{
  "abstract": "Integrative macromolecular docking pipelines generate thousands of candidate structural decoys, relying on downstream pairwise ensemble analysis, including Root-Mean-Square Deviation (RMSD) matrices and Fraction of Common Contacts (FCC) clustering, to identify biologically relevant binding poses. However, pairwise structural comparisons scale quadratically (O(N^2)), and canonical workflows in HADDOCK3 historically rely on disk-bound trajectory serialization and iterative CPU set operations, creating a quadratic computational and storage bottleneck for large ensembles. Here, we demonstrate that these operations can be reformulated as continuous linear algebra on GPU-resident tensors: structural superposition via batched Singular Value Decomposition (SVD) and residue contact overlap via binary matrix multiplication (M = A A^T). Across empirical benchmarks spanning N = 200 to 10,000 models (evaluating up to 49,995,000 decoy pairs), our GPU implementation achieves a 6.41-fold compute speedup on Apple Silicon (MPS) and up to 12.2-fold end-to-end acceleration in an N = 5,000 ablation on NVIDIA CUDA by eliminating trajectory disk I/O. Crucially, the implementation achieves zero downstream structural regression relative to canonical baselines, preserving identical cluster assignments (Adjusted Rand Index ARI = 1.0000, zero differing assignments) with sub-microangstrom RMSD concordance (Delta_max <= 4.07 x 10^-7 Angstrom on single-precision MPS; machine-precision Delta_max <= 8.88 x 10^-16 Angstrom on double-precision CUDA). The accelerated code, test suites, and benchmarking workflows are openly accessible on GitHub under the Apache-2.0 license.",
  "abstract_shown": true,
  "affiliations": [
    "University of Ghana"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 5,
  "authors": [
    {
      "name": "R. O. Salami"
    },
    {
      "name": "S. M. Kumordzi"
    },
    {
      "affs": [
        0
      ],
      "name": "D. N. Amuna"
    },
    {
      "name": "R. A. Carr"
    },
    {
      "name": "C. M. Morang'a"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bzcjg4rm5ffcw.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.10.758202",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.10.758202v1.full",
  "id": "bzcjg4rm5ffcw",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.10.758202v1",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7221558623",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.10.758202v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-10",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bzcjg4rm5ffcw.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:22Z",
      "native_id": "10.64898/2026.10.10.758202",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.10.758202v2"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Tensorizing Pairwise Structural Analysis: GPU-Accelerated RMSD and FCC Clustering in HADDOCK3 Without Structural Regression.",
  "title_text": "Tensorizing Pairwise Structural Analysis: GPU-Accelerated RMSD and FCC Clustering in HADDOCK3 Without Structural Regression.",
  "type": "preprint",
  "updated_at": "2026-10-30T12:28:20Z",
  "url": "https://research.draconitic.com/paper/bzcjg4rm5ffcw",
  "venue": "bioRxiv",
  "versions": []
}