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- 저자 : Shahlaei, Mohsen (검색결과 1 건)
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Safder, Usman,Nam, KiJeon,Kim, Dongwoo,Shahlaei, Mohsen,Yoo, ChangKyoo Elsevier 2018 Ecotoxicology and environmental safety Vol.162 No.-
<P><B>Abstract</B></P> <P>Octanol/water partition coefficient (log P), octanol/air partition coefficient (log K<SUB>OA</SUB>) and bioconcentration factor (log BCF) are important physiochemical properties of organic substances. Quantitative structure-property relationship (QSPR) models are a promising alternative method of reducing and replacing experimental steps in determination of log P, log K<SUB>OA</SUB> and log BCF. In the current study, we propose a new QSPR model based on a deep belief network (DBN) to predict the physicochemical properties of polychlorinated biphenyls (PCBs). The prediction accuracy of the proposed model was compared to the results of previous reported models. The predictive ability of the DBN model, validated with a test set, is clearly superior to the other models. All results showed that the proposed model is robust and satisfactory, and can effectively predict the physiochemical properties of PCBs without highly reliable experimental values.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A QSPR network to improve the prediction of biological activity of PCBs by 3–13%. </LI> <LI> DBN is suggested to give better prediction of log P, log BCF and log K<SUB>OA</SUB>. </LI> <LI> Appropriate for high throughput, unlike previously reported methods. </LI> <LI> The proposed model has better R<SUP>2</SUP> <SUB>pred</SUB> and PRESS compared with three reported models. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>
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