The direct cyanomethylation of indoles at the 2- or 3-position was achieved via photoredox catalysis. The versatile nitrile synthon is introduced as a radical generated from bromoacetonitrile, a photocatalyst, and blue LED as a light source. The mechanism of the reaction is explored by determination of the Stern-Volmer quenching constants. By combining photophysical data and mass spectrometry to follow the catalyst decomposition, the catalyst ligands were tuned to enable synthetically useful yields of radical coupling products. A range of indole substrates with alkyl, aryl, halogen, ester, and ether functional groups participate in the reaction, affording products in 16-90% yields. The reaction allows the rapid construction of synthetically useful cyanomethylindoles, products that otherwise require several synthetic steps.

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.