Development of novel antipsychotic agents by inhibiting dopamine transporter – in silico approach

Abstract

Dopamine transporter inhibition is deemed a promising approach to treating schizophrenia. This research paper outlines various QSAR models for molecules acting as dopamine transporter inhibitors. SMILES notation and local molecular graph invariants were utilized as descriptors for building QSAR models, with the Monte Carlo optimization method serving as a model developer. GA-MLR was used to obtain a QSAR model from the vast pool of 2D molecular descriptors. To test the quality, robustness, and predictability of the developed models, various statistical methods were employed and the model constructed with Monte Carlo optimization for split 2 is regarded as the best model with numerical values for the correlation coefficient (r²_Train, r²_Test), cross-validated correlation coefficient (q²_Train, q²_Test) and standard error of estimation (s_Train and s_Test) equal to 0.8564, 0.9235, 0.8410, 0.8902, 0.397 and 0.339, respectively. The molecular fragments accounting for the increase/decrease in examined activity were defined and used for the computer-aided design of new compounds. Final assessment of the designed inhibitors was performed using molecular docking studies, highlighting exceptional correlation with the QSAR modeling results. Physicochemical descriptors were computed to predict ADME parameters, pharmacokinetic properties, drug-like nature, and medicinal chemistry friendliness to support drug discovery and according to the obtained results, all the designed molecules possess high drug-likeness.