电子电离的比较评估 质谱预测方法
Sriram Devata1,2, Henderson James Cleaves2,3, John Dimandja4
1International Institute of Information Technology, Hyderabad 500 032, India.
Journal of the American Society for Mass Spectrometry
|June 30, 2023
概括
他们比较了用于预测电子电离质谱的计算方法,包括量子化学 (QCEIMS) 和机器学习 (CFM-EI,NEIMS). 没有任何一种方法是普遍最好的,光谱距离函数会影响化合物识别性能.
科学领域:
- 计算化学的计算化学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 电子电离质谱 (EI-MS) 对于化学识别至关重要.
- 预测EI质谱的计算方法已经大大进步.
- 关键的方法包括量子化学 (QCEIMS) 和机器学习 (CFM-EI,NEIMS).
研究的目的:
- 为了比较QCEIMS,CFM-EI和NEIMS的光谱预测准确度.
- 评估这些方法在化合物识别中的性能.
- 分析光谱距离函数对识别成功的影响.
主要方法:
- 对三个著名的计算EI质谱预测方法进行比较分析.
- 对光谱预测准确性的评估.
- 使用不同的光谱距离指标评估化合物识别性能.
主要成果:
- 没有一个单一的计算方法 (QCEIMS,CFM-EI,NEIMS) 在所有评估的方面都表现出优异的性能.
- 选择光谱距离函数显著影响化合物识别的成功率.
- 性能因具体的数据集和评估标准而异.
结论:
- 选择最佳的计算EI质谱预测方法取决于上下文.
- 仔细考虑光谱距离函数对于使用计算方法准确识别化合物至关重要.
- 需要进一步的研究来完善方法,并了解它们在各种化学空间中的局限性.
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