Synlett 2021; 32(18): 1837-1842
DOI: 10.1055/s-0040-1705977
cluster
Machine Learning and Artificial Intelligence in Chemical Synthesis and Catalysis

A Molecular Stereostructure Descriptor Based On Spherical Projection

Li-Cheng Xu
,
Xin Li
,
Miao-Jiong Tang
,
Luo-Tian Yuan
,
Jia-Yu Zheng
,
Shuo-Qing Zhang
,
Xin Hong
Department of Chemistry, Zhejing University, Zheda Road 38, 310027, Hangzhou, P. R. of China
› Author Affiliations
Financial support from the National Natural Science Foundation of China (21702182 and 21873081), the Fundamental Research Funds for the Central Universities (2020XZZX002-02), and the State Key Laboratory of Clean Energy Utilization (ZJUCEU2020007).


Abstract

Description of molecular stereostructure is critical for the machine learning prediction of asymmetric catalysis. Herein we report a spherical projection descriptor of molecular stereostructure (SPMS), which allows precise representation of the molecular van der Waals (vdW) surface. The key features of SPMS descriptor are presented using the examples of chiral phosphoric acid, and the machine learning application is demonstrated in Denmark’s dataset of asymmetric thiol addition to N-acylimines. In addition, SPMS descriptor also offers a color-coded diagram that provides straightforward chemical interpretation of the steric environment.

Supporting Information



Publication History

Received: 23 July 2020

Accepted after revision: 23 October 2020

Article published online:
18 November 2020

© 2020. Thieme. All rights reserved

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