Machine learning-assisted structure annotation of natural products based on MS and NMR data
文献类型：期刊论文
| 作者 | Hu,Guilin; Qiu,Minghua
|
| 刊名 | NATURAL PRODUCT REPORTS
 |
| 出版日期 | 2023 |
| 卷号 | 40期号:11页码:1735-1753 |
| 关键词 | H-1-NMR CHEMICAL-SHIFTS STRUCTURE-BASED PREDICTIONS TANDEM MASS-SPECTRA STRUCTURE ELUCIDATION NEURAL-NETWORK METABOLITE IDENTIFICATION COMP |
| ISSN号 | 1460-4752 |
| DOI | 10.1039/d3np00025g |
| 英文摘要 | 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. |
| WOS记录号 | WOS:001042902700001 |
| 源URL | [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. |
入库方式： OAI收割
来源：昆明植物研究所
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