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 mode

SUBMITTER: Kuang Y 

PROVIDER: S-EPMC9931051 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature