Yang Cheng, Wild Thérèse, Rakova Yulia, Maldonado Stephen, Sigman Matthew S, Stephenson Corey R J
Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States.
Department of Chemistry, University of Utah, Salt Lake City, Utah 84112, United States.
ACS Cent Sci. 2025 Mar 24;11(4):592-600. doi: 10.1021/acscentsci.4c01919. eCollection 2025 Apr 23.
-oxyl species are promising hydrogen atom transfer (HAT) catalysts to advance C-H bond activation reactions. However, because of the complex structure-activity relationship within the -oxyl structure, catalyst optimization is a key challenge, particularly for simultaneous improvement across multiple parameters. This paper describes a data-driven approach to optimize -oxyl hydrogen atom transfer catalysts. A focused library of 50 -hydroxy compounds was synthesized and characterized by three parameters-oxidation peak potential, HAT reactivity, and stability-to generate a database. Statistical modeling of these activities described by their intrinsic physical organic parameters was used to build predictive models for catalyst discovery and to understand their structure-activity relationships. Virtual screening of 102 synthesizable candidates allowed for rapid identification of several ideal catalyst candidates. These statistical models clearly suggest that -oxyl substructures bearing an adjacent heteroatom are more optimal HAT catalysts compared to the historical focus, phthalimide--oxyl, by striking the best balance among all three target experimental properties.
氧自由基是推进C-H键活化反应的有前景的氢原子转移（HAT）催化剂。然而，由于氧自由基结构内复杂的构效关系，催化剂优化是一项关键挑战，特别是要同时在多个参数上实现改进。本文描述了一种数据驱动的方法来优化氧自由基氢原子转移催化剂。合成了一个包含50种羟基化合物的聚焦文库，并通过氧化峰电位、HAT反应活性和稳定性这三个参数进行表征，以生成一个数据库。利用由其内在物理有机参数描述的这些活性的统计模型来构建用于催化剂发现的预测模型，并理解它们的构效关系。对102种可合成候选物的虚拟筛选使得能够快速鉴定出几种理想的催化剂候选物。这些统计模型清楚地表明，与历史研究重点邻苯二甲酰亚胺-氧自由基相比，带有相邻杂原子的氧自由基亚结构是更优的HAT催化剂，因为它们在所有三个目标实验性质之间达到了最佳平衡。
