通过机器学习和定制指纹工程来预测差异性离子移动的可通用在预测
Cailum M K Stienstra1, Christopher R M Ryan1, Daniel Demczuk1
1Department of Chemistry, University of Waterloo, Waterloo, Ontario N2L 3G1, Canada.
Analytical chemistry
|April 10, 2025
概括
一个新的机器学习模型使用化学结构预测差分移动性光谱法 (DMS) 中的离子行为. 该工具通过预测离子传输曲线来加速分析,从而改善分析工作流.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
- 机器学习 机器学习
背景情况:
- 不同移动性光谱 (DMS) 与质谱相结合,可提高分离化学物种 (包括异构体) 的选择性.
- 反映离子流动性的DMS分散曲线对于优化分析物传输至关重要,但缺乏快速的一般预测工具.
- 预测离子分散行为对于推进分析工作流程和仪器自动化至关重要.
研究的目的:
- 开发一种机器学习 (ML) 模型,用于在DMS中对离子分散曲线的概括预测.
- 创建一个in silico功能添加管道,用于从SMILES代码生成分子描述符.
- 提高DMS分析的准确性和适用性,特别是在溶剂修饰的环境中.
主要方法:
- 利用了1141个离子和离子在N2和N2/甲醇环境中的分散曲线测量的数据集.
- 开发了一个"in silico"功能添加管道,从SMILES代码中计算1591个RDKit和Mordred描述符.
- 使用累积密度函数 (CDF) 来规范分子描述符和应用ML模型,包括可解释性技术 (SHAP).
主要成果:
- 在分散曲线预测中获得了2.1 ± 0.2 V的平均绝对误差 (MAE),超过了以前的方法.
- 功能添加管道展示了功能集生成的半确定性过程.
- 该模型成功地预测了溶剂修饰环境中的分散曲线,这是一个新的能力.
结论:
- 开发的ML模型为DMS分散曲线提供了一个快速,通用的预测工具.
- 这种方法可以加速和潜在地自动化预先选在复杂的分析工作流程,如2DLC×DMS.
- 该工具通过自动识别传动窗口来增强DMS仪器的"自动驾驶"潜力.
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