Issue 7, 2023

Transferrable selectivity profiles enable prediction in synergistic catalyst space

Abstract

Organometallic intermediates participate in many multi-catalytic enantioselective transformations directed by a chiral catalyst, but the requirement of optimizing two catalyst components is a significant barrier to widely adopting this approach for chiral molecule synthesis. Algorithms can potentially accelerate the screening process by developing quantitative structure–function relationships from large experimental datasets. However, the chemical data available in this catalyst space is limited. Herein, we report a data-driven strategy that effectively translates selectivity relationships trained on enantioselectivity outcomes derived from one catalyst reaction systems where an abundance of data exists, to synergistic catalyst space. We describe three case studies involving different modes of catalysis (Brønsted acid, chiral anion, and secondary amine) that substantiate the prospect of this approach to predict and elucidate selectivity in reactions where more than one catalyst is involved. Ultimately, the success in applying our approach to diverse areas of asymmetric catalysis implies that this general workflow should find broad use in the study and development of new enantioselective, multi-catalytic processes.

Graphical abstract: Transferrable selectivity profiles enable prediction in synergistic catalyst space

Supplementary files

Article information

Article type
Edge Article
Submitted
28 okt 2022
Accepted
10 yan 2023
First published
17 yan 2023
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY-NC license

Chem. Sci., 2023,14, 1885-1895

Transferrable selectivity profiles enable prediction in synergistic catalyst space

Y. Kuang, J. Lai and J. P. Reid, Chem. Sci., 2023, 14, 1885 DOI: 10.1039/D2SC05974F

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