Elucidating Structures of Complex Organic Compounds Using a Machine Learning Model Based on the 13C NMR Chemical Shifts

Anan Wu; Qing Ye; Xiaowei Zhuang; Qiwen Chen; Jinkun Zhang; Jianming Wu; Xin Xu

doi:10.1021/prechem.3c00005

Precision Chemistry (Feb 2023)

Elucidating Structures of Complex Organic Compounds Using a Machine Learning Model Based on the 13C NMR Chemical Shifts

Anan Wu,
Qing Ye,
Xiaowei Zhuang,
Qiwen Chen,
Jinkun Zhang,
Jianming Wu,
Xin Xu

Affiliations

Anan Wu: Department of Chemistry, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen University, Xiamen, China
Qing Ye: Department of Chemistry, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen University, Xiamen, China
Xiaowei Zhuang: Department of Chemistry, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen University, Xiamen, China
Qiwen Chen: Department of Chemistry, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen University, Xiamen, China
Jinkun Zhang: Department of Chemistry, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen University, Xiamen, China
Jianming Wu: Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai, China
Xin Xu: Collaborative Innovation Center of Chemistry for Energy Materials, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, MOE Key Laboratory of Computational Physical Sciences, Department of Chemistry, Fudan University, Shanghai, China

DOI: https://doi.org/10.1021/prechem.3c00005
Journal volume & issue: Vol. 1, no. 1
pp. 57 – 68

Abstract

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We present a protocol that combines the support vector machine (SVM) model with accurate 13C chemical shift calculations at the xOPBE/6-311+G(2d,p) level of theory, denoted as SVM-M (i.e., SVM for magnetic property). We show here that this SVM-M protocol is a versatile tool for identifying the structural and stereochemical assignment of complex organic compounds with high confidence. Of particular significance is that, by utilizing the dual role of the decision values in SVM, the present SVM-M protocol provides an accurate yet efficient solution to simultaneously handle the classification issue (i.e., “is a given structure correct or incorrect?”) and the comparison-based problem (i.e., “which structure is more likely to be correct or wrong among several candidate structures?”). A significantly high success rate has been reached (i.e., ∼100% on a set of 760 sample molecules with 15928 13C chemical shifts), which makes the SVM-M protocol a powerful tool for routine applications in structural and stereochemical assignments, as well as in detecting mis-assignments, for complex organic compounds, including natural products.

Published in Precision Chemistry

ISSN: 2771-9316 (Online)
Publisher: American Chemical Society
Country of publisher: United States
LCC subjects: Science: Chemistry
Website: https://pubs.acs.org/journal/pcrhej

About the journal