In the present work, molecular docking methodology in combination with quantitative structure–activity relationship (QSAR) was employed to predict the inhibition activity of 87 structurally diverse pyrimidine-based derivatives as phosphodiestrae10A (PDE10A) inhibitors due to their potential in the treatment of schizophrenia. In this method, compounds in their preferred enzyme-docked conformations were utilized to derive interaction-based quantitative descriptors in order to explain reported PDE10A inhibitory activities. Multiple linear regression (MLR), artificial neural network (ANN), and least square support vector regression (LS-SVR) were exploited to developing the structure-based quantitative structure–activity relationship models. Among these models, LS-SVR model showed more satisfactory statistical parameters with regard to both internal (Rtrain = 0.951, Q2 = 0.804, RMSEtrain = 0.494) and external validation (Rtest = 0.941, RMSEtest = 0.549) test results. Information from the most relevant descriptors suggests that incorporating steric effect, electronegativity, and the number of substituted aromatic carbon correlate the activity with structural features of the studied compounds. Molecular docking analysis of the most potent inhibitor explored that hydrogen bond formation and hydrophobicity participated in the binding interaction of PDE10A complex active pocket which these findings are in line with those obtained from QSAR model. The reliability assessment of compounds predictions was checked by model applicability domain (AD) analysis.
