Hu Guilin, Qiu Minghua
State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, Yunnan, China.
University of the Chinese Academy of Sciences, Beijing 100049, People's Republic of China.
Nat Prod Rep. 2023 Nov 15;40(11):1735-1753. doi: 10.1039/d3np00025g.
Covering: up to March 2023Machine 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.
截至2023年3月
机器学习（ML）已成为分析天然产物（NP）结构的常用工具。本综述总结了机器学习辅助质谱（MS）和核磁共振（NMR）数据分析在确定天然产物化学结构方面的最新进展。首先，讨论了基于库匹配的基于机器学习的MS/MS分析，其中涉及利用机器学习算法计算相似度、预测MS/MS碎片并形成分子指纹。然后，综述了无库匹配的机器学习辅助MS/MS结构注释。此外，从四个角度讨论了机器学习算法在基于核磁共振辅助天然产物结构研究中的应用案例：核磁共振预测、官能团识别、结构分类和量子化学计算。最后，本综述通过讨论基于机器学习算法的天然产物结构确定所面临的挑战和趋势来结束。
