病毒MolAnalyte:一个人工智能驱动的在代谢物注释工具
Guilin Hu1, Jameel Hizam Alafifi1, Minghua Qiu1
1State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, Yunnan 650201, China.
Analytical chemistry
|December 17, 2025
概括
一个名为VirMolAnalyte的新工具分析了核磁共振 (NMR) 光谱,用于在没有实验数据的情况下识别代谢物. 这种深度学习方法通过提高NMR光谱分析效率来增强药物发现和代谢学研究.
科学领域:
- 代谢学 代谢学 代谢学
- 药物发现 药物发现 药物发现
- 频谱学是一种光谱学.
背景情况:
- 代谢物对生物功能和药物开发至关重要.
- 核磁共振 (NMR) 光谱是代谢物结构识别的关键.
- 目前的NMR光谱注释需要昂贵的实验数据.
研究的目的:
- 介绍VirMolAnalyte,这是一个用于分析13C DEPT NMR光谱而没有实验数据的工具.
- 以计算方式实现高质量的代谢物结构分析.
- 克服NMR分析中的实验数据依赖性的局限性.
主要方法:
- VirMolAnalyte使用深度神经网络从13C DEPT NMR光谱中提取分子指纹.
- 在多维in silico数据库搜索中使用过器-评估器策略.
- 该工具通过计算处理NMR光谱数据.
主要成果:
- 过器-评估器策略在DB6123上实现了94.2%的Top 1准确性,超过了传统方法.
- 在一个大型的in silico代谢物数据库中,VirMolAnalyte显示了90.0%的Top 1和100.0%的Top 5准确性.
- 该工具在从NMR光谱中识别代谢物时表现出高准确度.
结论:
- VirMolAnalyte提供了一种创新的计算方法,用于使用NMR数据进行代谢物分析.
- 该工具显著推进了新药发现和代谢学研究.
- VirMolAnalyte减少了对NMR光谱注释的昂贵实验数据的依赖.
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