AIs like Microsoft's Copilot, Google's Gemini and OpenAI's ChatGPT still make serious errors rendering structural formulae

The stereoselective functionalization of C–H bonds represents a central challenge in modern organic synthesis. Despite decades of innovation in C–H activation chemistry, methods for Z-selective functi...

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Azetidines are four-membered saturated N-heterocycles that are of interest in drug discovery and medicinal chemistry. Here the authors report how sulfamoyl fluoride substituents tune the reactivity of...

Olefin metathesis and cyclopropanation, major reactions in the synthetic toolbox, are predominantly catalyzed by platinum-group metals. First-row transition metal alternatives are desirable from the p...

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 optimize N-oxyl hydrogen atom transfer catalysts. A focused library of 50 N-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 N-oxyl substructures bearing an adjacent heteroatom are more optimal HAT catalysts compared to the historical focus, phthalimide-N-oxyl, by striking the best balance among all three target experimental properties.

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 optimize N-oxyl hydrogen atom transfer catalysts. A focused library of 50 N-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 N-oxyl substructures bearing an adjacent heteroatom are more optimal HAT catalysts compared to the historical focus, phthalimide-N-oxyl, by striking the best balance among all three target experimental properties.