Prediction of the enantiomeric excess value for asymmetric transfer hydrogenation based on machine learning-Reference-Cited by-同舟云学术

Prediction of the enantiomeric excess value for asymmetric transfer hydrogenation based on machine learning

Published:2023 Issue:6 Volume:10 Page:1456-1462
ISSN:2052-4129
Container-title:Organic Chemistry Frontiers
language:en
Short-container-title:Org. Chem. Front.

Author:

Gao Ben¹^ORCID,Chang Yuqi¹,Tang Wenjun¹²^ORCID

Affiliation:

1. School of Chemistry and Material Sciences, Hangzhou Institute of Advanced Study, University of Chinese Academy of Science, 1 Sub-lane Xiangshan, Hangzhou 310024, China

2. State Key Laboratory of Bio-Organic and Natural Products Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 345 Ling Ling Rd, Shanghai 200032, China

Abstract

By combining the sterimol parameters and burial volume with the molecular descriptors calculated by quantum chemistry, the enantioselectivity of asymmetric transfer hydrogenation can be predicted.

Publisher

Royal Society of Chemistry (RSC)

Subject

Organic Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/QO/D2QO01680J

Reference52 articles.

1. The Golden Age of Transfer Hydrogenation

2. Ruthenium(II)-Catalyzed Asymmetric Transfer Hydrogenation of Ketones Using a Formic Acid−Triethylamine Mixture

3. Broader, Greener, and More Efficient: Recent Advances in Asymmetric Transfer Hydrogenation

4. A New Class of “Tethered” Ruthenium(II) Catalyst for Asymmetric Transfer Hydrogenation Reactions

5. Predicting reaction performance in C–N cross-coupling using machine learning

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