Using Data Science To Guide Aryl Bromide Substrate Scope Analysis in a Ni/Photoredox-Catalyzed Cross-Coupling with Acetals as Alcohol-Derived Radical Sources

Ni/photoredox catalysis has emerged as a powerful platform for C(sp2)-C(sp3) bond formation. While many of these methods typically employ aryl bromides as the C(sp2) coupling partner, a variety of aliphatic radical sources have been investigated. In principle, these reactions enable access to the same product scaffolds, but it can be hard to discern which method to employ because nonstandardized sets of aryl bromides are used in scope evaluation. Herein, we report a Ni/photoredox-catalyzed (deutero)methylation and alkylation of aryl halides where benzaldehyde di(alkyl) acetals serve as alcohol-derived radical sources. Reaction development, mechanistic studies, and late-stage derivatization of a biologically relevant aryl chloride, fenofibrate, are presented. Then, we describe the integration of data science techniques, including DFT featurization, dimensionality reduction, and hierarchical clustering, to delineate a diverse and succinct collection of aryl bromides that is representative of the chemical space of the substrate class. By superimposing scope examples from published Ni/photoredox methods on this same chemical space, we identify areas of sparse coverage and high versus low average yields, enabling comparisons between prior art and this new method. Additionally, we demonstrate that the systematically selected scope of aryl bromides can be used to quantify population-wide reactivity trends and reveal sources of possible functional group incompatibility with supervised machine learning

Reinforcement learning prioritizes general applicability in reaction optimization

Reaction conditions that are generally applicable to a wide variety of substrates are highly desired. While many approaches exist to evaluate the general applicability of developed conditions, a universal approach to efficiently discover such conditions during optimizations de novo is rare. In this work, we report the design, implementation, and application of reinforcement learning bandit optimization models to identify generally applicable conditions in a variety of chemical transformations. Performance benchmarking on existing datasets statistically showed high accuracies for identifying general conditions. A palladium-catalyzed imidazole C–H arylation reaction and an aniline amide coupling reaction were investigated experimentally to demonstrate utilities of our learning model in practice