在非目标LC-HRMS方法开发中使用物理化学和结构数据进行化合物选择的新型化学空间依赖战略.
Lapo Renai1, Viktoriia Turkina1, Tobias Hulleman2,3
1Van't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, 1090 GD Amsterdam, The Netherlands.
概括
一个新的数据驱动策略通过使用液态染色学高分辨率质谱法 (LC-HRMS) 无偏向地采样各种结构进行非目标分析 (NTA) 来增强化学分析. 这种方法扩大了可测量的化学空间覆盖范围,用于环境和暴露研究.
科学领域:
- 环境化学环境化学
- 分析化学 分析化学
- 计算化学计算化学
背景情况:
- 虚拟化学空间正在迅速扩大,包括环境污染物.
- 使用LC-HRMS进行非向分析 (NTA) 对于测量广泛的化学区域至关重要.
- 目前使用内部标准的NTA方法与大型,多样化的化学空间作斗争.
研究的目的:
- 为开发NTA LC-HRMS方法开发开发数据驱动的战略,以不偏地采样各种化学结构.
- 在候选化合物清单中最大限度地提高物理化学和结构的多样性.
- 为了提高NTA可测量的化学空间覆盖率.
主要方法:
- 利用数据驱动的工作流来从广泛的化学空间 (例如,美国环保署的CompTox) 进行公正的采样.
- 使用预先计算的PubChem描述符 (例如分子量,XLogP) 来最大限度地提高多样性.
- 使用分子指纹预测LC-HRMS兼容性,以提高移动性和电离效率.
主要成果:
- 生成可测量的化合物清单 (MCLs),覆盖范围广泛,异质.
- 与传统方法相比,实现了更大的化学覆盖范围,并预测了LC-HRMS的适用性.
- 证明了增强的NTA方法开发,验证和边界评估能力.
结论:
- 提出的战略为扩大NTA可测量的化学空间提供了一个强大的框架,同时保持多样性.
- 这种方法为环境和暴露污染物分析提供了更广泛的覆盖范围.
- 允许对化学物质暴露进行更全面的评估.
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