Data Science Guided Multiobjective Optimization of a Stereoconvergent Nickel-Catalyzed Reduction of Enol Tosylates to Access Trisubstituted Alkenes

Abstract

Herein we report a method for a stereoconvergent synthesis of trisubstituted alkenes in two steps from simple ketone starting materials. The key step is a nickel-catalyzed reduction of the corresponding enol tosylates that predominantly relies on a monophosphine ligand to direct the stereoconvergent formation of either the E - or Z -trisubstituted alkene products. Reaction optimization was accomplished using a data science workflow including monophosphine training set design, statistical modeling, and multiobjective Bayesian optimization. The optimization campaign significantly improved access to both the E - and Z -trisubstituted products in up to ∼90:10 diastereoselectivity and >90% yield. After identifying superior ligands using training set design, only 25 reactions were required for each objective ( E - and Z -isomer formation) to converge on improved reaction parameters from a search space of ∼30,000 potential conditions using the EDBO+ platform. Additionally, a hierarchical machine learning model was developed to predict the stereoselectivity of untested monophosphine ligands to achieve a validation mean absolute error (MAE) of 7.1% selectivity (0.21 kcal/mol). Ultimately, we present a synergistic data science workflow leveraging the integration of training set design, statistical modeling, and Bayesian optimization, thereby expanding access to stereodefined trisubstituted alkenes.