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 ℃. 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 pK a . For evaluation of the predictive power of the PCR and PC-ANN models, pK a 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 pK a 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.

Artificial neural networks (ANNs), for a first time, were successfully developed for the prediction partial molar heat capacity of aqueous solutions at infinite dilution for various polar aromatic compounds over wide range of temperatures (303.55-623.20 K) and pressures (0.1-30.2 MPa). Two three-layered feed forward ANNs with back-propagation of error were generated using three (the heat capacity in T = 303.55 K and P = 0.1 MPa, temperature and pressure) and six parameters (four theoretical descriptors, temperature and pressure) as inputs and its output is partial molar heat capacity at infinite dilution. It was found that properly selected and trained neural networks could fairly represent dependence of the heat capacity on the molecular descriptors, temperature and pressure. Mean percentage deviations (MPD) for prediction set by the models are 4.755 and 4.642, respectively.