描述器数据库选择对利用溶解参数模型模拟毛细菌和微乳液电动色谱中的保留因子的影响
Sanka N Atapattu1, Colin F Poole2
1CanAm Bioresearch Inc., Winnipeg MB R3T 0P4, Canada.
Journal of chromatography. A
|May 1, 2025
概括
韦恩州立大学 (WSU) 描述器数据库和机器学习方法在毛细电动力学染色学中提供比亚伯拉罕描述器或组贡献方法更准确的化合物保留预测.
科学领域:
- 分析化学 分析化学
- 分离科学 分离科学
- 物理化学 物理化学
背景情况:
- 亚伯拉罕的溶解参数模型对于预测毛细管和微乳液电动色谱中的化合物保留至关重要.
- 实验性化合物描述符通常来自亚伯拉罕和韦恩州立大学 (WSU) 数据库.
- 当实验值无法获得时,计算方法为估计描述符提供了一个替代方案.
研究的目的:
- 为了比较复合描述符的质量从实验 (亚伯拉罕,WSU) 和计算 (组贡献,机器学习) 的方法.
- 评估这些描述器在毛细菌和微乳液电动色谱学中建模保留因子时的准确性.
- 确定描述符质量对保留模型可靠性的影响.
主要方法:
- 利用亚伯拉罕的溶解参数模型进行保留预测.
- 来自亚伯拉罕和WSU实验数据库的比较描述符集.
- 评估计算方法,包括组贡献和机器学习方法.
- 使用标准误差和确定系数评估模型性能.
主要成果:
- 与亚伯拉罕数据库相比,WSU描述器数据库提供了更准确的化合物保留描述.
- 基于机器学习的描述器在建模保留因素方面表现出比组贡献描述器更高的准确性.
- WSU描述器产生了从0.046-0.116和R2到0.976-0.996.6的模型标准误差.
- 机器学习描述器导致模型标准误差从0.086-0.116和R2从0.979-0.981.981.
结论:
- 描述器质量显著影响了毛细管电动力学染色学中保留建模的准确性.
- 华盛顿大学的数据库和机器学习方法代表了获得描述符的优越选择,以准确预测保留率.
- 这些发现指导了为增强色谱方法开发选择最佳描述源的指导.
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