{
  "abstract": "Drug-target association and dissociation rates often determine in vivo efficacy more than affinity alone. The association rate constant, however, is computationally challenging to predict since the productive encounter is a rare event in a large translational and orientational search space, which lies beyond the reach of conventional atomistic simulations. We present PySTARC (Python Simulation Toolkit for Association Rate Constants), a GPU-accelerated Brownian dynamics engine for estimating bimolecular association rate constants. PySTARC is a Python reimplementation of the BrownDye engine that converges even small reaction probabilities on a single GPU, at a throughput that would otherwise require a large CPU cluster. The current framework resolves reactions between integration steps with a closed-form Brownian bridge, models the internal flexibility of the solute through a coarse-grained bead chain, distributes trajectories across multiple GPUs, checkpoints long runs, monitors convergence, and automates the entire workflow from input structures to rate estimates. PySTARC is validated against protein-ligand and protein-protein complexes spanning five orders of magnitude in the association rate constant, with most estimates within one order of magnitude of experiment.",
  "abstract_shown": true,
  "affiliations": [
    "Flatiron Institute"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "A. A. Ojha"
    },
    {
      "name": "G. Huber"
    },
    {
      "name": "S. Dutta"
    },
    {
      "name": "S. M. Hanson"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1fewcxnafwxfg.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.28.754770",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.28.754770v1.full",
  "id": "1fewcxnafwxfg",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.28.754770v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "biophysics"
    ],
    [
      "openalex",
      "1312|Protein Structure and Dynamics"
    ]
  ],
  "openalex": "W7214952949",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.28.754770v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1fewcxnafwxfg.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:19Z",
      "native_id": "10.64898/2026.09.28.754770",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.28.754770v1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "biorxiv:biophysics",
      "subfield": "biophysics"
    }
  ],
  "title": "PySTARC: GPU-accelerated Brownian dynamics for bimolecular association rate constants",
  "title_text": "PySTARC: GPU-accelerated Brownian dynamics for bimolecular association rate constants",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:19Z",
  "url": "https://research.draconitic.com/paper/1fewcxnafwxfg",
  "venue": "bioRxiv",
  "versions": []
}