Data-Driven Workflow for the Development and Discovery of <i>N</i> -Oxyl Hydrogen Atom Transfer Catalysts

<i>N</i>-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 <i>N</i>-oxyl structure, catalyst optimization is a key challenge, particularly for simultaneous improvement across multiple parameters. This paper describes a data-driven approach to optimize <i>N</i>-oxyl hydrogen atom transfer catalysts. A focused library of 50 <i>N</i>-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 <i>N</i>-oxyl substructures bearing an adjacent heteroatom are more optimal HAT catalysts compared to the historical focus, phthalimide-<i>N</i>-oxyl, by striking the best balance among all three target experimental properties.