RT-Pred: 一个用于准确,定制的液体染色学预测化学品的保留时间的Web服务器
Mahi Zakir1, Marcia A LeVatte2, David S Wishart3
1Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada.
Journal of chromatography. A
|March 1, 2025
概括
一个新的网络服务器RT-Pred使用机器学习准确地预测化学保留时间 (RTs) 在各种染色学方法中. 该工具通过启用定制模型训练和数据库搜索来增强液态染色学-质谱学 (LC-MS) 中的化合物识别.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
- 生物化学 生化学
背景情况:
- 高性能液态染色体质谱法 (HPLC-MS) 对于化学分析至关重要,依靠保留时间 (RT) 进行识别.
- 机器学习 (ML) 模型可以从化学结构中预测RTs,但它们的应用受RTs对单个染色学方法 (CMs) 的高特异性限制.
- 数以百计的CMs存在,每个都有独特的参数,这对通用RT预测构成了重大挑战.
研究的目的:
- 开发一个用户友好的网络服务器,RT-Pred,用于预测化学保留时间在广泛的染色学设置.
- 使用户能够使用自己的实验数据和染色学条件来训练定制的RT预测模型.
- 通过提供准确的RT预测和促进数据库搜索,改进LC-MS中的化合物识别.
主要方法:
- 开发RT-Pred网络服务器,结合内部CM特定的ML预测器.
- 实现一个用户友好的界面,以训练定制RT预测模型与用户提供的RT数据.
- 整合一个空白/保留/除分类器以提高预测准确度,以及超过40个CM的数百万预测RT数据库.
主要成果:
- 在20种不同的染色学方法中,RT-Pred实现了0.95的平均确定系数 (R2).
- 与现有的RT预测器相比,Web服务器表现出更高的性能,平均绝对错误较低,R2得分更高.
- RT-Pred准确地识别出溶解于空体中的或保留在柱上的化合物,显著提高了整体性能.
结论:
- RT-Pred提供了一种多功能且准确的解决方案,用于预测各种染色学条件的保留时间,克服了以前的限制.
- 训练定制模型和搜索广泛数据库的能力使RT-Pred成为LC-MS中化合物识别的强大工具.
- RT-Pred是免费访问的,促进更广泛的采用和推进化学分析能力.
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