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pK_a 数据驱动的多重线性回归水解的洞察力 QSARs:适用于 perfluorinated 基乙

Jovian Lazare¹, Caroline Tebes-Stevens², Eric J Weber²

¹Oak Ridge Institute for Science and Education (ORISE), Hosted at U.S. Environmental Protection Agency, Athens, Georgia 30605, United States.

Environmental science & technology

|June 4, 2025

概括

定量结构活性关系 (QSARs) 可以估计甲基和多甲基物质 (PFAS) 的水解率. 改进的pKa预测对于准确建模水生环境中的PFAS转化至关重要.

科学领域:

环境化学环境化学
化学动力学化学动力学
预测建模预测建模

背景情况:

perfluoroalkyl 和 polyfluoroalkyl 物质 (PFAS) 广泛使用,但可以在水生环境中转化为持久的副产品.
化PFAS产生的 perfluorinated碳酸盐 (PFCAs) 和醇醇醇 (FTOHs),影响环境持久性.
准确预测PFAS转化率对于环境风险评估至关重要.

研究的目的:

为了评估现有的定量结构活性关系 (QSARs) 的预测性能,用于 perfluorinated 基乙烯水解.
确定关键的化学描述符和特性,如pKa值,影响PFAS的水解速率预测.
评估化学信息工具在PFCAs和FTOHs的pKa值估计中的准确性.

主要方法:

编制和分析了各种高化基的实验性水解数据.
使用这些编译的数据,评估了已建立的QSAR模型的预测能力.
评估了多个化学信息应用程序 (ChemAxon,SPARC,pkasolver,MolGpka,OPERA) 的性能,以估计PFCAs和FTOHs的pKa值.

主要成果:

QSAR模型证明了在不同的链长度中估计 perfluorinated alkyl esters 的半衰期的能力.
通过改善PFCA前体的计算化学描述值,可以提高水解速率的预测精度.
准确估计pKa值被认为是改善水解速率预测的关键因素,特别是对于更长链的PFAS.

结论:

现有的QSAR模型提供了预测PFAS水解速率的基本能力.
为了更可靠的水解预测,需要改进对PFCA前体的pKa值的计算.
需要进一步开发化学信息工具,以准确预测复杂的PFAS结构的pKa值,特别是长链化合物.

关键词:

在PFAS中,有很多方法.在QSAR中使用QSAR.水解是指水解过程中的水解.运动学的动力学.perfluorinated 基乙烯的化的化的化的化的化.perfluorinated 化合物是一种含有高的化合物.

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