{
  "abstract": "Quantitative prediction of protein–ligand (P–L) binding affinities is generally thought to require extensive configurational sampling, which is computationally demanding. Here, we examine how closely a fundamentally different approach, a quantum-mechanical (QM) description of P–L binding energetics based on a small set of representative structures, can approach the accuracy of MD-based free-energy calculations. Large-scale validation of the SQM2.20 scoring function is carried out on the Public Free Energy Benchmark data set, comprising 88 P–L series with 1,113 ligands in total. To compare directly with the experimental binding free energies (∆G bind ), a scaling factor derived independently in the PL-REX data set was applied, yielding SQM2.20 sc . With refined receptor structures and an extended pool of ligand poses, SQM2.20 sc achieved a weighted pairwise root-mean-square error (RMSE) in ∆∆G of 1.41 kcal/mol, close to the 1.26 kcal/mol reported for FEP+. n a subset of 58 targets and 872 ligands, SQM2.20 sc (RMSE 1.41 kcal/mol) outperformed OpenFE (1.74 kcal/mol). These results demonstrate the transferability of the approach across a wide range of protein targets and ligand chemistries, further strengthening the case for SQM scoring as a computationally efficient tool for applications in computer-aided drug design.",
  "abstract_shown": true,
  "affiliations": [
    "Institute of Organic Chemistry and Biochemistry of the CAS, Flemingovo nám. 2, Prague, Czechia, 16000"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jindřich Fanfrlík",
      "orcid": "0000-0002-1257-1201"
    },
    {
      "affs": [
        0
      ],
      "name": "Adam Pecina",
      "orcid": "0000-0003-3890-7831"
    },
    {
      "affs": [
        0
      ],
      "name": "Martin Lepšík",
      "orcid": "0000-0003-2607-8132"
    },
    {
      "affs": [
        0
      ],
      "name": "Jan Řezáč",
      "orcid": "0000-0001-6849-7314"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/83hm280ckv0p9.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010976/v1",
  "fulltext_url": null,
  "id": "83hm280ckv0p9",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010976/v1",
  "language": null,
  "licence": "CC-BY-NC-ND",
  "licence_label": "CC BY-NC-ND",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by-nc-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7223375154",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010976/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-23",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/83hm280ckv0p9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:26Z",
      "native_id": "10.26434/chemrxiv.15010976",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010976/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "Quantitative Protein–Ligand Binding Affinity Prediction from Quantum-Mechanical Scoring",
  "title_text": "Quantitative Protein–Ligand Binding Affinity Prediction from Quantum-Mechanical Scoring",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:26Z",
  "url": "https://research.draconitic.com/paper/83hm280ckv0p9",
  "venue": "ChemRxiv",
  "versions": []
}