Data-driven catalyst optimization for stereodivergent asymmetric synthesis by iridium/boron hybrid catalysis
Hongyu Chen, Shigeru Yamaguchi, Yuya Morita, Hiroyasu Nakao, Xiangning Zhai, Y. Shimizu, Harunobu Mitsunuma, Motomu Kanai
Abstract
Asymmetric catalysis enabling divergent control of multiple stereocenters remains challenging in synthetic organic chemistry. Although machine-learning-based optimization of molecular catalysis is a rapidly growing research field, the use of such approaches for catalyst design to achieve stereodivergent asymmetric synthesis producing multiple reaction outcomes, such as constitutional selectivity, diastereoselectivity, and enantioselectivity, is unprecedented. Here, we report the straightforward identification of asymmetric two-component iridium/boron hybrid catalyst systems for α-C-allylation of carboxylic acids. Structural optimization of the chiral ligands for iridium catalysts was driven by molecular-field-based regression analysis with a dataset containing overall 32 molecular structures. The catalyst systems enabled selective access to all the possible isomers of chiral carboxylic acids bearing contiguous stereocenters. This chemoselective and stereodivergent, asymmetric catalysis is applicable to late-stage structural modifications of drugs and their derivatives.