Application of PC‐ANN to Acidity Constant Prediction of Various Phenols and Benzoic Acids in Water
Aziz Habibi‐Yangjeh, Mahdi Esmailian · Chinese Journal of Chemistry · 2008
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.