{
  "abstract": "Cryptic binding pockets; sites absent or occluded in a proteins resting-state structure that become druggable only in specific, transiently populated conformations; represent one of the largest untapped opportunities in structure-based drug discovery. Large-scale structural surveys estimate that cryptic pockets occur in on the order of one in six protein families genome-wide, and that accounting for them could expand the druggable fraction of the disease-associated human proteome from roughly 40% to nearly 80%. Detecting cryptic pockets computationally has historically forced a choice btw physics-based conformational sampling, which is reliable but too computationally expensive to deploy across more than a handful of targets, and fast machine-learning pocket predictors trained on static structures, which frequently over-predict and lose the precision needed for practical triage. We report Kryptix-1, an in-house pipeline developed at Covenant Biosciences that combines conformational ensemble generation with a consensus pocket-scoring algorithm, benchmarked here against a matched static-structure baseline and against the published literature on a 10-protein sample from CryptoBench, an independently curated cryptic-pocket reference dataset. On the fields own standard residue-overlap metric (Jaccard index 0.5), Kryptix-1 succeeds on 80% of benchmark proteins, compared to 20% for the static baseline and approximately 40-45% for the best-performing methods reported in an independent comparative evaluation using the same metric. We report these results alongside an explicit accounting of where Kryptix-1s advantage is smaller or reverses, and we are explicit throughout that this is a small pilot evaluation, not a fully powered validation study.",
  "abstract_shown": true,
  "affiliations": [
    "Philadelphia College of Pharmacy, Saint Joseph's University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "R. Varghese"
    },
    {
      "name": "P. Tiwary"
    },
    {
      "name": "K. Oswal"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bf06pg1twgdzg.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.16.752177",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.16.752177v1.full",
  "id": "bf06pg1twgdzg",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.16.752177v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7214192876",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.16.752177v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bf06pg1twgdzg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:20Z",
      "native_id": "10.64898/2026.09.16.752177",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.16.752177v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Kryptix-1: Conformational Ensemble Sampling Substantially Improves Cryptic Pocket Detection Relative to Static-Structure and Prior Computational Approaches",
  "title_text": "Kryptix-1: Conformational Ensemble Sampling Substantially Improves Cryptic Pocket Detection Relative to Static-Structure and Prior Computational Approaches",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:20Z",
  "url": "https://research.draconitic.com/paper/bf06pg1twgdzg",
  "venue": "bioRxiv",
  "versions": []
}