{
  "abstract": "Alchemical free-energy calculations predict protein-ligand binding affinities with close-to-experimental accuracy, but their cost restricts them to small sets of ligands. Coarse-grained (CG) models such as Martini 3 retain a rigorous statistical-mechanical treatment at a fraction of this cost, yet their accuracy for protein-ligand binding has not been assessed systematically. Here, we compute absolute binding free energies for 172 ligands across 15 protein targets with a Martini 3 double-decoupling protocol and compare them against atomistic FEP+ and OpenFE references. Martini underbinds for every target, by 2.7 to 8.7 kcal/mol. This offset is systematic: it comprises a uniform contribution that grows with the number of charged pocket residues. A linear model in this number, which requires only the protein structure, reduces the mean absolute error from 5.7 to 1.2 kcal/mol in leave-one-out cross-validation, close to the 1.0 kcal/mol obtained with exact per-target offsets. The ranking of ligands within a target varies more widely (median Spearman $ρ$ of 0.32, from $-$0.41 to 0.89) and is rationalized by the structure-activity relationship of each series: Martini ranks ligands well when their affinity is driven by hydrophobicity, $π$-stacking, or size, and poorly when it is driven by hydrogen bonding or charge. Both trends follow from Martini's calibration against bulk partitioning, which captures neither the dielectric environment of a binding pocket nor the directionality of hydrogen bonds. While MMGBSA retains the atomistic detail needed to rank congeneric ligands, Martini yields physically meaningful free energies, and its coarse-graining maps many atomistically distinct molecules onto one representation, making it a complementary tool for rapidly spanning chemical space.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.11714",
  "author_total": 2,
  "authors": [
    {
      "name": "Luis J. Walter"
    },
    {
      "name": "Tristan Bereau"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8mach7kh9v43p.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.11714",
  "fulltext_url": null,
  "id": "8mach7kh9v43p",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.11714",
  "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",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7221124062",
  "pdf_url": "https://arxiv.org/pdf/2610.11714",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-08",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/8mach7kh9v43p.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:54Z",
      "native_id": "2610.11714",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.11714"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "arxiv:physics.chem-ph",
      "subfield": "physical"
    }
  ],
  "title": "Predictable Accuracy of Martini 3 Absolute Protein-Ligand Binding Free Energies across 15 Targets",
  "title_text": "Predictable Accuracy of Martini 3 Absolute Protein-Ligand Binding Free Energies across 15 Targets",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:54Z",
  "url": "https://research.draconitic.com/paper/8mach7kh9v43p",
  "venue": "arXiv",
  "versions": []
}