Hence, the developed model would be practically useful as a blueprint in developing novel antipsychotic agents with improved activity for the treatment of schizophrenia mental disorder.Ĭhemoinformatic studies were carried on some inhibitors of dopamine transporter to develop a predictive and robust QSAR model and also to elucidate binding mode and molecular interactions between the ligands (inhibitors) and the receptor targeting schizophrenia as novel Antipsychotic agents. Our findings revealed that the developed model passed the general requirements for an acceptable QSAR model and also satisfied the OECD principles for model development. Furthermore, all the selected ligands displayed hydrogen bonds and hydrophobic interactions with the amino acid residues of the target (4M48) which could account for their higher binding energy. More so, the molecular docking investigation showed that the binding affinity of the selected ligands ranges from -10.05 to -9.0 kcal/mol and with ligand 21 possessed the highest binding affinity (-10.05 kcal/mol). Also, the mechanistic interpretation of the descriptors found in the model revealed that two out of five descriptors MATS7s (32.3%) and RDF95m (30.4%) having pronounced influence on the observed antipsychotic property of the compounds evidenced by their highest percentage contributions. The obtained results were compared with standard validation parameters to ascertain the predictivity, reliability, and robustness of the model. The best model with statistically significant parameters has squared correlation coefficient R2= 0.802, adjusted squared correlation coefficient R2 adj = 0.767, Leave one out (LOO) cross-validation coefficient (Q2) = 0.693, lack of fit score (LOF) = 0.406, R2 Test = 0.77, Y-randomization test (cR2p) = 0.714, Chi-squared (χ2) =0.026, bootstrapping (Systematic errors = 0.272) and Variance Inflation Factor (VIF) <2. Density Functional Theory (DFT) approach was utilized to optimize the ligands at B3LYP/6-31G∗ at the ground state and Multi-linear regression of the genetic function approximation (MLR-GFA) method was employed in building Penta-parametric linear equation models. Chemoinformatic studies were carried on some inhibitors of dopamine transporter to develop a predictive and robust QSAR model and also to elucidate binding mode and molecular interactions between the ligands (inhibitors) and the receptor targeting schizophrenia as novel Antipsychotic agents.

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