构建一个质谱库,包含使用机器学习模型准备的预测电子电离质谱,并开发一种高效的搜索方法
Ayumi Kubo1, Azusa Kubota1, Haruki Ishioka1
1JEOL Ltd., 3-1-2 Musashino, Akishima, Tokyo 196-8558, Japan.
Mass spectrometry (Tokyo, Japan)
|May 30, 2023
概括
一个新的机器学习模型从化学结构中预测电子电离质谱,扩大化合物识别能力. 这通过创建一个大型的光谱数据库来推进气体染色学/质谱分析,减少错误阳性并提高准确性.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
- 化学信息学 化学信息学
背景情况:
- 电子电离 (EI) 质谱仪对于气相色谱 (GC/MS) 中的化合物鉴定至关重要.
- 目前的EI质谱库是有限的,阻碍了许多化合物的识别,并导致潜在的错误阳性.
- 现有的复合数据库缺乏全面的EI质谱数据.
研究的目的:
- 开发一种能够从化学结构中预测EI质谱的机器学习模型.
- 创建一个广泛的数据库,预测数百万化合物的EI质谱.
- 为了提高EI质谱库搜索的准确性和效率.
主要方法:
- 使用化学公式和已知的EI质谱来训练机器学习模型.
- 使用训练模型来预测PubChem数据库中的化合物的EI质谱.
- 将预测的光谱集成到一个全面的质谱库中,以改进搜索.
主要成果:
- 成功开发了一种机器学习模型,可以从化学结构中准确预测EI质谱.
- 一个预测EI质谱数据库的生成,该数据库包含来自PubChem的1亿种化合物.
- 通过使用扩展的图书馆来提高图书馆搜索时间和准确性的演示.
结论:
- 开发的机器学习方法显著扩大了GC/MS分析中可识别化合物的范围.
- 预测EI质谱数据库为化学结构阐明提供了宝贵的资源.
- 这种方法解决了传统图书馆搜索的局限性,减少了识别错误并改善了分析工作流.
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