Hits:

Indexed by:期刊论文

Date of Publication:2015-01-01

Journal:CHEMOSPHERE

Included Journals:SCIE、EI、PubMed

Volume:119

Page Number:438-444

ISSN No.:0045-6535

Key Words:Soil organic carbon normalized sorption coefficient (K-oc); Quantitative structure-activity relationship (QSAR); Multiple linear regression (MLR); Predictive ability

Abstract: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.