基于机器学习的保留时间预测工具,用于常规LC-MS数据分析
Sofiia A Dymura1, Oleksandr O Viniichuk1,2, Kostiantyn P Melnykov1,2
1Enamine Ltd. (www.enamine.net), Winston Churchill Street 78, Kyiv 02094, Ukraine.
Journal of chemical information and modeling
|July 16, 2025
概括
我们开发了一个图形神经网络模型,用于在液态染色体质谱法 (LC-MS) 中准确预测保留时间 (RT). 这增强了化学合成数据分析,并已集成到现有的工具包中.
科学领域:
- 分析化学 分析化学
- 计算化学计算化学
- 化学信息学 化学信息学
背景情况:
- 液体染色学-质谱学 (LC-MS) 对于化学合成分析至关重要.
- 准确的保留时间 (RT) 预测对于改善LC-MS数据处理至关重要.
- 来自化学合成的大型内部数据集为模型开发提供了机会.
研究的目的:
- 开发和评估一个新的RT预测模型用于LC-MS数据分析.
- 利用内部实验数据和先进的神经网络架构.
- 提高内部LC-MS工作流程的分析能力.
主要方法:
- 使用GATv2Conv + DL架构开发一个图形神经网络 (NN) 模型.
- 在化学合成实验的大型内部数据集上训练NN模型.
- 使用METLIN SMRT数据集和120秒LC-MS方法对模型的评估.
主要成果:
- 开发的RT预测模型实现了2.48秒的平均绝对误差 (MAE).
- 超过95%的预测错误发生在RT ± 7.12秒到RT ± 9.58秒的间隔内.
- 该模型成功地集成到内部LC-MS分析工具包中.
结论:
- GATv2Conv + DL图形NN模型为LC-MS分析提供准确的RT预测.
- 该模型显著提高了化学合成工作流程的预测和分析能力.
- 一个20,000个数据点的子集公开发布,以支持社区研究和基准测试.
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