{
  "id": 9423907,
  "title": "Generative Access to Make-on-Demand Chemical Space Enables Ultra-Large Virtual Screening",
  "url": "https://urgent.news/2026/09/23/generative-access-to-make-on-demand-chemical-space-enables-ultra",
  "topic": "ai",
  "section": "AI",
  "published": "2026-09-23T00:00:00.000Z",
  "source": {
    "name": "bioRxiv",
    "slug": "biorxiv",
    "url": "https://www.biorxiv.org/content/10.64898/2026.09.17.752345v1?rss=1"
  },
  "original_language": "en",
  "account": "Ultra-large-scale virtual screening (ULVS) has grown reliant on make-on-demand chemical spaces; however, efficiently accessing these spaces can be difficult because their size surpasses what can be exhaustively enumerated and docked. In this study, researchers introduce REAL-SWIT, a generative ULVS framework. This framework learns Enamine REAL Space as a synthesizable molecular distribution and integrates it with a target-specific scoring model. This combination enables docking-guided exploration of REAL Space at the level of complete molecules. The generative model that underpins REAL-SWIT is capable of capturing transferable features from the REAL Space distribution. Remarkably, around 96% of molecules generated through this process were found within REAL Space. Moreover, a subset of molecules generated that were not initially included in the training release later appeared in subsequent expanded releases. In computational benchmarks, REAL-SWIT outperformed fragment-based search strategies by identifying more molecules with favorable docking scores. This includes combinations of cooperative building blocks that fragment-level prioritization might have overlooked. To validate the approach experimentally, researchers screened ROCK1 and identified six biochemical inhibitors among the 23 synthesized compounds. One such inhibitor, RX-3, exhibited an IC50 of 0.17 M. These findings demonstrate that generative access to make-on-demand chemical space serves as a practical strategy for ultra-large virtual screening.",
  "summary": "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…",
  "key_points": [],
  "editors_take": null,
  "illustration": null,
  "coverage": {
    "outlets": 1,
    "also_reported_by": []
  },
  "ai_generated": true,
  "disclaimer": "Summaries, key points and the editor’s take are written by software from other outlets’ reporting and may contain errors — always check the linked original."
}