在C NMR光谱和基于焦点图书馆的结构之间进行交叉模式检索
Hanyu Sun1,2, Xi Xue1, Xue Liu1
1State Key Laboratory of Bioactive Substances and Functions of Natural Medicines, Institute of Materia Medica, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing 100050, PR China.
Analytical chemistry
|April 2, 2024
概括
聚焦图书馆通过使用碳-13核磁共振 (C NMR) 光谱来改善化合物识别. 与传统的大型光谱库相比,这种方法提高了结构阐释的准确性.
科学领域:
- 分析化学 分析化学
- 计算化学计算化学
- 频谱学是一种光谱学.
背景情况:
- 化合物识别依赖于与光谱库匹配C NMR光谱.
- 以前的深度对比学习系统 (CReSS) 面临着大型,多余的库和缺乏未知的结构的局限性.
研究的目的:
- 为了提高化合物识别中的结构阐明准确性.
- 在深度学习模型中解决传统光谱库的局限性.
- 开发一种更有效的方法,用于C NMR光谱和化学结构之间的交叉模式检索.
主要方法:
- 用CMGNet生成的专注图书馆取代传统的大型图书馆.
- 使用深度对比学习系统 (CReSS) 与专注的库.
- 引入了SAmpRNN,一个循环神经网络,以放大集中图书馆.
- 在 6,471 13C NMR 频谱上评估了组合模型.
主要成果:
- 组合模型实现了54.03%的Top-10精度,显著超过了随机库 (9.17%) 的CReSS.
- 在30个随机病例中,SAmpRNN放大增加了结构识别准确度的70.0%.
- 专注图书馆 (CFLS) 提供了比传统图书馆更准确的候选结构.
结论:
- 聚焦图书馆显著提高了对13C NMR光谱和结构的交叉模式检索精度.
- 由SAmpRNN增强的CReSS和CMGNet组合模型为化合物识别提供了一个强大的工具.
- 这种方法为传统的图书馆匹配方法提供了更准确,更有效的替代方案.
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