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Molecules 2017, 22(10), 1671;

Predictive QSAR Models for the Toxicity of Disinfection Byproducts

2,3,* , 1,2,3
College of Environmental Science and Engineering, Guilin University of Technology, Guilin 541004, China
Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology, Guilin University of Technology, Guilin 541004, China
Collaborative Innovation Center for Water Pollution Control and Water Safety in Karst Area, Guilin University of Technology, Guilin 541004, China
Author to whom correspondence should be addressed.
Received: 26 September 2017 / Revised: 30 September 2017 / Accepted: 1 October 2017 / Published: 9 October 2017
(This article belongs to the Section Molecular Diversity)
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Several hundred disinfection byproducts (DBPs) in drinking water have been identified, and are known to have potentially adverse health effects. There are toxicological data gaps for most DBPs, and the predictive method may provide an effective way to address this. The development of an in-silico model of toxicology endpoints of DBPs is rarely studied. The main aim of the present study is to develop predictive quantitative structure–activity relationship (QSAR) models for the reactive toxicities of 50 DBPs in the five bioassays of X-Microtox, GSH+, GSH−, DNA+ and DNA−. All-subset regression was used to select the optimal descriptors, and multiple linear-regression models were built. The developed QSAR models for five endpoints satisfied the internal and external validation criteria: coefficient of determination (R2) > 0.7, explained variance in leave-one-out prediction (Q2LOO) and in leave-many-out prediction (Q2LMO) > 0.6, variance explained in external prediction (Q2F1, Q2F2, and Q2F3) > 0.7, and concordance correlation coefficient (CCC) > 0.85. The application domains and the meaning of the selective descriptors for the QSAR models were discussed. The obtained QSAR models can be used in predicting the toxicities of the 50 DBPs. View Full-Text
Keywords: disinfection byproduct; QSAR; validation; toxicity; drinking water disinfection byproduct; QSAR; validation; toxicity; drinking water

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Qin, L.; Zhang, X.; Chen, Y.; Mo, L.; Zeng, H.; Liang, Y. Predictive QSAR Models for the Toxicity of Disinfection Byproducts. Molecules 2017, 22, 1671.

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