用机器学习引导的SABRE超极化对酸酸在酸水中的α-酸的优化
Erica Curran1,2, Sina Sadeghi3, Stephen J McBride1,2
1Department of Chemistry, NC State University, Raleigh, North Carolina 27695, United States.
Analytical chemistry
|December 9, 2025
概括
机器学习优化信号放大通过可逆交换 (SABRE) 超极化. 这种方法在代谢监测应用中显著增加了极化水平,节省了实验时间.
科学领域:
- 核磁共振 (NMR) 光谱学 核磁共振 (NMR) 光谱学
- 超极化技术是一种超极化技术.
- 代谢成像成像 - 代谢成像
背景情况:
- 通过可逆交换信号放大 (SABRE) 是一种高效的超极化方法.
- 最近像Ace-SABRE这样的进步使生物相容的解决方案能够用于代谢监测.
- 扩大Ace-SABRE的基板范围对于更广泛的应用至关重要.
研究的目的:
- 应用机器学习 (ML),特别是贝叶斯优化 (BO),以加速SABRE优化.
- 为了建模复杂的SABRE动态,并减少实验时间.
- 获得化学洞察力,以预测改进的样本成分.
主要方法:
- 贝叶斯优化 (BO) 应用于SABRE的四个关键输入参数.
- 开发一个ML模型来模拟SABRE动态.
- 基于ML指导的参数优化的实验验证.
主要成果:
- 对1-Cα-谷氨酸 (AKG) 达到6.6%的最大观察到的自由极化.
- 显著改善了原来的平均自由极化值~0.90%.
- ML模型提供了化学洞察力,从而提高了两极分化.
结论:
- 基于机器学习的优化是加速SABRE开发的强大工具.
- 这种方法可以有效地探索SABRE参数空间.
- 开发的模型促进了对代谢监测的极化增加.
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