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个人信息Personal Information
副教授
博士生导师
硕士生导师
任职 : 环境生态与工程研究生导师纵向党支部书记
性别:男
毕业院校:中科院南京土壤所
学位:博士
所在单位:环境学院
学科:环境科学
办公地点:环境楼B409
联系方式:办公电话:84707189 手机:13610848936
电子邮箱:xlqiao@dlut.edu.cn
In silico model for predicting soil organic carbon normalized sorption coefficient (K-oc) of organic chemicals
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论文类型:期刊论文
发表时间:2015-01-01
发表刊物:CHEMOSPHERE
收录刊物:SCIE、EI、PubMed
卷号:119
页面范围:438-444
ISSN号:0045-6535
关键字:Soil organic carbon normalized sorption coefficient (K-oc); Quantitative structure-activity relationship (QSAR); Multiple linear regression (MLR); Predictive ability
摘要:As a kind of in silico method, the methodology of quantitative structure-activity relationship (QSAR) has been shown to be an efficient way to predict soil organic carbon normalized sorption coefficients (K-oc) values. In the present study, a total of 824 logK(oc) values were used to develop and validate a QSAR model for predicting Koc values. The model statistics parameters, adjusted determination coefficient (R-adj(2)) of 0.854, the root mean square error (RMSE) of 0.472, the leave-one-out cross-validation squared correlation coefficient (Q(Loo)(2)) of 0.850, the external validation coefficient Q,;(t of 0.761 and the RMSEext of 0.558 were obtained, which indicate satisfactory goodness of fit, robustness and predictive ability. The squared Moriguchi octanol-water partition coefficient (MLOGP2) explained 66.5% of the logK(oc) variance. The applicability domain of the current model has been extended to emerging pollutants like polybrominated diphenyl ethers, perfluorochemicals and heterocyclic toxins. The developed model can be used to predict the compounds with various functional groups including C=C , -C C- OH, -O-, -CHO, C=O, -C=O(O), -COOH, -C6H5, -NO2, -NH2, -NH-, N-, -N-N-, -NH-C(O)-NH-, -O-C(O)-NH2, -C(O) NH2, -X(F, Cl, Br, I), -S-, -SH, S(O)(2)-, -OS(O)(2), -NH-S(O)(2), (SR)(2)PH(OR)(2) and Si . (C) 2014 Elsevier Ltd. All rights reserved.