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• W2002117772 abstract "Principal component regression (PCR) and principal component-artificial neural network (PC-ANN) models were applied to prediction of the acidity constant for various benzoic acids and phenols (242 compounds) in water at 25 °C. A large number of theoretical descriptors were calculated for each molecule. The first fifty principal components (PC) were found to explain more than 95% of variances in the original data matrix. From the pool of these PC's, the eigenvalue ranking method was employed to select the best set of PC for PCR and PC-ANN models. The PC-ANN model with architecture 47-20-1 was generated using 47 principal components as inputs and its output is pKa. For evaluation of the predictive power of the PCR and PC-ANN models, pKa values of 37 compounds in the prediction set were calculated. Mean percentage deviation (MPD) for PCR and PC-ANN models are 18.45 and 0.6448, respectively. These improvements are due to the fact that the pKa of the compounds demonstrate non-linear correlations with the principal components. Comparison of the results obtained by the models reveals superiority of the PC-ANN model relative to the PCR model." @default.
• W2002117772 created "2016-06-24" @default.
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• W2002117772 date "2008-05-01" @default.
• W2002117772 modified "2023-09-28" @default.
• W2002117772 title "Application of PC-ANN to Acidity Constant Prediction of Various Phenols and Benzoic Acids in Water" @default.
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• W2002117772 doi "https://doi.org/10.1002/cjoc.200890162" @default.
• W2002117772 hasPublicationYear "2008" @default.
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