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<p>Optimizing both on-target and off-target potencies is essential for developing effective and selective small-molecule therapeutics. Free energy calculations offer rapid potency predictions, usually within hours and with experimental accuracy and thus enables efficient identification of promising compounds for synthesis, accelerating early-stage drug discovery campaigns. While free energy predictions are routinely applied to individual proteins, here, we present a free energy framework for efficiently achieving kinome-wide selectivity that led to the discovery of selective Wee1 kinase inhibitors. Ligand-based relative binding free energy calculations rapidly identified multiple novel potent chemical scaffolds. Subsequent protein residue mutation free energy calculations that modified the Wee1 gatekeeper residue, significantly reduced their off-target liabilities across the kinome. Thus, with judicious use of this gatekeeper residue selectivity handle, applying this computational strategy streamlined the optimization of both on-target and off-target potencies, offering a roadmap to expedite drug discovery timelines by decreasing unanticipated off-target toxicities.</p>

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Harnessing free energy calculations for kinome-wide selectivity in drug discovery campaigns with a Wee1 case study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月26日

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  • Jennifer Lynn Knight,
  • Anthony J. Clark,
  • Jiashi Wang,
  • Andrew Placzek,
  • Pieter H. Bos,
  • Sathesh Bhat,
  • Jeffrey A. Bell,
  • Sarah Silvergleid,
  • Wu Yin,
  • Felicia Gray,
  • Shaoxian Sun,
  • Karen Akinsanya,
  • Robert Abel,
  • Aleksey I. Gerasyuto

摘要

Optimizing both on-target and off-target potencies is essential for developing effective and selective small-molecule therapeutics. Free energy calculations offer rapid potency predictions, usually within hours and with experimental accuracy and thus enables efficient identification of promising compounds for synthesis, accelerating early-stage drug discovery campaigns. While free energy predictions are routinely applied to individual proteins, here, we present a free energy framework for efficiently achieving kinome-wide selectivity that led to the discovery of selective Wee1 kinase inhibitors. Ligand-based relative binding free energy calculations rapidly identified multiple novel potent chemical scaffolds. Subsequent protein residue mutation free energy calculations that modified the Wee1 gatekeeper residue, significantly reduced their off-target liabilities across the kinome. Thus, with judicious use of this gatekeeper residue selectivity handle, applying this computational strategy streamlined the optimization of both on-target and off-target potencies, offering a roadmap to expedite drug discovery timelines by decreasing unanticipated off-target toxicities.