{
  "abstract": "Molecular docking predicts how a small molecule binds to a protein and is a key bottleneck in drug discovery. Classical scoring functions sum empirical pairwise contacts, blind to quantum-mechanical effects like orbital charge transfer that govern binding specificity. We introduce Q-Score, encoding GNN-predicted orbital donor-acceptor energies into a weighted graph and scoring binding by solving a maximum-weight vertex clique problem via Digitized-Counterdiabatic QAOA. Each interaction anchor maps to one qubit and compatibility constraints become edges. Across 11 protein targets, DC-QAOA recovers the exact optimum on 8 at 10 qubits. On 1000 AI-generated molecules, Q-Score is orthogonal to classical scoring with Spearman rho of 0.05, driven by orbital quality with rho of 0.90, and free of molecular-weight bias, enriching for strong orbital interactions at twice the random rate. DC-QAOA achieves a mean approximation ratio of 0.94 with 52 percent exact. Execution of 1000 circuits on IBM Eagle confirms 6-qubit solvability on NISQ hardware.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2607.09737",
  "author_total": 6,
  "authors": [
    {
      "name": "Kangyu Zheng"
    },
    {
      "name": "Yidong Zhou"
    },
    {
      "name": "Ruihao Li"
    },
    {
      "name": "Zixin Ding"
    },
    {
      "name": "Zhiding Liang"
    },
    {
      "name": "Shaohua Li"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/bc82m4v28knxv.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2607.09737",
  "fulltext_url": null,
  "id": "bc82m4v28knxv",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2607.09737",
  "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",
      "cs.LG"
    ],
    [
      "arxiv",
      "physics.chem-ph"
    ],
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7168286848",
  "pdf_url": "https://arxiv.org/pdf/2607.09737",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-03",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/bc82m4v28knxv.ris",
  "sources": [
    {
      "first_seen": "2026-11-04T15:21:24Z",
      "native_id": "2607.09737",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2607.09737"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    }
  ],
  "title": "Q-Score: A Quantum-Native Scoring Function for Molecular Docking",
  "title_text": "Q-Score: A Quantum-Native Scoring Function for Molecular Docking",
  "type": "preprint",
  "updated_at": "2026-11-06T18:21:01Z",
  "url": "https://research.draconitic.com/paper/bc82m4v28knxv",
  "venue": "arXiv",
  "versions": []
}