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

Literature Information

Publication Date 2023-07-31
DOI 10.1039/D3NP00025G
Impact Factor 13.423
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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.

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Source Journal

Natural Product Reports

Natural Product Reports
CiteScore: 21.2
Self-citation Rate: 3.9%
Articles per Year: 64

Natural Product Reports (NPR) is a critical review journal that stimulates progress in all areas of natural products research, including isolation, structural and stereochemical determination, biosynthesis, biological activity and synthesis. The scope of the journal is very broad, and many reviews discuss the role of natural products in the wider bioinorganic, bioorganic and chemical biology communities. Areas covered include the following: Enzymology Nucleic acids Genetics Chemical ecology Carbohydrates Primary and secondary metabolism Analytical techniques NPR articles are designed to give an interesting insight into the topic, focusing on the key developments that have shaped a field. Authors are encouraged to include their own perspective on developments, trends and future directions. Articles providing a very comprehensive overview or exhaustive list of previous literature and lacking critical insight are generally not suitable for publication in NPR. Meta-analyses of previously published data using existing tools can be included, however NPR articles should not include any new methods or data.

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