Predictive Multivariate Linear Regression Analysis Guides Successful Catalytic Enantioselective Minisci Reactions of Diazines
The Minisci reaction is one of the most direct and versatile methods for forging new carbon-carbon bonds onto basic heteroarenes, a broad subset of compounds ubiquitous in medicinal chemistry. Whilst many Minisci-type reactions result in new stereocenters, control of the absolute stereochemistry has proved challenging. An asymmetric variant was recently realized using chiral phosphoric acid catalysis, although in that study the substrates were limited to quin-olines and pyridines. Mechanistic uncertainties and non-obvious enantioselectivity trends made the task of extending the reaction to important new substrates classes challenging and time-intensive. Herein, we describe an approach to addressing this problem through the rigorous analysis of the reaction landscape guided by a carefully designed reaction dataset and facilitated through multivariate linear regression (MLR) analysis. These techniques permitted the devel-opment of mechanistically informative correlations providing the basis to transfer enantioselectivity outcomes to new reaction components, ultimately predicting pyrimidines to be particularly amenable to the protocol. The prediction of enantioselectivity outcomes for these valuable, pharmaceutically-relevant motifs were remarkably accurate in most cases and resulted in a comprehensive exploration of scope, significantly expanding the utility and versatility of this methodology. This successful outcome is a powerful demonstration of the benefits of utilizing MLR analysis as a predic-tive platform for effective and efficient reaction scope exploration across substrate classes.
The Royal Society (uf130004)
European Research Council (757381)