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

doi:10.1039/d3np00025g

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	Machine learning-assisted structure annotation of natural products based on MS and NMR data
	Hu,Guilin; Qiu,Minghua
	2023
发表期刊	NATURAL PRODUCT REPORTS
ISSN	1460-4752
卷号	40 期号:11 页码:1735-1753
摘要	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.
关键词	H-1-NMR CHEMICAL-SHIFTS STRUCTURE-BASED PREDICTIONS TANDEM MASS-SPECTRA STRUCTURE ELUCIDATION NEURAL-NETWORK METABOLITE IDENTIFICATION COMP
DOI	10.1039/d3np00025g
收录类别	SCI
WOS记录号	WOS:001042902700001
引用统计
文献类型	期刊论文
条目标识符	http://ir.kib.ac.cn/handle/151853/75274
专题	中国科学院昆明植物研究所
推荐引用方式 GB/T 7714	Hu,Guilin,Qiu,Minghua. Machine learning-assisted structure annotation of natural products based on MS and NMR data[J]. NATURAL PRODUCT REPORTS,2023,40(11):1735-1753.
APA	Hu,Guilin,&Qiu,Minghua.(2023).Machine learning-assisted structure annotation of natural products based on MS and NMR data.NATURAL PRODUCT REPORTS,40(11),1735-1753.
MLA	Hu,Guilin,et al."Machine learning-assisted structure annotation of natural products based on MS and NMR data".NATURAL PRODUCT REPORTS 40.11(2023):1735-1753.

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