Issue 11, 2023

Machine learning-assisted structure annotation of natural products based on MS and NMR data

Abstract

Covering: up to March 2023

Machine learning (ML) has emerged as a popular tool for analyzing the structures of natural products (NPs). This review presents a summary of the recent advancements in ML-assisted mass spectrometry (MS) and nuclear magnetic resonance (NMR) data analysis to establish the chemical structures of NPs. First, ML-based MS/MS analyses that rely on library matching are discussed, which involves the utilization of ML algorithms to calculate similarity, predict the MS/MS fragments, and form molecular fingerprint. Then, ML assisted MS/MS structural annotation without library matching is reviewed. Furthermore, the cases of ML algorithms in assisting structural studies of NPs based on NMR are discussed from four perspectives: NMR prediction, functional group identification, structural categorization and quantum chemical calculation. Finally, the review concludes with a discussion of the challenges and the trends associated with the structural establishment of NPs based on ML algorithms.

Graphical abstract: Machine learning-assisted structure annotation of natural products based on MS and NMR data

Article information

Article type
Review Article
Submitted
09 May 2023
First published
31 Jul 2023

Nat. Prod. Rep., 2023,40, 1735-1753

Machine learning-assisted structure annotation of natural products based on MS and NMR data

G. Hu and M. Qiu, Nat. Prod. Rep., 2023, 40, 1735 DOI: 10.1039/D3NP00025G

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