{
  "abstract": "Identifying synthetically accessible, potent ligands from vast chemical space remains a major challenge in drug discovery, particularly because experimental synthesis and testing are costly and low throughput. Here, we present a fully open-source computational-experimental workflow that integrates fragment-based de novo molecular design with rigorous alchemical absolute binding free-energy calculations to enable physics-based prioritization before chemical synthesis. We apply this strategy by targeting the virulence-associated zinc metalloprotease LasB from Pseudomonas aeruginosa. From ~40,000 diverse compounds generated through multi-objective optimization, a small subset was prioritized and experimentally evaluated, leading to the discovery of three previously unreported nanomolar inhibitors (IC50 22-53 nM) with excellent metabolic and plasma stability. This work demonstrates a general and scalable framework for coupling generative molecular design with quantitative binding affinity prediction, improving decision-making and reducing the cost, time, and risk associated with early-stage drug discovery.",
  "abstract_shown": true,
  "affiliations": [
    "Saarland University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "A. Martinez Leon"
    },
    {
      "name": "A. Vgenopoulou"
    },
    {
      "name": "G. Jezequel"
    },
    {
      "name": "A. M. Kany"
    },
    {
      "name": "A. K. Hirsch"
    },
    {
      "affs": [
        0
      ],
      "name": "J. S. Hub"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7yc0sr6k6qf4q.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.10.12.758448",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.10.12.758448v1.full",
  "id": "7yc0sr6k6qf4q",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.10.12.758448v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "biophysics"
    ],
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7221816582",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.10.12.758448v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-12",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7yc0sr6k6qf4q.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:38Z",
      "native_id": "10.64898/2026.10.12.758448",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.10.12.758448v1"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "biorxiv:biophysics",
      "subfield": "biophysics"
    }
  ],
  "title": "Prospective discovery of LasB inhibitors by integrating generative design, free energy ranking, and experimental validation",
  "title_text": "Prospective discovery of LasB inhibitors by integrating generative design, free energy ranking, and experimental validation",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:38Z",
  "url": "https://research.draconitic.com/paper/7yc0sr6k6qf4q",
  "venue": "bioRxiv",
  "versions": []
}