A theoretical and Insilco pharmacokinetic studies were carried out on some Phenyl piperidine derivatives using Density Functional Theory (DFT/B3LYP/6-31G*) with Spartan 14 V1.1.4 software to investigate the antipsychotic activity of the compounds. PaDEL-Descriptor software 2.20 version was utilized to generate molecular descriptors while Genetic Function Algorithm (GFA) was used for variable selections to develop Penta-parametric Multi-linear regression models. The statistical parameters of the best model (R2Train= 0.8572, R2adj= 0.8274, R2Test= 0.678, Q2cv (LOO) = 0.7664, Ꭓ2= 0.0036, r2m(LOO)= 0.694, cR2p= 0.763, RMSE= 0.168 and Delta r2m(LOO)= 0.0051) revealed that the model was predictive, robust and possessed good quality. Similarly, the descriptors (AATS8v, GATS1e, SpMAD_Dzs, SP-7 and RDF135v) were found to influence the inhibitory activity of the compounds. Likewise, descriptors SpMAD_Dzs (38.94%) with positive correlation and SP-7 (33.17%) with negative correlation showed predominant influences on the observed activity of the compounds evidenced by their highest percentage contributions. The model proved to be reliable, stable and could be accepted because it satisfied the general requirements for QSAR model development. More so, Insilco Pharmacokinetics and ADMET Risk screenings showed that four compounds (1,2,5 and 6) possessed exceptional good distribution profiles with low ADMET Risk. Consequently, the obtained results are envisaged to provide a rationale blueprint for the structural requirementsfor the development of novel Phenyl piperidine analogues as potent antidepressant agents.

Author Name: Olasupo Sabitu Babatunde Babatunde, Adamu Uzairu, Gideon Adamu Shallangwa, Sani Uba

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Keywords: Serotonin; antipsychotic; QSAR; Descriptors; Model

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EISSN: 2527-1075

EOI/DOI: https://doi.org/10.18540/jcecv

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