NDLI: A novel quantitative structure–activity relationship model for prediction of biomagnification factor of some organochlorine pollutants

Content Provider	Springer Nature Link
Author	Fatemi, Mohammad Hossein Baher, Elham
Copyright Year	2009
Abstract	The biomagnification factor (BMF) is an important property for toxicology and environmental chemistry. In this work, quantitative structure–activity relationship (QSAR) models were used for the prediction of BMF for a data set including 30 polychlorinated biphenyls and 12 organochlorine pollutants. This set was divided into training and prediction sets. The result of diversity test reveals that the structure of the training and test sets can represent those of the whole ones. After calculation and screening of a large number of molecular descriptors, the methods of stepwise multiple linear regression and genetic algorithm (GA) were used for the selection of most important and significant descriptors which were related to BMF. Then multiple linear regression and artificial neural network (ANN) techniques were applied as linear and non-linear feature mapping techniques, respectively. By comparison between statistical parameters of these methods it was concluded that an ANN model, which used GA selected descriptors, was superior over constructed models. Descriptors which were used by this model are: topographic electronic index, complementary information content, XY shadow/XY rectangle and difference between partial positively and negatively charge surface area. The standard errors for training and test sets of this model are 0.03 and 0.20, respectively. The degree of importance of each descriptor was evaluated by sensitivity analysis approach for the nonlinear model. A good results (Q $^{2}$ = 0.97 and SPRESS = 0.084) is obtained by applying cross-validation test that indicating the validation of descriptors in the obtained model in prediction of BMF for these compounds.
Starting Page	343
Ending Page	352
Page Count	10
File Format	PDF
ISSN	13811991
Journal	Molecular Diversity
Volume Number	13
Issue Number	3
e-ISSN	1573501X
Language	English
Publisher	Springer Netherlands
Publisher Date	2009-02-14
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Genetic algorithm Artificial neural network Biomagnification factor Organochlorine compounds Quantitative structure–activity relationship Sensitivity analysis Pharmacy Polymer Sciences Organic Chemistry Biochemistry
Content Type	Text
Resource Type	Article
Subject	Organic Chemistry Medicine Drug Discovery Molecular Biology Physical and Theoretical Chemistry Information Systems Catalysis Inorganic Chemistry

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