A General Photocatalytic Strategy for Nucleophilic Amination of Primary and Secondary Benzylic C-H Bonds.

We report a visible-light photoredox-catalyzed method that enables nucleophilic amination of primary and secondary benzylic C(sp3)-H bonds. A novel amidyl radical precursor and organic photocatalyst operate in tandem to transform primary and secondary benzylic C(sp3)-H bonds into carbocations via sequential hydrogen atom transfer (HAT) and oxidative radical-polar crossover. The resulting carbocation can be intercepted by a variety of N-centered nucleophiles, including nitriles (Ritter reaction), amides, carbamates, sulfonamides, and azoles, for the construction of pharmaceutically relevant C(sp3)-N bonds under unified reaction conditions. Mechanistic studies indicate that HAT is amidyl radical-mediated and that the photocatalyst operates via a reductive quenching pathway. These findings establish a mild, metal-free, and modular protocol for the rapid diversification of C(sp3)-H bonds to a library of aminated products.

Dataset Design for Building Models of Chemical Reactivity.

Models can codify our understanding of chemical reactivity and serve a useful purpose in the development of new synthetic processes via, for example, evaluating hypothetical reaction conditions or in silico substrate tolerance. Perhaps the most determining factor is the composition of the training data and whether it is sufficient to train a model that can make accurate predictions over the full domain of interest. Here, we discuss the design of reaction datasets in ways that are conducive to data-driven modeling, emphasizing the idea that training set diversity and model generalizability rely on the choice of molecular or reaction representation. We additionally discuss the experimental constraints associated with generating common types of chemistry datasets and how these considerations should influence dataset design and model building.