "author_total": 2,
  "authors": [
    {
      "name": "Shan Yu"
    },
    {
      "name": "Xuening Wu"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/228zff4kqjcxp.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.00988",
  "fulltext_url": null,
  "id": "228zff4kqjcxp",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.00988",
  "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",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7216591533",
  "pdf_url": "https://arxiv.org/pdf/2610.00988",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/228zff4kqjcxp.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:08Z",
      "native_id": "2610.00988",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.00988"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "arxiv:q-bio.BM",
      "subfield": "biophysics"
    }
  ],
  "title": "Auditable Algebraic Counting Field for Cryptic-Pocket Detection from Apo Structures",
  "title_text": "Auditable Algebraic Counting Field for Cryptic-Pocket Detection from Apo Structures",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:08Z",
  "url": "https://research.draconitic.com/paper/228zff4kqjcxp",
  "venue": "arXiv",
  "versions": []
}