Designer substrate library for quantitative, predictive modeling of reaction performance

Abstract

Assessment of reaction substrate scope is often a qualitative endeavor that provides general indications of substrate sensitivity to a measured reaction outcome. Unfortunately, this field standard typically falls short of enabling the quantitative prediction of new substrates' performance. The disconnection between a reaction's development and the quantitative prediction of new substrates' behavior limits the applicative usefulness of many methodologies. Herein, we present a method by which substrate libraries can be systematically developed to enable quantitative modeling of reaction systems and the prediction of new reaction outcomes. Presented in the context of rhodium-catalyzed asymmetric transfer hydrogenation, these models quantify the molecular features that influence enantioselection and, in so doing, lend mechanistic insight to the modes of asymmetric induction.