机器学习用于预测通过CMPA技术染色学分离的奇拉分析物的保留时间
Xiong Liu1, He Zhang1, Wei Zhou2
1School of Chemistry and Chemical Engineering, Hunan University of Science and Technology, Xiangtan 411201, Hunan, PR China.
机器学习准确地预测了使用奇拉移动相添加剂 (CMPA) 技术的奇拉分析剂保留时间. 这加快了染色体分离方法的开发速度,减少了昂贵的试错实验.
科学领域:
- 分析化学 分析化学
- 染色体学 染色体学 是一种染色学.
- 机器学习应用 机器学习应用
背景情况:
- 状移动相添加剂 (CMPA) 技术对于分离反体至关重要.
- 开发 enantioseparation 方法往往是耗时且昂贵的,因为它需要大量的试错.
研究的目的:
- 开发一种机器学习模型,用于预测奇拉分析剂保留时间.
- 为了加快CMPA技术的开发,以进行分离.
主要方法:
- 从HPLC分离中采集的反体保留时间,使用环极素衍生物作为CMPA.
- 计算的分子描述器,用于奇拉分析剂和CMPA.
- 开发和评估机器学习模型,使用R2来评估性能.
主要成果:
- CatBoost机器学习模型在预测保留时间方面表现出很高的准确性.
- 该模型有效地预测了奇拉分析物的可分离性.
- 实现了保留时间和可分离性的精确预测.
结论:
- 机器学习,特别是CatBoost模型,提供了一种快速有效的方法来预测enantioseparation.
- 这种基于ML的方法显著降低了开发CMPA技术的实验负担.
- 促进了更快,更具成本效益的染色学反分离.
更多相关视频
13:35A Convenient Method for Extraction and Analysis with High-Pressure Liquid Chromatography of Catecholamine Neurotransmitters and Their Metabolites
Published on: March 1, 2018
10:14Chromatographic Fingerprinting by Template Matching for Data Collected by Comprehensive Two-Dimensional Gas Chromatography
Published on: September 2, 2020
相关概念视频
Chromatography: Introduction
The phase in which the compounds linger or on which the compounds adsorb is called the stationary phase, whereas the mobile phase is the solvent that carries the solutes to be analyzed. In traditional column chromatography, the mixture flows through the stationary phase, and the compounds partition between the stationary and mobile phases...
Principles Of Column Chromatography
Chromatographic Methods: Terminology
High-Performance Liquid Chromatography: Introduction
In HPLC, two phases play a critical role in the separation process:
High-Performance Liquid Chromatography: Instrumentation
Ion-Exchange Chromatography
