Molecular Field Analysis Using Computational-Screening Data in Asymmetric N-Heterocyclic Carbene-Copper Catalysis toward Data-Driven In Silico Catalyst Optimization

A molecular-field-based regression analysis using computational screening data for N-heterocyclic carbene (NHC)-Cu-catalyzed asymmetric carbonyl additions of a silylboronate to aldehydes is reported. A computational screening was performed to collect enantioselectivity data (ΔΔG‡: energy differences...

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Bibliographic Details
Published in:Bulletin of the Chemical Society of Japan 2022-02, Vol.95 (2), p.271-277
Main Authors: Mukai, Masakiyo, Nagao, Kazunori, Yamaguchi, Shigeru, Ohmiya, Hirohisa
Format: Article
Language:English
Subjects:
Aldehydes
Asymmetry
Carbenes
Carbonyls
Catalysis
Copper
Density functional theory
Enantiomers
Ligands
Mathematical analysis
Optimization
Regression analysis
Screening
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Summary:A molecular-field-based regression analysis using computational screening data for N-heterocyclic carbene (NHC)-Cu-catalyzed asymmetric carbonyl additions of a silylboronate to aldehydes is reported. A computational screening was performed to collect enantioselectivity data (ΔΔG‡: energy differences between the transition states leading to each enantiomer) via transition-state (TS) calculations using density functional theory (DFT) methods. A molecular field analysis (MFA) was carried out using the obtained calculated ΔΔG‡ values and TS structures (30 samples in total). Important structural information for enantioselectivity extracted by the MFA was visualized on the TS structures, which provided insight into an asymmetric induction mechanism. Based on the obtained information, chiral NHC ligands were designed, which showed improved enantioselectivity in these carbonyl additions (designed ligands: up to 96% ee, initial training samples: up to 73% ee).
ISSN:0009-2673
1348-0634
DOI:10.1246/bcsj.20210349