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外源化合物在鱼体内生物半减期的QSAR模型
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  • 英文篇名:QSAR Models for Predicting Biological Half-life of Xenobiotics in Fish
  • 作者:张文灏 ; 陈景文 ; 徐童 ; 王雅
  • 英文作者:Zhang Wenhao;Chen Jingwen;Xu Tong;Wang Ya;Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education),School of Environmental Science and Technology,Dalian University of Technology;
  • 关键词:外源化合物 ; 生物半减期 ; QSAR ; 支持向量机
  • 英文关键词:xenobiotics;;biological half-life;;QSAR;;SVM
  • 中文刊名:生态毒理学报
  • 英文刊名:Asian Journal of Ecotoxicology
  • 机构:工业生态与环境工程教育部重点实验室大连理工大学环境学院;
  • 出版日期:2019-06-15
  • 出版单位:生态毒理学报
  • 年:2019
  • 期:03
  • 基金:国家自然科学基金(21661142001)
  • 语种:中文;
  • 页:95-103
  • 页数:9
  • CN:11-5470/X
  • ISSN:1673-5897
  • 分类号:X171.5
摘要
生物半减期(t1/2)是评价外源化合物在鱼体内蓄积效应的重要参数。实验测定t_(1/2)的速度慢、成本高,难以满足化学品生态风险评价的需求,需要发展替代实验的模型预测方法。本研究搜集了653种化合物t1/2实测值,采用多元线性回归(MLR)和支持向量机(SVM) 2种方法,建立了鱼体logt1/2的定量构效关系(QSAR)预测模型。MLR模型的校正决定系数(R(adj)~2)为0.751,均方根误差(RMSE_(train))为0.587,去一法交叉验证系数(Q_(LOO)~2)为0.735,外部验证系数(Q_(ext)~2)为0.682,这表明模型具有较好的拟合度、稳健性和预测能力。SVM模型具有更好的拟合和预测能力(R_(adj)~2=0.839,RMSE_(train)=0.457,Q_(ext)~2=0.708)。采用Williams法对模型的应用域进行表征。所建模型可用于预测多环芳烃、多氯联苯、多溴联苯醚、有机磷农药、药物等典型化合物,以及其他烷烃、环烷烃、烯烃、醇、醚、酸、酯、酮、含卤素化合物、芳香族化合物、含硫、氮、磷化合物的在鱼体内的logt1/2值。
        Biological half-life(t_(1/2)) of chemicals is a key parameter for assessing bioaccumulation of xenobiotics.As the number of synthetic chemicals is huge,it is not practical to experimentally measure their t_(1/2) values one by one. It is important to develop alternative methods to predict the t_(1/2) values of xenobiotics. In this study,t_(1/2) values of 653 chemicals in fish were collected,and multiple linear regression(MLR) and support vector machine( SVM)methods were adopted to develop quantitative structure-activity relationship(QSAR) models for predicting t_(1/2). For the MLR model,the adjusted determination coefficient(R_(adj)~2) is 0.751,the root-mean-square error( RMSE) is 0.587,leave-one-out cross validated coefficient(Q_(LOO)~2) is 0.735 and external explained variance(Q_(ext)~2) is 0.682,which indicate that the MLR model has high goodness of fit,robustness,and predictive ability. The results of the SVM model also show high goodness of fit and good predictive ability( R_(adj.train)~2= 0.839,RMSEtrain= 0.457,Q_(ext)~2=0.708). The model application domains were characterized by the Williams plot. The obtained models can be used to predict logt_(1/2) of chemicals including polycyclic aromatic hydrocarbons,polychlorinated biphenyls,poly brominated diphenyl ethers,pesticides,pharmaceuticals,alkanes,naphthenic hydrocarbons,alkenes,alcohols,ethers,acids,esters,ketones,halogenated compounds,aromatics,organosulfur compounds,organonitrogen compounds and organophosphorus compounds.
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