Data-Driven Investigation of Asymmetric Photo-Organocatalyzed Alkylation of Indoles

Abstract

Enantioselective dual catalysis represents a powerful strategy for the preparation of functionally dense optically active compounds. Photocatalysis, paired with organocatalysis, undeniably represents a popular choice that takes advantage of radical and polar reactivity for crafting challenging bonds. Despite the interest in this methodology, the detailed analysis of the effects governing selectivity between the two catalytic cycles has not been extensively studied. Herein, we report a data-driven mechanistic study campaign to disclose the factors that influence the enantioselectivity of a photo-organocatalytic alkylation of indoles. Data science tools were used to generate a virtual library of potential substrates, thus ensuring the selection of a statistically diverse training set. Subsequent multivariate linear regression (MLR) analysis led to the identification of substrate electronic features as the key factor affecting the stereochemical outcome. Moreover, comprehensive mechanistic experiments unveiled the key role of the in situ generated oxindole intermediate that connects the two catalytic cycles in determining the observed selectivity. These findings lay the foundation for developing asymmetric dual catalytic transformations of synthetic relevance and provide a general platform for interrogating other complex catalytic systems.