{
  "abstract": "Ultra-large-scale virtual screening (ULVS) increasingly relies on make-on-demand chemical spaces, but efficient access to these spaces remains challenging because their scale far exceeds what can be exhaustively enumerated and docked. Here, we present REAL-SWIT, a generative ULVS framework that learns Enamine REAL Space as a synthesizable molecular distribution and couples this generator with a target-specific scoring model to enable docking-guided exploration of REAL Space at the level of complete molecule. The generative model learned transferable features of the REAL Space distribution: approximately 96% of generated molecules were found in REAL Space, and a subset of generated molecules absent from the training release appeared in later expanded releases. In computational benchmarks, REAL-SWIT identified more molecules with favorable docking scores than representative fragment-based search strategies, including cooperative building-block combinations that fragment-level prioritization tended to miss. Experimental validation for ROCK1 yielded six biochemical inhibitors among 23 synthesized compounds, including RX-3 with an IC50 of 0.17 M. These results establish generative access to make-on-demand chemical space as a practical strategy for ULVS.",
  "abstract_shown": true,
  "affiliations": [
    "Westlake University"
  ],
  "article_type": "New results",
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "name": "K. Zhang"
    },
    {
      "name": "Y. Sun"
    },
    {
      "name": "X. Li"
    },
    {
      "name": "Y. Wang"
    },
    {
      "name": "C. Peng"
    },
    {
      "name": "X. Jin"
    },
    {
      "name": "Q. Hu"
    },
    {
      "affs": [
        0
      ],
      "name": "J. Huang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7sm48b6agav2r.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.09.17.752345",
  "fulltext_url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752345v1.full",
  "id": "7sm48b6agav2r",
  "issns": [],
  "landing_url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752345v1",
  "language": null,
  "licence": "CC-BY-ND",
  "licence_label": "CC BY-ND",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nd/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "biorxiv",
      "bioinformatics"
    ],
    [
      "openalex",
      "1703|Computational Drug Discovery Methods"
    ]
  ],
  "openalex": "W7214106239",
  "pdf_url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752345v1.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-17",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7sm48b6agav2r.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:24:09Z",
      "native_id": "10.64898/2026.09.17.752345",
      "source": "biorxiv",
      "url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752345v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "biorxiv:bioinformatics",
      "subfield": "bioinformatics"
    }
  ],
  "title": "Generative Access to Make-on-Demand Chemical Space Enables Ultra-Large Virtual Screening",
  "title_text": "Generative Access to Make-on-Demand Chemical Space Enables Ultra-Large Virtual Screening",
  "type": "preprint",
  "updated_at": "2026-10-30T12:24:09Z",
  "url": "https://research.draconitic.com/paper/7sm48b6agav2r",
  "venue": "bioRxiv",
  "versions": []
}