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.