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. While
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 quinolines and
pyridines. Mechanistic uncertainties and nonobvious enantioselectivity
trends made the task of extending the reaction to important new substrate
classes challenging and time-intensive. Herein, we describe an approach
to address this problem through rigorous analysis of the reaction
landscape guided by a carefully designed reaction data set and facilitated
through multivariate linear regression (MLR) analysis. These techniques
permitted the development 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 predictions 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 predictive platform for
effective and efficient reaction scope exploration across substrate
classes.