{
  "abstract": "Accurate and efficient prediction of binding free energies remains a central challenge in computational chemistry, largely due to the complexity of solvent effects and the trade-off between accuracy and computational cost. Explicit solvent molecular dynamics (MD) simulations provide detailed thermodynamic and kinetic insight, now routinely reaching microsecond timescales, while end-point methods such as MM/GBSA offer high efficiency but are sensitive to model parameters. In particular, the accuracy of MM/GBSA strongly depends on the atomic radii used to define the dielectric boundary within the implicit solvent framework. Here, we introduce an explicit–implicit hybrid workflow for late-stage ligand prioritization that integrates the strengths of both approaches. In this framework, microsecond-scale explicit solvent MD simulations act as a rapid-dissociation filter, flagging weak or unstable binders through spontaneous dissociation within the simulation window. MM/GBSA is then used to rank the remaining candidates. To improve the accuracy of the implicit solvent stage, we employ optimized atomic radii sets (OPTBIND), specifically designed for binding free-energy calculations. On the protein–ligand complexes of Curated32-4 set , a new curated benchmark of 32 complexes including 4 congeneric series, the entropy-free MM/GBSA stage with OPT- BIND 6D achieved Pearson r = 0.82 and Spearman ρ = 0.84, with 100% recovery of the experimental rank ordering across all four congeneric series, while a single-replica consistency test of the rapid-dissociation filter yielded an AUC of 0.88 and retained all 15 sub-micromolar binders. Finally, as a real-world drug design application, we apply the workflow to halogen substitutions in SARS-CoV-2 main protease inhibitors, demonstrating its ability to identify favorable chemical modifications. Notably, the approach prospectively identifies F→Cl and F→I as affinity-enhancing substitutions; the predicted enhancement for the chlorine analog is subsequently confirmed experimentally, while the iodine analog remains a testable prediction, highlighting its predictive power in guiding chemical optimization. Together, these results establish a practical framework for detailed studies of receptor–ligand binding and for late-stage ligand prioritization in drug discovery.",
  "abstract_shown": true,
  "affiliations": [
    "Genetics, Bioinformatics, and Computational Biology",
    "Virginia Tech",
    "Department of Chemistry",
    "Roanoke College",
    "Department of Computer Science",
    "Department of Physics"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Fatemeh Ghafouri-Plate"
    },
    {
      "affs": [
        2,
        3
      ],
      "name": "Timothy W. Johann"
    },
    {
      "affs": [
        4,
        1
      ],
      "name": "Luke H. Elder"
    },
    {
      "affs": [
        4,
        1,
        5
      ],
      "name": "Alexey V. Onufriev",
      "orcid": "0000-0002-4930-6612"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5barq9xxw2y32.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010255/v1",
  "fulltext_url": null,
  "id": "5barq9xxw2y32",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010255/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7221555044",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010255/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/5barq9xxw2y32.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "10.26434/chemrxiv.15010255",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010255/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "source:chemrxiv",
      "subfield": "general"
    }
  ],
  "title": "Late-Stage Ligand Prioritization by Combining Rapid-Dissociation Filtering with MM/GBSA",
  "title_text": "Late-Stage Ligand Prioritization by Combining Rapid-Dissociation Filtering with MM/GBSA",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/5barq9xxw2y32",
  "venue": "ChemRxiv",
  "versions": []
}