{
  "id": 14052855,
  "title": "Tensorizing Pairwise Structural Analysis: GPU-Accelerated RMSD and FCC Clustering in HADDOCK3 Without Structural Regression.",
  "url": "https://urgent.news/2026/10/12/tensorizing-pairwise-structural-analysis-gpu-accelerated-rmsd-and-fcc",
  "topic": "science",
  "section": "Science",
  "published": "2026-10-12T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.10.10.758202v1?rss=1"
  },
  "original_language": "en",
  "account": "Macromolecular docking pipelines typically generate a large number of candidate structural decoys, which are analyzed using pairwise ensemble methods such as Root-Mean-Square Deviation (RMSD) matrices and Fraction of Common Contacts (FCC) clustering to identify relevant binding poses. However, the computational and storage demands of these methods can become a bottleneck for large datasets. The HADDOCK3 software has traditionally relied on disk-bound trajectory serialization and iterative CPU set operations, which create a quadratic computational complexity issue.\n\nThis study demonstrates that the aforementioned operations can be restructured as continuous linear algebra computations on GPU-resident tensors. Specifically, structural superposition can be achieved through batched Singular Value Decomposition (SVD), while residue contact overlap can be accomplished via binary matrix multiplication (M = A A^T). The GPU implementation was evaluated against empirical benchmarks involving model sizes ranging from 200 to 10,000, encompassing up to 49,995,000 decoy pairs.\n\nAcross these benchmarks, the GPU-accelerated approach achieved a significant 6.41-fold speedup on Apple Silicon (MPS) and up to 12.2-fold end-to-end acceleration on NVIDIA CUDA during an N = 5,000 ablation test. Notably, the GPU implementation maintained zero structural regression relative to the traditional baselines, ensuring that the cluster assignments remained identical (Adjusted Rand Index = 1.0000) and the RMSD differences were minimal (Delta_max).",
  "summary": "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…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}