极性有机化合物土壤吸附系数的定量构效关系分析：取代苯胺和酚类

QSAR analysis of soil sorption coefficients for polar organic chemicals: substituted anilines and phenols.

作者信息

Liu Gousheng, Yu Jianguo

机构信息

Department of Applied Chemistry, Jiangxi Science and Technology Normal University, Nanchang 330013, P.R. China.

出版信息

Water Res. 2005 May;39(10):2048-55. doi: 10.1016/j.watres.2005.03.030.

DOI:10.1016/j.watres.2005.03.030

PMID:15913706

Abstract

Based on descriptors of n-octanol/water partition coefficients (logKow), molecular connectivity indices, and quantum chemical parameters, several QSAR models were built to estimate the soil sorption coefficients (logKoc) of substituted anilines and phenols. Results showed that descriptor logKow plus molecular quantum chemical parameters gave poor regression models. Further study was performed to improve the QSAR model by using artificial neural networks (ANNs). It showed that ANN model with suitable network architecture could make a better agreement between predicted and measured values of the soil sorption coefficients. The quality of the QSAR models confirmed the suitability of ANN to predict the soil sorption coefficients for polar organic chemicals of substituted anilines and phenols.

摘要

基于正辛醇/水分配系数（logKow）、分子连接性指数和量子化学参数的描述符，构建了多个定量构效关系（QSAR）模型，以估算取代苯胺和酚类的土壤吸附系数（logKoc）。结果表明，描述符logKow加上分子量子化学参数得到的回归模型效果不佳。通过使用人工神经网络（ANN）进行了进一步研究以改进QSAR模型。结果表明，具有合适网络结构的ANN模型能够使土壤吸附系数的预测值与测量值之间具有更好的一致性。QSAR模型的质量证实了ANN适用于预测取代苯胺和酚类等极性有机化学品的土壤吸附系数。