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The accurate QSPR models to predict the bioconcentration factors of nonionic organic compounds based on the heuristic method and support vector machine [An article from: Chemosphere]
Book Details
PublisherElsevier
ISBN / ASINB000RR9IX6
ISBN-13978B000RR9IX5
AvailabilityAvailable for download now
Sales Rank99,999,999
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Chemosphere, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
The heuristic method (HM) and support vector machine (SVM) were used to build the linear and nonlinear quantitive structure-property relationship (QSPR) models for the prediction of the fish bioconcentration factors (BCF) for 122 diverse nonionic organic chemicals using the three descriptors calculated from the molecular structure alone and selected by HM. Both the linear and nonlinear model can give very satisfactory prediction results: the square of correlation coefficient R^2 was 0.929 and 0.953, the root mean square (RMS) error was 0.404 and 0.331, respectively for the whole dataset. The prediction result of the SVM model is better than that obtained by heuristic method, which proved SVM was a useful tool in the prediction of the BCF. At the same time, the HM model showed the influencing degree of different molecular descriptors on bioconcentration factors and then could improve the understanding for the bioconcentration mechanism of organic pollutants from molecular level.
Description:
The heuristic method (HM) and support vector machine (SVM) were used to build the linear and nonlinear quantitive structure-property relationship (QSPR) models for the prediction of the fish bioconcentration factors (BCF) for 122 diverse nonionic organic chemicals using the three descriptors calculated from the molecular structure alone and selected by HM. Both the linear and nonlinear model can give very satisfactory prediction results: the square of correlation coefficient R^2 was 0.929 and 0.953, the root mean square (RMS) error was 0.404 and 0.331, respectively for the whole dataset. The prediction result of the SVM model is better than that obtained by heuristic method, which proved SVM was a useful tool in the prediction of the BCF. At the same time, the HM model showed the influencing degree of different molecular descriptors on bioconcentration factors and then could improve the understanding for the bioconcentration mechanism of organic pollutants from molecular level.
