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- W2809918716 abstract "Octanol/water partition coefficient (log P), octanol/air partition coefficient (log KOA) 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 KOA 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." @default.
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- W2809918716 date "2018-10-01" @default.
- W2809918716 modified "2023-10-03" @default.
- W2809918716 title "Quantitative structure-property relationship (QSPR) models for predicting the physicochemical properties of polychlorinated biphenyls (PCBs) using deep belief network" @default.
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- W2809918716 doi "https://doi.org/10.1016/j.ecoenv.2018.06.061" @default.
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