基于MS和NMR数据的自然产品的机器学习辅助结构注释
Guilin Hu1,2, Minghua Qiu1,2
1State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, Yunnan, China. mhchiu@mail.kib.ac.cn.
Natural product reports
|July 31, 2023
概括
机器学习 (ML) 使用质谱 (MS) 和核磁共振 (NMR) 数据加速自然产品 (NP) 结构的阐明. 这篇评论强调了ML在分析复杂的NP结构方面的进步.
科学领域:
- 化学 化学 化学
- 计算化学计算化学
- 生物化学 生物化学
背景情况:
- 自然产品 (NPs) 在药物发现中至关重要,但它们的结构阐明具有挑战性.
- 机器学习 (ML) 为分析复杂的化学数据提供了强大的工具.
- 最近的进展显著改善了ML在NP结构确定中的应用.
研究的目的:
- 审查最近的ML辅助分析质谱 (MS) 和核磁共振 (NMR) 数据的进展,以阐明NP结构.
- 在MS/MS分析中总结ML的应用,无论是依赖图书馆还是独立图书馆.
- 在基于NMR的NP结构研究中探索ML的作用.
主要方法:
- 对MS/MS数据应用的ML算法的审查,包括相似性计算,碎片预测和分子指纹.
- 在没有先前库匹配的情况下对MS/MS结构注释进行ML技术的分析.
- 检查在NMR数据分析中的ML应用:预测,功能组识别,结构分类和量子化学计算.
主要成果:
- ML显著提高了从MS/MS和NMR数据中阐明NP结构的准确性和效率.
- 机器学习算法可以预测MS/MS碎片并识别分子指纹,帮助图书馆匹配.
- 通过预测和分类,ML可以在没有图书馆的情况下进行结构性注释,并帮助基于NMR的研究.
结论:
- 机器学习是自然产品结构阐明的转变工具,提高了速度和准确性.
- 在开发强大的机器学习模型和整合各种数据类型方面仍然存在挑战.
- 未来的趋势指向更复杂的ML算法和NP研究中的更广泛的应用.
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