Prediction of polar surface area of drug molecules: A QSPR approach

Abstract

A quantitative structure-property relationship (QSPR) study based on an artificial neural network (ANN) was carried out for the prediction of the microemulsion liquid chromatography polar surface area (PSA) of a set of 32 drug compounds. The genetic algorithm-kernel partial least squares (GA-KPLS) method was used as a variable selection tool. A KPLS method was used to select the best descriptors and the selected descriptors were used as input neurons in neural network model. For choosing the best predictive model from among comparable models, square correlation coefficient Q(2) for the whole set calculated based on leave-group-out predicted values of the training set and model-derived predicted values for the test set compounds is suggested to be a good criterion. Finally, to improve the results, structure-property relationships were followed by nonlinear approach using artificial neural networks and consequently better results were obtained. Also this demonstrates the advantages of ANN.