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<p>Optimizing the chemical structure of promising drug candidates through systematic modifications to improve potency and physiochemical properties is a vital step in the drug discovery pipeline. In contrast to the well-established de novo generation schemes, computational methods specifically tailored for lead optimization remain largely underexplored. Prior models are often limited to addressing specific subtasks, such as generating two-dimensional molecular structures, while neglecting crucial protein–ligand interactions in three-dimensional space. To overcome these challenges, we propose Delete (Deep lead optimization enveloped in protein pocket), a one-stop solution for lead optimization by combining generative artificial intelligence and structure-based approaches. Our model can handle all subtasks of lead optimization through a unified deleting (masking) strategy, and it accounts for intricate pocket–ligand interactions through an equivariant network design. Statistical assessments and retrospective studies across individual subtasks demonstrate that Delete has an outstanding ability to craft molecules with superior protein-binding energy and reasonable drug-likeness using given fragments or atoms. Subsequently, we utilize Delete to design inhibitors targeting the previously identified LTK protein. Among the ligands designed by Delete, CA-B-1 is successfully validated as a potent (1.36 nM) and selective inhibitor by in vitro and in vivo experiments. This work represents a successful implementation of the powerful structure-based lead optimization model, Delete, for rapid and controllable rational drug design.</p>

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Deep lead optimization enveloped in protein pocket and its application in designing potent and selective ligands targeting LTK protein

  • 期刊论文

  • 发表日期：

    2025年2月20日

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  • Shicheng Chen,
  • Odin Zhang,
  • Chenran Jiang,
  • Huifeng Zhao,
  • Xujun Zhang,
  • Mengting Chen,
  • Yun Liu,
  • Qun Su,
  • Zhenxing Wu,
  • Xinyue Wang,
  • Wanglin Qu,
  • Yuanyi Ye,
  • Xin Chai,
  • Ning Wang,
  • Tianyue Wang,
  • Yuan An,
  • Guanlin Wu,
  • Qianqian Yang,
  • Jiean Chen,
  • Wei Xie,
  • Haitao Lin,
  • Dan Li,
  • Chang-Yu Hsieh,
  • Yong Huang,
  • Yu Kang,
  • Tingjun Hou,
  • Peichen Pan

摘要

Optimizing the chemical structure of promising drug candidates through systematic modifications to improve potency and physiochemical properties is a vital step in the drug discovery pipeline. In contrast to the well-established de novo generation schemes, computational methods specifically tailored for lead optimization remain largely underexplored. Prior models are often limited to addressing specific subtasks, such as generating two-dimensional molecular structures, while neglecting crucial protein–ligand interactions in three-dimensional space. To overcome these challenges, we propose Delete (Deep lead optimization enveloped in protein pocket), a one-stop solution for lead optimization by combining generative artificial intelligence and structure-based approaches. Our model can handle all subtasks of lead optimization through a unified deleting (masking) strategy, and it accounts for intricate pocket–ligand interactions through an equivariant network design. Statistical assessments and retrospective studies across individual subtasks demonstrate that Delete has an outstanding ability to craft molecules with superior protein-binding energy and reasonable drug-likeness using given fragments or atoms. Subsequently, we utilize Delete to design inhibitors targeting the previously identified LTK protein. Among the ligands designed by Delete, CA-B-1 is successfully validated as a potent (1.36 nM) and selective inhibitor by in vitro and in vivo experiments. This work represents a successful implementation of the powerful structure-based lead optimization model, Delete, for rapid and controllable rational drug design.