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- W1434682397 abstract "The n-octanol/water partition coefficient (log P) provides a useful quantitative parameter for representing the lipophilic/hydrophobic nature of organic chemicals. It describes the tendency for a compound to partition preferentially in aqueous or lipophilic media. Under these conditions, it is widely used in many modeling areas such as design of drugs and pesticides. It is also a key parameter in the models designed for estimating the environmental fate of organic chemicals. Additive–constitutive calculation schemes based on molecular structures have been proposed and used for determination of log P values. A simple and powerful log P model for organic chemicals based on regression analysis from the components of the autocorrelation vectors is derived. A user-friendly program AUTOLOGP (version 2.11) has been developed from this regression model. This chapter is aimed at deriving a backpropagation neural network (BNN) model for predicting the log P of chemicals and comparing its performances with those of AUTOLOGP (version 2.11). The AUTOLOGP (version 2.11) compares unfavorably with a three-layer backpropagation neural network (BNN) model using the same inputs, on the basis of the same training set and several heterogeneous testing sets." @default.
- W1434682397 created "2016-06-24" @default.
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- W1434682397 date "1996-01-01" @default.
- W1434682397 modified "2023-10-17" @default.
- W1434682397 title "AUTOLOGP Versus Neural Network Estimation of n-Octanol/Water Partition Coefficients" @default.
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- W1434682397 doi "https://doi.org/10.1016/b978-012213815-7/50003-0" @default.
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