open access

https://hdl.handle.net/2115/93622

A Primer on 2D Descriptors in Selectivity Modeling for Asymmetric Catalysis

英語

quantitative structure-selectivity relationships

2023-11-27

Wiley-Blackwell

Chemistry-A European journal

30(10)

e202302837

Machine learning has permeated all fields of research, including chemistry, and is now an integral part of the design of novel compounds with desired properties. In the field of asymmetric catalysis, the preference still lies with models based on a physical understanding of the catalysis phenomenon and the electronic and steric properties of catalysts. However, such models require quantum chemical calculations and are thus limited by their computational cost. Here, we highlight the recent advances in modeling catalyst selectivity by using the 2D structures of catalysts and substrates. While these have a less explicit mechanistic connection to the modeled property, 2D descriptors, such as topological indices, molecular fingerprints, and fragments, offer the tremendous advantages of low cost and high speed of calculations. This makes them optimal for the in-silico screening of large amounts of data. We provide an overview of common quantitative structure-property relationship workflow, model building and validation techniques, applications of these methodologies in asymmetric catalysis design, and an outlook on improving the understanding of 2D-based models. Recent advances in modeling catalyst selectivity by using 2D structures of catalysts and substrates are highlighted in this brief review. Two-dimensional descriptors offer the advantage of lower cost and higher speed in calculations than quantum chemical parameters. We provide an overview of a typical quantitative structure-property relationship workflow, including model building and validation techniques, as well as applications of these methodologies in the design of asymmetric catalysis.image

学術雑誌論文

Accepted Manuscript

This is the peer reviewed version of the following article: P. Sidorov, N. Tsuji, Chem. Eur. J. 2024, 30, e202302837, which has been published in final form at https://doi.org/10.1002/chem.202302837. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.

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