Linear and nonlinear quantitative structure property relationships modeling of aqueous solubility of phenol derivatives

Soumaya Kherouf, Nabil Bouarra, Amel Bouakkadia, Djelloul Messadi

Abstract


Quantitative structure-solubility relationships (QSSR) considered as a type of

Quantitative structure-property relationships (QSPR) studies, where aqueous solubility of chemicals is related to chemical structure. In the present work multiple linear regression (MLR) and artificial neural network (ANN) techniques were used for quantitative structure-solubility relationship (QSSR) studies of water solubility of 68 phenols (phenol and its derivatives) based on molecular descriptors calculated from the optimized 3D structures. By applying missing value, zero and multicollinearity tests with a cutoff value of 0.95, and genetic algorithm (GA) to select the descriptors that resulted in the best fitted models. After the descriptor selection, multiple linear regression (MLR) was used to construct linear QSSR model. The =91.0%, LOO= 89.33%, s =0.340 values of the model developed by MLR showed a good predictive capability for logS values of phenol and its derivatives. the results of MLR model were compared with those of the ANN model. the comparison showed that the = 94.99%), s = 0.245 of ANN were higher and lower, respectively, which illustrated ANN present an excellent alternative to develop QSSR model for the logS values of phenols than MLR.


Keywords


QSPR; aqueous solubility; phenols; multiple linear regression; artificial neural

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