Prediction of acidity constant for substituted acetic acids in water using artificial neural networks

478-487Linear and non-linear quantitative structure-activity relationships have been successfully developed for the modelling and prediction of acidity constant (pKa) of 87 substituted acetic acids with diverse chemical structures. The descriptors appearing in the multi-parameter linear regression (MLR) model are considered as inputs for developing the back-propagation artificial neural network (BP-ANN). ANN model is constructed using two molecular descriptors; the most positive charge of acidic hydrogen atom (q⁺) and most negative charge of the carboxylic oxygen atom (q⁻) as inputs and its output is pKa. It has been found that properly selected and trained neural network with 53 substituted acetic acids could fairly represent dependence of the acidity constant on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network has been applied for prediction pKa values of 17 compounds in the prediction set. Mean percentage deviation (MPD) for prediction set using the MLR and ANN models are 9.135 and 1.362, respectively. These improvements are due to the fact that the pKa of substituted acetic acids demonstrates non-linear correlations with the molecular descriptors