在MALDI和MALDI-2质谱成像中预测组织中药物的电离行为,使用机器学习
Krischan Koerfer1, Jonas Oldopp1, Klaus Dreisewerd1
1Institute of Hygiene, University of Muenster, Muenster, North Rhine-Westphalia 48149, Germany.
Analytical chemistry
|October 21, 2025
概括
机器学习预测了Matrix辅助激光脱离/电离质谱 (MALDI-MS) 和MALDI-2成像 (MSI) 中的电离效率. 这种方法有助于制药研究,改善了对组织中的药物和代谢物的检测极限预测.
科学领域:
- 分析化学 分析化学
- 生物化学 生物化学
- 计算化学计算化学
背景情况:
- 矩阵辅助激光脱/电离质谱法 (MALDI-MS) 和其成像变体 (MSI) 对于分析生物分子,特别是药物分布在制药研究中至关重要.
- 在MALDI(-2) 分析中预测离子产量具有挑战性,使药物代谢物的检测极限的确定变得复杂化.
- 像MALDI-2这样的定位技术可以增强信号强度,但不能完全解决离子产量可预测性问题.
研究的目的:
- 开发一种机器学习 (ML) 模型,用于预测MALDI-MSI中的电离效率.
- 解决MALDI-MSI中不可预测的离子产量的挑战,从而改善制药研究的规划.
- 探索ML在预测一般MALDI-MSI应用中的电离性的实用性.
主要方法:
- 利用了1200个药物类化合物的数据集,以及现有的MALDI和MALDI-2数据 (正和负离子模式).
- 训练了六种不同的ML模型,使用化合物的物理化学特性和2D结构.
- 采用SHAP分析来了解各种参数对预测准确性的贡献.
主要成果:
- 机器学习模型已成功开发,用于预测MALDI的电离效率{-2) -MSI.
- SHAP分析表明,涉及众多参数的多因素方法对于准确的预测至关重要,而不是少数特定的功能组.
- 该研究证明了使用ML来预测离子产量的可行性,比目前的方法有显著的改进.
结论:
- 机器学习为预测MALDI(-2) -MSI中的离子产量提供了一个强大的工具,增强了它在制药研究中的实用性.
- 这种基于ML的方法可以通过提供更好的药物和代谢物的检测极限估计,帮助优化研究设计.
- 开发的方法对于在药品之外的各种MALDI(-2) -MSI应用中预测电离性具有更广泛的含义.
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