{
  "abstract": "Electronic rearrangements associated with bond forming/breaking in catalytic enzymes require quantum mechanical (QM) treatment beyond classical molecular mechanics (MM). Hybrid QM/MM methods enable tractable simulations but require system-specific setup and are sensitive to the QM region choice and treatment of the QM/MM interface. We demonstrate quantum-accurate treatment of all-atom, complete enzymes in explicit solvent comprising up to 54k atoms and 1 microsecond of total simulation time using the machine-learned interatomic potential (MLIP) eSEN-omol. We reproduce experimental barrier trends for Claisen rearrangement in chorismate mutase, resolve critical intermediate states in PETase catalyzed polymer depolymerization, and distinguish mechanistic alternatives for metal-activated phosphoryl transfer in nucleoside diphosphate kinase. We realize 1000x speedups relative to typical QM/MM calculations without system-specific tuning. These results establish MLIPs as a practical route to QM-accurate simulations of enzyme catalysis.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.09293",
  "author_total": 13,
  "authors": [
    {
      "name": "Meng Gao"
    },
    {
      "name": "Armin Shayesteh Zadeh"
    },
    {
      "name": "Aniruddha Seal"
    },
    {
      "name": "Siva Dasetty"
    },
    {
      "name": "Siddarth K. Achar"
    },
    {
      "name": "Misko Dzamba"
    },
    {
      "name": "Benjamin K. Miller"
    },
    {
      "name": "Leif D. Jacobson"
    },
    {
      "name": "C. Lawrence Zitnick"
    },
    {
      "name": "Brandon M. Wood"
    },
    {
      "name": "Zachary W. Ulissi"
    },
    {
      "name": "Daniel S. Levine"
    },
    {
      "name": "Andrew L. Ferguson"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1e3n59j1szp8g.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.09293",
  "fulltext_url": null,
  "id": "1e3n59j1szp8g",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.09293",
  "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",
      "physics.chem-ph"
    ],
    [
      "openalex",
      "3107|Advanced Chemical Physics Studies"
    ]
  ],
  "openalex": "W7212131203",
  "pdf_url": "https://arxiv.org/pdf/2609.09293",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-16",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/1e3n59j1szp8g.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:01Z",
      "native_id": "2609.09293",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.09293"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    }
  ],
  "title": "Quantum-accurate atomistic modeling of enzyme catalysis using a machine learned potential",
  "title_text": "Quantum-accurate atomistic modeling of enzyme catalysis using a machine learned potential",
  "type": "preprint",
  "updated_at": "2026-10-30T12:22:01Z",
  "url": "https://research.draconitic.com/paper/1e3n59j1szp8g",
  "venue": "arXiv",
  "versions": []
}