Data-driven Workflow for the Development and Discovery of N-oxyl Hydrogen Atom Transfer Catalysts

N-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 N-oxyl structure, catalyst optimization is a key challenge, particularly for simultaneous improvement across multiple parameters. This paper describes a data-driven approach to op-timize N-oxyl hydrogen atom transfer catalysts. A focused library of 50 N-hydroxy compounds was synthesized and char-acterized 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 N-oxyl sub-structures bearing an adjacent heteroatom as more optimal HAT catalysts compared to the historical focus, phthalimide-N-oxyl, by striking the best balance among all three target experimental properties.