Data-Driven Catalyst Optimization for Stereodivergent Asymmetric Synthesis of α-Allyl Carboxylic Acids by Iridium/boron Hybrid Catalysis

13 May 2021, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Asymmetric catalysis enabling divergent control of multiple stereocenters remains challenging in synthetic organic chemistry. While machine learning-based optimization of molecular catalysis is an emerging approach, data-driven 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 stereodivergent asymmetric catalysis is applicable to late-stage structural modifications of drugs and their derivatives.


Catalysis informatics
Machine learning
Asymmetric Catalysis
Stereodivergent Asymmetric Synthesis

Supplementary materials

SI final


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