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<p>Fragment-based drug discovery (FBDD) is an effective approach for exploring chemical space using small, low-affinity fragments as starting points to facilitate development of lead compounds. Strategies to improve fragment potency include fragment merging and linking to generate higher-affinity inhibitors. Recently, artificial intelligence (AI) and machine learning (ML) have accelerated this process through structure-based optimization and generative compound design. Here, we present an AI-assisted FBDD workflow applied to the SARS-CoV-2 macrodomain (Mac1), a conserved viral protein involved in immune evasion and ADP-ribose metabolism. Using available structural data and previously identified fragments, we combined deep learning with molecular docking to design novel Mac1 binders. Selected compounds were synthesized and validated by NMR spectroscopy and X-ray crystallography, demonstrating improved binding relative to the original fragment hits with <i>K</i><sub>D</sub> values in the range of 299-990 µM. This study demonstrates the advantages of integrating AI with FBDD to streamline molecular design, providing a data-driven framework for discovering new Mac1 inhibitors and guiding future antiviral drug development.</p><p></p>

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AI-assisted fragment-based drug discovery of SARS-CoV-2 macrodomain binders validated by NMR and X-ray crystallography

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月17日

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  • Elnaz Aledavood,
  • Sandra Ramos-Inza,
  • Jannis Born,
  • Megan Lambert,
  • Hannes Berg,
  • Jan Michael Köhler,
  • Anna Niesteruk,
  • Sridhar Sreeramulu,
  • Amy J. Thompson,
  • Halina Mikolajek,
  • James Sandy,
  • Juan Sanchez-Weatherby,
  • SiYi Wang,
  • Eleanor P. Williams,
  • Martin A. Walsh,
  • Michael A. Hough,
  • Harald Schwalbe,
  • Ana Martínez,
  • Carmen Gil

摘要

Fragment-based drug discovery (FBDD) is an effective approach for exploring chemical space using small, low-affinity fragments as starting points to facilitate development of lead compounds. Strategies to improve fragment potency include fragment merging and linking to generate higher-affinity inhibitors. Recently, artificial intelligence (AI) and machine learning (ML) have accelerated this process through structure-based optimization and generative compound design. Here, we present an AI-assisted FBDD workflow applied to the SARS-CoV-2 macrodomain (Mac1), a conserved viral protein involved in immune evasion and ADP-ribose metabolism. Using available structural data and previously identified fragments, we combined deep learning with molecular docking to design novel Mac1 binders. Selected compounds were synthesized and validated by NMR spectroscopy and X-ray crystallography, demonstrating improved binding relative to the original fragment hits with KD values in the range of 299-990 µM. This study demonstrates the advantages of integrating AI with FBDD to streamline molecular design, providing a data-driven framework for discovering new Mac1 inhibitors and guiding future antiviral drug development.