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开发定量结构属性关系模型和用于预测土壤吸附系数 (logK_OC) 的工具

Xianhai Yang¹, Yue Yang¹, Peter Watson²

¹School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China.

Environmental pollution (Barking, Essex : 1987)

|January 17, 2025

概括

预测土壤有机碳吸附系数 (K_OC) 对化学物质管理至关重要. 这项研究使用机器学习开发了准确的定量结构-属性关系 (QSPR) 模型,创建了一个用户友好的工具,用于可靠的K_OC估计.

科学领域:

环境化学环境化学
计算化学计算化学
毒理学毒理学毒理学

背景情况:

土壤/沉积物有机碳吸附系数 (K_OC) 对于评估环境中的化学行为至关重要.
实验性K_OC确定是昂贵和耗时的,需要预测方法.
定量结构-属性关系 (QSPR) 建模为估计K_OC值提供了一个可行的替代方案.

研究的目的:

开发可靠的定量结构-属性关系 (QSPR) 模型,用于预测有机碳吸附系数 (K_OC).
创建一个用户友好的软件工具来估计K_OC值.
评估开发的QSPR模型和软件工具的预测性能.

主要方法:

使用1477个实验logK_OC值和7个机器学习算法开发QSPR模型.
确定最佳模型,包括单变量和多变量方法,有或没有logK_OW.
使用内部和外部数据集进行验证,评估合适性,稳定性和预测能力.

主要成果:

开发了三种类型的最佳模型,展示了出色的适合性 (R²_火车 > 0.700),强度 (Q² > 0.600) 和预测能力 (Q²_EXT > 0.700).
使用开发的模型创建了一个软件工具"logKOC预测器".
外部验证证实了该工具在其适用性领域内可靠地估计未知的logK_OC值.

结论:

开发的QSPR模型和"logKOC预测器"工具提供了一种可靠和有效的方法来估计logK_OC值.
这些预测模型可以通过填补K_OC的数据空白来显著帮助化学品管理.
该工具的可靠性对于模型适用范围内的物质是很高的,并以"高可靠性"标记.

关键词:

机器学习机器学习有机物质是有机物质.预测工具是一个预测工具.可靠性评估可靠性评估土壤/沉积物有机碳吸附系数土壤/沉积物有机碳吸附系数在n-Octanol/水分区系数.

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