## 3 Results and Discussion
### ADMET Profiling
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Compound** & \(\mathbf{\Delta E_{vdw}}\) & \(\mathbf{\Delta E_{elec}}\) & \(\mathbf{\Delta G_{pb}}\) & \(\mathbf{\Delta G_{np}}\) & \(\mathbf{\Delta G_{bind}}\) \\ \hline ZINC82121447 & \(-92.04\pm 6.86\) & \(-872.1\pm 1.3\) & \(863.04\pm 8.2\) & \(-12.9\pm 0.2\) & \(-114.03\pm 10.8\) \\ ZINC02170552 & \(-132.7\pm 3.1\) & \(218.7\pm 11.1\) & \(-153.5\pm 16.9\) & \(-15.71\pm 0.1\) & \(-83.2\pm 20.4\) \\ ZINC65274016 & \(-165.36\pm 3.9\) & \(-59.02\pm 5.2\) & \(165.36\pm 0.62\) & \(-16.17\pm 0.08\) & \(-75.24\pm 8.4\) \\ ZINC10763862 & \(-168.37\pm 6.5\) & \(-121.9\pm 5.8\) & \(233.28\pm 4.7\) & \(-16.26\pm 0.3\) & \(-73.35\pm 8.4\) \\ ZINC02412146 & \(-158.42\pm 6.1\) & \(-50.07\pm 3.4\) & \(157.87\pm 6.3\) & \(-17.09\pm 0.2\) & \(-67.72\pm 9.4\) \\
6 MZ & \(-137.79\pm 14.1\) & \(-72.43\pm 35.3\) & \(190.8\pm 43.8\) & \(15.31\pm 1.0\) & \(-34.75\pm 16.4\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: MM/PBSA results for the considered putative hits. Listed energies are in kJ/mol. The uncertainty is measured with a standard deviation. and (3) CNS (logPS) permeability. The fraction of unbound measures the fraction of unbound drugs in the plasma. The ability of a drug to enter the brain, which helps in drug efficacy, is measured using logBB or logPS. A logBB > 0.3 means that it readily crosses the BBB, while a logBB value less than \(-1\) indicates poor distribution in the brain. A logPS value > \(-2\) suggests it can penetrate the CNS, and a logPS value < \(-3\) means that the compound is unable to penetrate. Among the five compounds, only ZINC02412146 shows a higher fraction of unbound values, which suggests that a larger portion of this compound is available for interaction with its target. All five compounds except ZINC65274016 have relatively poor BBB permeability and thus are unlikely to penetrate the BBB. ZINC65274016, ZINC10763862, and ZINC02170552 have the least negative values of CNS permeability, so they might have better potential for exerting effects within the central nervous system. Metabolism governs the biotransformation of chemical compounds within the body, impacting their pharmacological activity and toxicity. Computational tools, such as molecular docking and pharmacophore modeling, facilitate the prediction of ligand metabolism pathways and interactions with CYP2D6 as a substrate or inhibitor. None of the compounds listed are expected to interact significantly with the CYP2D6 enzyme either as substrates or inhibitors. Excretion governs the elimination of chemical compounds and their metabolites from the body, predominantly through renal and hepatic pathways. Computational models, such as clearance prediction algorithms and renal function estimators, aid in assessing ligand excretion kinetics. Among the top five ligands, ZINC65274016 represents relatively high total clearance, indicating faster removal of the compound from the body. Toxicity assessment is crucial for evaluating the safety profile of a drug. Toxic effects can result from the primary action of the drug, its metabolites, or off-target interactions. Three categorical parameters (AMES toxicity, hepatotoxicity, and skin sensitization) were used to evaluate the toxicities of the molecules. The first parameter indicates whether a compound is mutagenic and hence likely to be a carcinogen. Hepatoxicity shows whether a compound is associated with disrupted liver function. The third parameter checks whether a compound is associated with skin sensitization. ZINC02412146 does not exhibit hepatotoxicity, AMES toxicity, or skin sensitization.