The artificial neural network-based QSPR and DFT prediction of lipophilicity for thioguanine

Abstract

By the importance of exploring anti-cancer properties of thioguanine (TG), the relationships between quantum chemical indices and lipophilicity of TG tautomers were investigated using the quantitative structure-property relationship (QSPR) approach in two isolated and chitosan-encapsulated states. Accordingly, twenty numbers of different tautomeric forms of TG were selected to predict the logP using the QSPR models. Density functional theory (DFT) calculations along with Dragon package were applied to estimate the required quantum chemical descriptors. The Pearson correlation coefficient statistical test and Kennard-Stone algorithm were used to measure the statistical relationship and data splitting into training and testing set, respectively. Furthermore, the multiple linear regression (MLR) and artificial neural network (ANN) methods were employed for generating the models. In this regard, the analysis of variance (ANOVA) was used to form a basis criterion for testing the significance of MLR and ANN results. Moreover, the leave one out (LOO) method was used for examining the prediction efficiency of select models. The obtained result indicated benefits of proposed models for predicting reliable results of logP.