Computational Design and Evaluation of MAO-B Inhibitors for Parkinson’s Disease: Molecular Docking, Qsar Model Pharmacophore Modeling and Admet Prediction

Computational Design and Evaluation of MAO-B Inhibitors for Parkinson’s Disease: Molecular Docking, Qsar Model Pharmacophore Modeling and Admet Prediction | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Computational Design and Evaluation of MAO-B Inhibitors for Parkinson’s Disease: Molecular Docking, Qsar Model Pharmacophore Modeling and Admet Prediction Aiman Haidri Aiman Haidri, Krishna Kumar Krishna Kumar, Mohit Kumar Mohit Kumar, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8908469/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Parkinson’s disease (PD) remains a pressing neurodegenerative challenge, with monoamine oxidase B (MAO-B) inhibitors offering therapeutic promise by mitigating oxidative stress and dopaminergic neuron loss. This study integrates molecular docking, Quantitative structure-activity relationship (QSAR), Pharmacophore modeling & ADMET to design and evaluate a hybrid Tacrine-Selegiline MAO-B inhibitors for Parkinson disease. A dataset of structurally diverse Hybrid Tacrine-Selegiline MAO-B inhibitors compounds was curated from literature sources and subjected to high-throughput virtual screening via molecular docking against the MAO-B active site (PDB ID: 2V5Z), yielding binding affinities and Key interactions for Novel Hybrid drug. QSAR analysis employed multiple linear regression and algorithms to correlate molecular descriptors with IC50 data, achieving robust predictive performance (R 2 > 0.94982). Complementary ADMET & Pharmacophore modeling identified critical Pharmacophoric features- such as HBA, HBD and AI validated against known inhibitors. Results highlight top-ranking hybrid derivatives of Tacrine-Selegine 6n & 7d compound with enhanced potency, superior binding scores, outperforming reference standard Selegiline which results as Novel drug for Parkinson Disease. MAO-B Inhibitors Parkinson disease Computational Drug Design QSAR Molecular Docking Pharmacophore Modelling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Parkinson’s disease (PD), a chronic progressive neurodegenerative disorder, mainly manifests due to the degeneration of dopaminergic neurons in the substantia nigra pars compacta, ultimately reducing the dopaminergic level in the striatum [ 1 ]. The consequent altered neurochemical homeostasis manifests both motor symptoms like resting tremors, rigidity, bradykinesia, and postural instability and non-motor symptoms like dementia, depression, sleep problems, and autonomic dysfunction [ 2 ]. With the ever-increasing global population and vulnerability to environmental factors, the spread of PD is dramatically escalating; therefore, the demand for effective therapeutic approaches is urgently needed [ 3 ]. In the molecular pathophysiology of PD, the enzyme monoamine oxidase-B (MAO-B) has been found to be of prime importance. It catalyzes the process of dopamine’s oxidative deamination, which results in the formation of hydrogen peroxide along with other ROS, ultimately leading to oxidative stress, mitochondrial damage, and neuronal loss [ 4 ]. Therefore, selective MAO-B inhibition has been identified as an effective strategy not only to delay the synaptic depletion of dopamine but also to reduce subsequent oxidative neuronal damage [ 5 ]. The already existing FDA-approved selective MAO-B inhibitors, like Selegiline, Rasagiline, and Safinamide, is effective for providing symptomatic relief, which has been used as either the primary or the add-on therapy for the treatment of PD along with levodopa [ 6 ]. Even then, the existing drugs are not selective with several side effects that are dependent on the doses as well as differing ability to pass the BBB, which clearly indicates the search for the most optimized molecules with better pharmacological profiles is required [ 7 ]. Computational drug design has grown to be an invaluable aid in the quest to expedite the discovery of central nervous system CNS active compounds [ 8 ]. Computer-aided approaches allow for the precise calculation of compound properties, binding affinity, and biological activity, thus eliminating the need for laboratory synthesis [ 9 ]. The current work relies on a comprehensive and integrated computer-aided approach that involves the use of Molecular Docking, QSAR modeling, Pharmacophore Analysis and ADMET to examine the Tacrine and Selegiline hybrid compounds with the potential of acting as inhibitors of the MAO-B enzyme [ 10 ]. The hybrid compounds are proposed to combine the dopaminergic and neuroprotective attributes of Selegiline with the cholinesterase inhibitory activity of Tacrine for the development of multi-target directed ligands to be utilized against neurodegenerative disorders (Fig. 1 ) [ 11 ]. Through correlating structural descriptors with inhibitory activity, analyzing critical binding interactions in the MAO-B active site, and recognizing important elements in the pharmacophore for inhibiting MAO-B, the research endeavor seeks to investigate the structural elements that define MAO-B inhibition [ 12 ]. In the end, the results may provide mechanistic information for the rational design of next-generation compounds for inhibiting MAO-B with increased selectivity, activity, and drug-like character for the potential therapeutic management of Parkinson's disease [ 13 ]. Levodopa, Pramipexole, Ropinirole, Amantadine & Selegiline MAO-B inhibitors used in Parkinson’s disease. Selegiline, a selective MAO-B inhibitor, is used to treat Parkinson's disease and depression. By increasing dopamine levels in the brain, it enhances mood and motor control. Selegiline can be administered orally or Trans dermally, and it may be prescribed alone or in conjunction with other medications [ 14 ]. METHODOLOGY The study investigates a series of Tacrine-Selegiline hybrid compounds (6a–o and 7a–l) synthesized and characterized as multi-target directed ligands (MTDLs) with dual inhibitory activity against cholinesterase (AChE, BuChE) and monoamine oxidases (MAO-A, MAO-B), with a particular focus on their potential as MAO-B inhibitors [ 15 ]. For computational analysis, compounds with reported IC₅₀ values against MAO-B were selected, including the lead molecule 7d, which demonstrated notable inhibitory potency (IC₅₀ = 4.75 µM) [ 20 ]. Chemical structures were redrawn using ChemDraw based on the synthetic schemes and NMR data provided in the original article, followed by 3D geometry optimization using the MMFF94 force field in Avogadro [ 16 ]. The IC₅₀ values were annotated as biological endpoints for QSAR modeling, Molecular docking simulations were conducted to assess the binding affinity and interaction profiles of the selected hybrids with MAO-B [ 17 ]. The crystal structure of human MAO-B (PDB ID: 2V5Z) was retrieved from the RCSB Protein Data Bank, and prepared by removing water molecules, adding polar hydrogens, and assigning Kollman charges using AutoDock Tools [ 18 ]. Ligands were converted to PDBQT format after energy minimization, and docking was performed using AutoDock Vina with the grid box centered on key active site residues Tyr398, Tyr435, and the FAD cofactor [ 18 ]. Molecular docking Molecular docking is a structure-based computational method used to predict the three-dimensional structure of an active site complex formed between a target protein and a bound ligand [ 19 ]. It predicts binding affinity through a scoring function, with more negative scores reflecting stronger predicted interactions [ 20 ]. Docking helps identify key features that contribute to binding, compare the efficiencies of various ligands, and supports the design of potent MAO-B inhibitors [ 21 ]. The docking scores of different Tacrine-selegine compounds are shown in the form of Table 1 . Table 1 Docking Score of Tacrine-Selegiline hybrid compounds with IC 50 Value S. No Compound Name R Group N IC 50 (µM) a ±SD hMAO -B Docking Score (Kcal/mol) 1. 6a H 2 2.94 ± 0.19 -12.79 2. 6b H 3 2.56 ± 0.56 -11.80 3. 6c H 4 2.73 ± 0.01 -14.18 4. 6d H 5 2.84 ± 0.05 -12.46 5. 6e H 6 2.24 ± 0.07 -15.26 6. 6f H 7 2.47 ± 0.02 -14.25 7. 6g H 8 3.04 ± 0.13 -11.0 8. 6h CH 3 3 2.57 ± 0.03 -12.80 9. 6i CH 3 4 2.43 ± 0.02 -13.02 10. 6j CH 3 5 1.53 ± 0.09 -13.02 11. 6k CH 3 6 1.43 ± 0.03 -8.30 12. 6l CH 3 7 4.63 ± 1.17 -12.62 13. 6m CH 3 8 1.12 ± 0.03 -10.20 14. 6n F 6 65.61 ± 1.12 -14.85 15. 6o F 8 71.39 ± 1.34 -9.01 16. 7a H 3 5.29 ± 0.41 -14.53 17. 7b H 4 4.70 ± 0.02 -14.67 18. 7c H 5 3.14 ± 0.26 -10.00 19. 7d H 6 4.75 ± 0.24 -14.91 20. 7e H 7 5.13 ± 0.06 -10.40 21. 7f H 8 5.78 ± 0.81 -11.40 22. 7g CH 3 3 2.17 ± 0.08 -13.30 23. 7h CH 3 4 3.42 ± 0.04 -13.24 24. 7i CH 3 5 3.77 ± 0.12 -9.05 25. 7j CH 3 6 3.65 ± 0.01 -14.47 26. 7k CH 3 7 4.56 ± 0.87 -11.30 27. 7l CH 3 8 4.21 ± 0.04 -10.01 28. Rasagiline - - 0.23 ± 0.03 -10.34 29. Iproniazid - - 7.54 ± 0.23 -7.42 30. Tacrine–Selegiline hybrid-6 CH 3 - - -12.90 31. Tacrine–Selegiline hybrid-7 CH 3 - - -13.31 The results of how the Tacrine-Selegiline hybrid compounds work with the human monoamine oxidase-B enzyme are shown in Tables 1 and 2 . These tables show things like how the compounds stick to the enzyme and how much energy they need to do that. We also look at which amino acids in the enzyme the compounds interact with and how well they can stop the enzyme from working. The scores we get from docking the compounds into the enzyme tell us how well each one can bind to the oxidase-B enzyme. A negative score means the compound sticks to the enzyme really well and the connection, between them is strong and stable. We use these scores to compare the Selegiline hybrid compounds to other things that are known to inhibit the monoamine oxidase-B enzyme. Table 1 shows the docking scores and how they compare to the IC₅₀ values that were found in experiments for compounds 6a to 6o and 7a to 7l. It also includes the reference standards Rasagiline and Iproniazid well as the internal standards Tacrine–Selegiline hybrid-6 and Tacrine–Selegiline hybrid-7 . The docking scores for compounds 6a to 6o and 7a, to 7l are important to look at. Some special mixes of chemicals did well in tests. They worked better than Rasagiline and Iproniazid. This means they can bind to MAO-B easily. The compounds 6e, 6f, 6n, 7b and 7d were really good. They had scores like − 15.267 kcal/mol, -14.2556 kcal/mol, -14.858 kcal/mol − 14.6758 kcal/mol and − 14.9156 kcal/mol. These were better than the drugs and the basic structures they were based on which are Tacrine–Selegiline hybrid 6 and Tacrine–Selegiline hybrid 7 with scores − 12.9068 kcal/mol and − 13.3117 kcal/mol. The special mixes of chemicals, like compounds 6e, 6f, 6n, 7b and 7d did a job. The docking performance of these derivatives is really good compared to Tacrine–Selegiline hybrid 6 and Tacrine–Selegiline hybrid 7 . This shows that making some changes, to the structure of these derivatives makes them bind to MAO-B better. We also see that when the IC₅₀ values are lower the docking scores are more negative. This means that our docking method is working well and that changing the structure of these derivatives based on what we learn from the docking results is a way to do things in this study. The MAO-B binding of these derivatives is what we are trying to improve. Table 2 gives us a look at how ligands interact with proteins. It lists the amino acid residues that are involved in binding within the MAO-B site. The two standards, Tacrine–Selegiline hybrid 6 and Tacrine–Selegiline hybrid 7 interact with residues like LEU171, CYS172, ILE199 and GLN206. This shows that Tacrine–Selegiline hybrid 6 and Tacrine–Selegiline hybrid 7 are reference compounds because they have interactions with key catalytic residues like LEU171, CYS172, ILE199 and GLN206, in the MAO-B active site. The derivatives that work well like 6e, 6n and 7d seem to interact more with important parts of the molecule including ILE316 and TYR326. These parts are crucial for the derivatives to do their job and stop the enzyme from working. The derivatives 6e, 6n and 7d have consistent interactions, with these critical parts, which is important for them to be effective. The thing that really stands out is how often certain amino acids like LEU171, CYS172, ILE199, GLN206, ILE316 and TYR326 are involved in the compounds that score the highest. We see this in Tacrine–Selegiline hybrid 6 and Tacrine–Selegiline hybrid 7 . This shows just how important these amino acids are when it comes to stopping MAO-B from working. Some compounds are really good at interacting with MAO-B. They have better binding energies than the standard compounds. This means that these compounds fit into the MAO-B binding pocket in a way that's just right, for them, which is a key part of how they work [ 22 ]. Overall, comparison with standards Tacrine–Selegiline hybrid 6 and Tacrine–Selegiline hybrid 7 clearly demonstrates that enhanced MAO-B inhibition is governed not only by binding strength but also by the quality, consistency, and multiplicity of interactions with critical active-site residues. These findings further confirm the success of rational structural optimization in generating potent MAO-B inhibitors with improved inhibitory potential over standard reference compounds. The molecular docking interaction of the Tacrine–Selegiline hybrid compound 6n in the active site of human MAO-B. The figure above highlights the location of the ligand in the binding pocket as well as its interaction with the key amino acid residues involved in the catalytic function of MAO-B. The best docked pose of compound 6n in the MAO-B active site, with a docking score of − 14.858 kcal/mol. The binding conformation and interactions of the compound with key residues such as LEU171, CYS172, ILE199, GLN206, ILE316, and TYR326 reveal high binding affinity and validate the compound as a potential effective MAO-B inhibitor. The best docked pose of the Tacrine–Selegiline hybrid compound 7d in the active site of human MAO-B. The compound has a strong binding affinity with a docking score of − 14.9156 kcal/mol , which reveals a stable binding. The presence of key interactions with the crucial active site residues LEU171, CYS172, ILE199, GLN206, ILE316, and TYR326 ensures its high inhibitory potential against MAO-B. The specific ligand-protein interaction profile of compound 7d within the MAO-B binding pocket. The figure emphasizes the position of the ligand and its interactions with the conserved catalytic residues, ensuring a best-fit scenario and optimal stabilization within the enzyme pocket. These interactions also confirm compound 7d as a potential MAO-B inhibitor. 2.2 QSAR Modeling : 2.2.1 Computational structural predictors : Quantitative structure–activity relationship (QSAR) studies were conducted to identify the correlation between molecular descriptors and the biological activity (pIC50 values) of the 27 Tacrine-Selegiline hybrids obtained from a literature [ 23 ]. All the structures were drawn and saved in a .cdx file format as two-dimensional (2D), utilizing the ChemDraw Ultra 12.0 module of Chem Office software [ 24 ]. Then, these 2D structures were converted to three-dimensional (3D) structures and optimized using the Molecular Mechanics 2 (MM2) force field, which is employed to remove steric clashes and convert them into the lowest-energy stable conformation, utilizing the Chem3D Pro 12.0 module of the same software {CambridgeSoft Corporation ChemDraw Ultra , Version 12.0; CambridgeSoft: Cambridge, MA, 2011}[ 24 ]. These 3D structures were used to calculate structural predictors (also referred to as molecular descriptors (Figure no. 3) using the PaDEL Descriptor Computation software, an open source molecular descriptor calculation free software currently available [ 25 ]. It can compute a total of 1875 descriptors, comprising 1444 one-dimensional (1D) and two-dimensional (2D) descriptors, together with 431 three-dimensional (3D) descriptors, and 12 kinds of fingerprints, amounting to 16,092 bits in total [ 26 ]. The molecular descriptor is the outcome of a logical and mathematical process that converts chemical information, represented symbolically, into a valuable numerical value or the result of a standardized experiment [ 27 ], [ 28 ], [ 29 ], [ 30 ]. Molecular descriptors are essential in chemistry, pharmaceutical sciences, environmental policy, health research, and quality control, as they convert molecules, regarded as tangible entities, into numerical representations, facilitating mathematical analysis of the chemical information inherent in the molecule [ 31 ]. It measures the molecular framework and quantifies several features of molecular structure, including size, shape, symmetry, complexity, branching, cyclicity, and stereo electronic character. Hence playing a pivotal role in QSAR and molecular drug design [ 32 ]. Molecular descriptors used in QSAR modeling 2.2.2 Statistical model generation : A total of 1,875 descriptors were calculated, and before the creation of the QSAR model, the descriptor set was condensed to 207 [ 33 ]. Descriptors exhibiting perfect constancy and strong intercorrelation were eliminated, based on variance and correlation coefficient thresholds of 0.0001 and 0.99, using the V-WSP algorithm [ 34 ], [ 35 ]. Several QSAR models with statistically significant results were created. Two of these models were the most reliable and had the best predictive capacity (Table 3 ) [ 36 ]. These versions were named Model A and Model B. Both models met the specified standards for internal and external validation of QSAR performance [ 37 ]. The abundance of structural predictors significantly surpasses that of compounds, necessitating the identification of critical predictors for QSAR modeling [ 38 ]. The genetic algorithm-multiple linear regression (GA-MLR) has been used to create a QSAR model using a reduced set of predictor variables after variable selection by the genetic algorithm technique [ 39 ]{Broadhurst D., et al . “Genetic algorithms as a method for variable selection in multiple linear regression and partial least squares regression with applications to pyrolysis mass spectrometry” [ 40 ]. The fitness function is computed using the default settings specified in the NanoBridges program. Total iterations = 100, equation length = 5, crossover probability = 1, mutation probability = 0.5, initial equations created = 100, best equations picked = 20, smoothing parameters (LOF computations) = 10 [ 41 ]. A population of 100 distinct random combinations of the computed molecular descriptors is formed [ 40 ]. A QSAR model is constructed using each parent combination of descriptors for the whole dataset via multiple linear regression (MLR) [De Campos LJ and De Melo EB. “Modeling structure-activity relationships of prodiginines with antimalarial activity using GA/MLR and OPS/PLS [ 42 ], [ 43 ]. Table 3 QSAR Model of Hybrid MAO-B inhibitors. QSAR MODEL A MLR Eq. 9 Pic50 = -41.89909(+/-10.5767) + 8.87531(+/-1.48459) SaaN + 0.21797(+/-0.66238) AATS8s -0.00141(+/-0.00016) ATSC5m + 0.17343(+/-0.89449) AATS8e Test Molecules: 06 (Compound No. are as 1, 4, 12, 14, 22, 19) = 22.22% Training Molecules: 21 (Compound No. are as 2, 3, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 17, 18, 20, 21, 23, 24, 25, 26, 27) = 77.77% QSAR MODEL B MLR Equation: Pic50 = -37.6699(+/-3.49996) -0.00001(+/-0.00001) ATS7i + 8.46927(+/-0.70642) SaaN − 0.00005(+/-0.00005) VR1_Dzp − 0.00124(+/-0.00023) ATSC5m Total Test Compounds: 07 (Compound No. are as 5, 8, 10, 12, 14, 21, 23) = 25.92% Total Training Compounds: 20 (Compound No. are as 1, 2, 3, 4, 6, 7, 9, 11, 13, 15, 16, 17, 18, 19, 20, 22, 24, 27, 25, 26) = 77.77% QSAR MODEL C MLR Equation : Pic50 = -38.7502(+/-3.56455) +0.43886(+/-2.45053) GGI10 + 6.16573(+/-1.24601) GATS5m + 0.38609(+/-0.47574) GATS7i + 7.3407(+/-0.74816) SaaN Total Test Compounds : 07 (Compound No. are as 5, 8, 10, 12, 14, 21, 23) = 25.92% Total Training Compounds : 20 (Compound No. are as 1, 2, 3, 4, 6, 7, 9, 11, 13, 15, 16, 17, 18, 19, 20, 22, 24, 27, 25, 26) = 77.77% Statistical parameter Threshold Model A Model B Model C Fitting-criteria Internal-validation R² > 0.6 0.94982 0.92161 0.92099 SEE 0.6 0.93728 0.90071 0.89993 F-value > theoretical 75.71 44.09 43.71 Q²LOO > 0.5 0.92424 0.86788 0.86092 rm²LOO > 0.5 0.89392 0.79574 0.80532 Δrm² 0.6 0.85041 0.92193 0.91823 rm²(test) > 0.5 0.77765 0.87085 0.85171 Δrm² (test) 0.5 0.8386 0.89797 0.89396 Q²F1 > 0.7 0.8386 0.89797 0.89396 Q²F2 > 0.7 0.81461 0.89222 0.88798 Three QSAR models (Model A, Model B, and Model C) were assessed utilizing standard fitting, internal validation, and external validation criteria. All models met the requisite criteria (R² > 0.6, SEE 0.5, r²(ext) > 0.6), hence validating the statistical acceptability of each model. Model A exhibited superior internal performance within the group, evidenced by the greatest R² (0.9498), the lowest SEE (0.0889), the highest Q²LOO (0.9242), and the most favorable Δrm² (0.007). This indicates that it was highly resilient and did not overfit. Model B had superior external predictivity, evidenced by the highest r²(ext) (0.9219), rm²(test) (0.87085), and rpred² (0.8979) (Table no: 3). This indicates it was more effective in forecasting previously unobserved molecules. Model C performed adequately; however, it was slightly inferior to Models A and B in both internal and external evaluations. Descriptor significance analysis indicated that SaaN, GATS5m, and ATSC5m were the most dependable predictors of activity. The sum of nitrogen E-states (SaaN) exerted a significant and favorable influence on MAO-B inhibitory activity. This indicates that electronically active nitrogen environments are significant. “GATS5m” (Geary autocorrelation weighted by mass at lag 5) yielded a strong positive coefficient, underscoring the importance of appropriate mass distribution and spatial correlations. “ATSC5m” exerted a considerable adverse impact, indicating that excessive mass autocorrelation at lag 5 impedes activity (Table no: 4), these descriptors collectively clarify the structural features that govern enhanced MAO-B inhibition relevant to antiparkinsonian efficacy. 2.3 Pharmacophore Modelling : The pharmacophore models the 3D characteristics in the molecules that will define the biological nature of this molecule [ 44 ]. These include hydrogen bond donor or acceptors, hydrophobic characteristics, aromatic rings or charged groups [ 45 ]. The pharmacophore can be used during drug discovery as a means to understand the ligand’s interaction within the target protein and to develop and design an effective ligand based upon that understanding [ 45 ]. There are currently programs such as LigandScout that automatically take protein-ligand complex data as input and generate three-dimensional pharmacophore models based on experimental information (e.g., X-ray crystal structure or docking analysis) [ 46 ]. The program creates pharmacophore models from ligand-protein interaction data, thus providing a convenient graphical interface for visualizing the three-dimensional pharmacophore and further analysis through hypothesis generation and library screening of compounds for leads [ 47 ]. 2.4 Toxicity and Safety Considerations Toxicity and safety evaluation are very important in the development of novel MAO-B inhibitors, especially for compounds that would be administered over a long period of time in chronic neurological diseases like Parkinson’s disease [ 48 ]. Theoretically, multi-target Tacrine–based scaffolds display relatively promising pharmacological actions; however, the parent drug Tacrine is historically associated with clinically significant hepatotoxicity, cholinergic adverse effects, and gastrointestinal intolerance [ 49 ]. Thus, hybrid molecules incorporating Tacrine motifs have to be painstakingly optimized to minimize off-target interactions, reduce metabolic activation to reactive intermediates, and enhance brain selectivity while sustaining therapeutic potency [ 50 ] In silico prediction tools give important preliminary information on safety liabilities that can be expected well before actual experimental testing is performed [ 51 ]. Absorption, distribution, metabolism, excretion, and toxicity parameters can be predicted to forecast blood-brain barrier permeability, cytochrome P450 inhibition, mutagenic or carcinogenic risk, hepatotoxic potential, among others [ 52 ]. Molecules violating more than one drug-likeness rule or showing high predicted toxicity indices may be deprioritized at the initial stage [ 53 ]. Besides, selectivity to MAO-B over MAO-A is a very important determinant of safety. Poor selectivity may raise the risk of hypertensive crises owing to dietary tyramine interactions [ 54 ]. Ideally, rationally designed MAO-B inhibitors should exhibit high central nervous system permeability, favorable therapeutic index, absence of strong CYP inhibition, low hepatotoxicity, and negligible cardiotoxic or genotoxic signals [ 54 ]. These considerations emphasize the need to integrate toxicity-aware design with computational optimization and subsequent in vitro validation [ 55 ] Table 4 Molecular descriptor and Physical interpretation S.N. Significant Descriptors Regression coefficient Contribution effect Physical interpretation 1 SaaN + 8.87531, + 8.46927, + 7.34070 Strong positive contribution Atom-type E-state indices : Sum of aaN E-states 2 ATSC5m –0.00141, –0.00124 Strong negative contribution Centered Broto-Moreau autocorrelation of lag 5 weighted by mass 3 GATS5m + 6.16573 Strong positive contribution Geary autocorrelation of lag 5 weighted by mass QSAR modeling yielded three statistically robust and predictive models. Model A had superior internal robustness, whereas Model B demonstrated optimal external predictivity. Descriptor analysis confirmed that SaaN, GATS5m, and ATSC5m are key structural drivers of MAO-B inhibitory activity, emphasizing the importance of nitrogen electronic states and uniform topological mass distribution [ 56 ]. The validated statistical parameters and descriptor significance indicate that these models are effective instruments for the design and enhancement of novel MAO-B inhibitors for the treatment of Parkinson's disease [ 57 ]. Ligand-based pharmacophore models were generated using LigandScout 4.4. Active compounds were aligned to identify common features: Aromatization (AI), Hydrogen bond acceptors (HBA), Hydrogen bond Donor (HBD), hydrophobic centers. Fit scores were calculated and validated against known MAO-B inhibitors. This reflects the drug–receptor interactions [ 58 ]. Table 5 Pharmacophoric features of Standard Hybrid Tacrine-Selegiline-6 & 7 S. No Compound Name Chemical Formula Molecular Weight (gm/mol) Docking score (kcal/mol) Pharmacophoric features HBA HBD NI PI 1. Hybrid Tacrine-Selegiline- 6 C 27 H 31 N 3 O 1 413.565 -12.9068 2 1 0 1 2. 6n C 30 H 36 N 3 O 1 F 1 473.636 -14.858 3 1 0 1 1. 3. Hybrid Tacrine-Selegiline- 7 C 28 H 33 N 3 O 1 427.592 -13.3117 2 1 0 1 4. 7d C 29 H 37 N 5 O 1 471.649 -14.9156 1 4 0 0 The things that make a drug work called features and other properties of some special compounds that combine Tacrine and Selegiline were looked at and compared to the standard compounds we know which all is shown in Table 5 . We looked at things, like what the compound's made of, how heavy it is how well it fits into a special spot and what makes it special like parts that can form hydrogen bonds, which are called hydrogen bond acceptors and hydrogen bond donors and other parts like non-ionizable groups and π-interaction sites. Compound 6-std is a chemical with a weight of 413.565 grams per mole. It has a code that is C₂₇H₃₁N₃O₁. When we did some tests, on Compound 6-STD it showed a docking score of − 12.9068 kcal/mol. We also looked at what makes Compound 6-std work. Found that it has some important features. Compound 6-std has two places where it can accept hydrogen bonds, one place where it can give a hydrogen bond and one place where it can have a kind of interaction called a π-interaction. Compound 6n is really interesting. It has a weight of 473.636 g/mol and its formula is C₃₀H₃₆N₃O₁F₁. When we looked at Compound 6n we found that it had a better docking score, which was − 14.858 kcal/mol. This is better, than what we saw with 6-std. Compound 6n has some features, like three places where it can accept hydrogen bonds, one place where it can give a hydrogen bond and one place where it can have a π-interaction. This makes Compound 6n really unique. Compound 7-std is a thing with a weight of 427.592 grams per mole. It has a code of C₂₈H₃₃N₃O₁. When we tested Compound 7-std it had a docking score of -13.3117 kilocalories per mole. This Compound 7-std has some features that are important for its job. It has two parts that can take hydrogen bonds one part that can give a hydrogen bond and one part that can do a π-interaction. Compound 7-std is really good, at doing its thing because of these features. Compound 7d is an interesting thing. It has a weight of 471.649 grams per mole. The formula for Compound 7d is C₂₉H₃₇N₅O₁. When we looked at how it fits with other things Compound 7d had a really good score of − 14.9156 kilocalories per mole. This was the score out of all the compounds we picked. We also did something called analysis on Compound 7d. This analysis showed that Compound 7d has one place where it can accept hydrogen and four places where it can give hydrogen. We did not see any interactions with pi electrons, in Compound 7d. Overall, compounds 6n and 7d demonstrated more favorable docking scores than their corresponding standards, 6-std and 7std, respectively. Differences in hydrogen bonding capacity and Pharmacophoric features were observed among the compounds, corresponding to variations in predicted binding affinity toward the MAO-B active site. Comparison with Standard Drugs To better understand the performance of the synthesized Tacrine–Selegiline derivatives, their docking, physicochemical, and pharmacological profiles can be compared against established and successful MAO-B inhibitors, Rasagiline, and Selegiline [ 59 ]. In this current work, several derivatives have a docking score equal to or better than Rasagiline, which indicates a profound interaction between these derivatives and Tyr398, Tyr435, and the FAD cofactor site of the MAO-B enzyme [ 60 ]. Unlike single-target approved drugs, hybrid molecules possess dual or multi-target properties to address more than one pathological pathway involved in dopaminergic breakdown and cholinergic system impairment [ 61 ]. Yet, traditional drugs have already established clinical safety, pharmacokinetic profiles, and a history of successful treatment for many decades. A newly developed molecule must establish its advantages in more than one aspect: binding affinity and selectivity/solubility and safety profiles. Finally, the computational advantages observed for hybrid compounds underscore the great promise of these entities for next-generation agents [ 62 ]. However, rigorous in vitro studies for enzyme activity, cell toxicity assays, and pharmacology tests on animals should be conducted to allow proper comparisons to current therapeutic standards. The values and explanation of 6 standard compound: The compound violated the Pfizer and GSK rules (score = 1.0 for both). Golden Triangle rule (score = 0.00). The absence of PAINS alerts (0 alerts) confirms that the compound does not contain substructures associated with false-positive biological activity [ 66 ]. Absorption analysis revealed good intestinal permeability, as evidenced by the predicted Caco-2 permeability value (− 4.956) and moderate MDCK permeability (− 4.601). The compound was neither a P-glycoprotein inhibitor (0.0) nor a significant substrate (0.004), indicating minimal efflux liability, and it demonstrated good human intestinal absorption (> 30%) [ 67 ]. Distribution studies showed very high plasma protein binding (98.27%), which may limit the free circulating drug fraction, while the predicted volume of distribution (VDss = 0.312) suggests moderate tissue distribution [ 68 ]. Additionally, the high blood–brain barrier penetration probability (BBB = 0.912) indicates the compound’s ability to readily access the central nervous system. Metabolic profiling indicated strong interactions with cytochrome P450 enzymes, with the compound acting as a potent inhibitor of CYP1A2 (0.998), CYP2D6 (0.997), and CYP3A4 (0.923), while also serving as a substrate for CYP1A2 (1.0), CYP2C19 (0.996), and CYP3A4 (1.0), suggesting a high potential for metabolic instability and drug–drug interactions; mild to moderate interactions were also predicted for CYP2B6 and CYP2C8 [ 69 ]. The predicted elimination half-life was very short (T½ = 0.315 h), indicating rapid systemic clearance. Toxicity predictions revealed significant safety concerns, including a high probability of hERG channel blockade (0.82 and 0.808), suggesting potential cardiotoxicity, along with elevated risks of respiratory toxicity (0.997), hepatotoxicity (0.817), nephrotoxicity (0.983), neurotoxicity, ototoxicity (0.869), and genotoxicity (0.783). Moderate risks were observed for AMES mutagenicity (0.768), carcinogenicity (0.644), skin sensitization (0.749), hematotoxicity (0.602), and rat oral acute toxicity (0.402). In contrast, eye corrosion (0.0) and eye irritation (0.006) risks were negligible, while immunotoxicity in RPMI-8226 cells (0.251) and cytotoxicity toward A549 cells (0.224) were low; however, elevated cytotoxicity was predicted in HEK-293 cells (0.77), indicating potential renal cellular toxicity. The values and explanation of 6n compound: The compound failed both the Pfizer and GSK rules (score 1.0 for each), so there’s some real concern about high lipophilicity and molecular size—both red flags for tricky ADMET properties. The Golden Triangle rule didn’t get met either (score 0.0). On the bright side, no PAINS alerts popped up, so there aren’t any obvious substructures that usually mess with assays or cause false positives. The compound shows good intestinal permeability—Caco-2 value at − 5.032 backs that up—and moderate MDCK permeability (− 4.579). PAMPA permeability checks out too (0.843), pointing to solid passive diffusion. It’s not predicted to be a P-glycoprotein inhibitor (0.006) or much of a substrate (0.004), so efflux shouldn’t be a big issue. Human intestinal absorption seems fine (HIA probability 0.003, so HIA ≥ 30%). But when it comes to bioavailability, it’s probably on the lower side—there’s a strong chance it’s under 50% (F50% = 0.883). For distribution, the compound practically glues itself to plasma proteins (PPB = 98.43%). That means not much of it is floating around in a free, active form. Volume of distribution is moderate (VDss = 0.244 L/kg), so it spreads out a bit but not wildly. It’s got a decent shot at crossing the blood–brain barrier (BBB = 0.746), so it could reach the central nervous system. As for hepatic transporters, it’s predicted to block OATP1B3 (0.783), but has little effect on OATP1B1, BCRP, and MRP1.Metabolism-wise, the compound really goes after cytochrome P450 enzymes. It’s a strong inhibitor of CYP1A2 (0.945) and CYP2D6 (0.879), but only weakly inhibits CYP2C19, CYP2C9, CYP3A4, CYP2B6, and CYP2C8. At the same time, it acts as a substrate for several CYP isoforms—CYP1A2 (1.0), CYP2C19 (0.998), and CYP3A4 (0.999)—which means it’s likely to be metabolized pretty thoroughly and could cause drug–drug interactions. Human liver microsomal instability is high (0.899), so it probably won’t last long in the liver. When it comes to excretion, plasma clearance is moderate (CL = 6.013 mL/min/kg), but the half-life is extremely short (T½ = 0.328 h). So, this compound doesn’t stick around—it’s eliminated fast. The toxicity profile, though, is concerning. There’s a high chance it blocks hERG channels (0.874 and 0.781 at 10 µM), which raises red flags for cardiotoxicity. Risks for respiratory toxicity (0.998), nephrotoxicity (0.996), neurotoxicity (0.993), ototoxicity (0.931), skin sensitization (0.927), and human hepatotoxicity (0.822) are all high. Moderate risks show up for AMES mutagenicity (0.653), carcinogenicity (0.579), hematotoxicity (0.591), and rat oral acute toxicity (0.37). On the other hand, eye corrosion and irritation risks are close to zero. As for cytotoxicity, there’s not much to worry about with A549 (0.078) and RPMI-8226 cells (0.245), but HEK-293 cells show higher sensitivity (0.717), hinting at possible kidney cell toxicity. The values and explanation of 7 standard compound: This compound breaks both the Pfizer and GSK rules (scoring a full 1.0 on each), which points to possible trouble with ADMET, especially because of its high lipophilicity. It also falls short on the Golden Triangle rule (0.00 score). On the bright side, there are no PAINS alerts, so the molecule doesn’t have those usual substructures that mess with assays or cause fake biological hits. The predicted Caco-2 permeability value (− 5.032) sits comfortably in the acceptable range, and the MDCK permeability (− 4.579) shows the compound should move across membranes without much trouble. It barely interacts with P-glycoprotein, either as an inhibitor (0.006) or a substrate (0.004), so you don’t have to worry about much efflux. Human intestinal absorption checks out too (HIA probability = 0.003, meaning absorption should be at least 30%).Distribution throws up some issues, though. The compound binds very tightly to plasma proteins (PPB = 98.43%), which means less of the drug stays free in the bloodstream. The predicted volume of distribution (VDss = 0.244 L/kg) suggests it reaches tissues moderately well. It does have a strong chance of crossing into the brain (BBB = 0.746), which means it can likely get into the central nervous system. On the metabolism front, things get complicated. The compound is predicted to strongly inhibit CYP1A2 (0.945) and CYP2D6 (0.879), and it moderately inhibits CYP3A4 (0.395). It’s also a substrate for CYP1A2 (1.0), CYP2C19 (0.998), and CYP3A4 (0.999), so it’s likely to get metabolized quickly and could interact with a lot of other drugs. It doesn’t do much with CYP2B6 or CYP2C8. Plus, it looks like it’s unstable in human liver microsomes (HLM instability = 0.899), so it probably won’t stick around long in the body. Elimination is fast—really fast. The predicted half-life is just 0.328 hours, so it gets cleared from the system almost right away. This makes it an ultra-short half-life drug. Toxicity is where most of the red flags pop up. There’s a high risk of blocking the hERG channel (0.874 and 0.781), which raises concerns about possible cardiotoxicity. The software also predicts high risks for respiratory toxicity (0.998), liver damage (0.822), kidney toxicity (0.996), neurotoxicity (0.993), and ototoxicity (0.931). Risks for AMES mutagenicity (0.653), carcinogenicity (0.579), skin sensitization (0.927), hematotoxicity (0.591), and rat oral acute toxicity (0.37) sit in the moderate range. Eye corrosion (0.0) and irritation (0.003) aren’t an issue at all. Cytotoxicity is low in RPMI-8226 (0.245) and A549 cells (0.078), but it jumps up in HEK-293 cells (0.717), hinting at some kidney cell toxicity. The values and explanation of 7d compound: The compound broke both the Pfizer and GSK rules (score 1.0 for each) and didn’t meet the Golden Triangle rule at all (score 0.00). On the bright side, it didn’t trigger any PAINS alerts, so there aren’t any red flags for false-positive bioactivity. The compound seems to get through the gut wall pretty well, backed up by a solid Caco-2 value (− 4.974) and moderate MDCK permeability (− 4.57). It doesn’t act as a P-glycoprotein inhibitor or substrate (both 0.0), so it’s not likely to get pumped out of cells, and it shows good human intestinal absorption—over 30%.For distribution, it binds strongly to plasma proteins (97.69%), which means less free drug in the bloodstream. The predicted volume of distribution (VDss = 0.439) points to moderate tissue spread. Interestingly, it’s very likely to cross the blood–brain barrier (BBB = 1.0), so it gets into the central nervous system with ease. The compound interacts with a bunch of cytochrome P450 enzymes. It’s a strong inhibitor of CYP1A2, CYP2C19, CYP2C9, CYP2D6, CYP3A4, CYP2B6, and CYP2C8, and it’s also a substrate for CYP1A2, CYP2C19, and CYP3A4. That’s a recipe for metabolic instability and all sorts of drug–drug interactions. Elimination happens fast here, with a really short predicted half-life (T½ = 0.295 hours). The body clears it out quickly. The compound scores high for hERG channel blockade (0.97 and 0.881), which points to possible heart risks. There’s also a big chance of respiratory toxicity (0.998), liver toxicity (0.881), kidney toxicity (0.999), nervous system toxicity (0.997), ear toxicity (0.923), and genotoxicity (0.894). Risks for AMES mutagenicity (0.615), cancer (0.558), skin sensitization (0.816), blood toxicity (0.603), and acute oral toxicity in rats (0.741) are moderate. On the plus side, eye corrosion (0.0) and irritation (0.001) look like non-issues, and immunotoxicity in RPMI-8226 cells (0.366) and cytotoxicity toward A549 cells (0.261) are low. But it does show high cytotoxicity in HEK-293 cells (0.935), which could mean real trouble for kidney cells. Conclusion The computational research conclude that the Hybrid Tacrine-Selegine Compound-6n and 7d show better interaction and robust predictive performance (R 2 > 0.94982) for MAO-B inhibition. The In-silico ADMET assessment showed positive results for pharmacokinetic and safety attributes which confirmed that the proposed candidates for Parkinson disease. The Pharmacophore model showed the main structural elements similarity with standard that enabled In-silico promised novel compound MAO-B inhibition. LIST OF ABBREVIATION PD Parkinson’s disease MAO-B Monoamine Oxidase B ROS Reactive Oxygen Species FDA Food and Drug Administration BBB Blood-Brain Barrier CNS Central Nervous System QSAR Quantitative Structural-Activity Relationship ADMET Absorption, Distribution, Metabolism, Excretion and Toxicity MTDLs Multi-target directed ligand AchE Acetylcholinesterase BuChE Butyrylcholinesterase MAO-A Monoamine Oxidase A NMR Nuclear Magnetic Resonance MMFF94 Merck Molecular Force Field 94 RCSB Research Collaboratory for Structural Bioinformatics PDBQT Protein Data Bank, Partial Charge (Q), & Atom Type (T) FAD Flavin Adenine Dinucleotide IC50 Half-Maximal Inhibitory Concentration PIC50 Negative Logarithm of the Half-Maximal Inhibitory Concentration MM2 Molecular Mechanics 2 PaDEL Pharma Data Exploration Laboratory GA-MLR Genetics algorithm- multiple linear regression LOF Local Outlier Factor MLR Multiple linear regression GA Genetic Algorithm OPS Operation and Procedure Classification System PLS Primary Lateral Sclerosis CYP Cytochrome P450 AI Aromatization HBA Hydrogen bond acceptors HBD Hydrogen bond donor Declarations Acknowledgment All authors conveyed special thanks to Mr. Jitender Joshi (President), and Prof. (Dr.) Dharam Buddhi (Vice Chancellor) of Uttaranchal University for their research-associated encouragement. 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Pharm Res 34(3):544–551. 10.1007/s11095-016-2086-y Iacopetta D et al (2023) Impact of Cytochrome P450 Enzymes on the Phase I Metabolism of Drugs, Applied Sciences Vol. 13, vol. 13, no. 10, May 2023. 10.3390/app13106045 Table 2 Table 2 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Supplementryfile.zip Table2Ligand.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8908469","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600548920,"identity":"de219355-8212-4329-bb77-2ba659ed7275","order_by":0,"name":"Aiman Haidri Aiman Haidri","email":"","orcid":"","institution":"Uttaranchal University","correspondingAuthor":false,"prefix":"","firstName":"Aiman","middleName":"Haidri Aiman","lastName":"Haidri","suffix":""},{"id":600548921,"identity":"217be23d-361a-4876-a99c-281c6dc3af7a","order_by":1,"name":"Krishna Kumar Krishna Kumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDElEQVRIie3PsUrDQBjA8TsOdPlS16+k+AwtgXM50gdxSTiIiyLOFjwJnEseoOIzuIrjwUFcolkLXQy+QKCrSK/FTZLWTfD+S47w/fjuCPH5/mBIqGLuOybAjGlBuDO9NXuSg7SZj7INUTsIId8EogiE3f7tJcMHm6+unuPopDAc4byOH++s2zITp10kHKQ6vK8kH72qDLFayqcqdaTMLlQHOQaqWaCZwCNS4qRYSm4cocr2kXwV6BtHqMb0603yuuknIVAVBtpyDHI2NmBivtixZVhQ7chLhFDSRoFM+MJtSXregtXhh7vY9WQOl639hHjK67PmvZ2JTvKjdDuZ7Du+afqbYZ/P5/sfrQHGLF5caHwwjgAAAABJRU5ErkJggg==","orcid":"","institution":"Uttaranchal University","correspondingAuthor":true,"prefix":"","firstName":"Krishna","middleName":"Kumar Krishna","lastName":"Kumar","suffix":""},{"id":600548922,"identity":"9b4b68a8-c3be-43f1-81dd-c6687e51f7b6","order_by":2,"name":"Mohit Kumar Mohit Kumar","email":"","orcid":"","institution":"Vivek University","correspondingAuthor":false,"prefix":"","firstName":"Mohit","middleName":"Kumar Mohit","lastName":"Kumar","suffix":""},{"id":600548923,"identity":"64517352-a354-4ccc-923d-afb1f1f4dba5","order_by":3,"name":"Ruchi Kant Ruchi Kant","email":"","orcid":"","institution":"Uttaranchal University","correspondingAuthor":false,"prefix":"","firstName":"Ruchi","middleName":"Kant Ruchi","lastName":"Kant","suffix":""},{"id":600548924,"identity":"f5ecc263-c7c5-43ec-9b96-9368d06edbb0","order_by":4,"name":"Aman Kumar","email":"","orcid":"","institution":"Uttaranchal Institute of Pharmaceutical Sciences, Uttaranchal University","correspondingAuthor":false,"prefix":"","firstName":"Aman","middleName":"","lastName":"Kumar","suffix":""},{"id":600548925,"identity":"8c683055-2ca8-4897-a526-7727381b9d03","order_by":5,"name":"Anshi Anshi","email":"","orcid":"","institution":"Uttaranchal Institute of Pharmaceutical Sciences, Uttaranchal University","correspondingAuthor":false,"prefix":"","firstName":"Anshi","middleName":"","lastName":"Anshi","suffix":""}],"badges":[],"createdAt":"2026-02-18 10:53:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8908469/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8908469/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103976653,"identity":"ddcace53-8ad1-44be-9e97-6ce3cf03f4e5","added_by":"auto","created_at":"2026-03-05 08:42:40","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":52494,"visible":true,"origin":"","legend":"\u003cp\u003eTacrine and Selegiline hybrid compounds\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8908469/v1/168ca81ee3c652f48c0b2e15.jpg"},{"id":103976600,"identity":"119a248e-ccaa-410f-bc67-80ea97a4e74f","added_by":"auto","created_at":"2026-03-05 08:42:26","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":115348,"visible":true,"origin":"","legend":"\u003cp\u003eBest Pose Tacrine–Selegiline hybrid 6 and 6n\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8908469/v1/618a785e18345d08dea92b7b.jpg"},{"id":103976655,"identity":"310a7861-7325-434d-9632-29298ab5ddb2","added_by":"auto","created_at":"2026-03-05 08:42:41","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":118615,"visible":true,"origin":"","legend":"\u003cp\u003eBest dock pose of\u003cstrong\u003e \u003c/strong\u003eTacrine–Selegiline hybrid 7 and 7d\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8908469/v1/d95fe957eb2bd5f131dbdd47.jpg"},{"id":104402221,"identity":"3f8802d6-df0a-4832-8841-cbfc97da54b0","added_by":"auto","created_at":"2026-03-11 12:14:43","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":59628,"visible":true,"origin":"","legend":"\u003cp\u003ePharmacophore 2D structure Tacrine-Selegiline hybrid 6 and 6n\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8908469/v1/f55ca61d689d391b2762db6b.jpg"},{"id":103976588,"identity":"b64c348a-8d74-4c50-a50f-4451800c10a9","added_by":"auto","created_at":"2026-03-05 08:42:15","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":60992,"visible":true,"origin":"","legend":"\u003cp\u003ePharmacophore 2D structure of Tacrine-selegiline hybrid 7 and 7d\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8908469/v1/d7f711c33c4b8b117f408796.jpg"},{"id":103976602,"identity":"61abe207-6877-48cc-bc1a-be1c0dffa7d2","added_by":"auto","created_at":"2026-03-05 08:42:26","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":149598,"visible":true,"origin":"","legend":"\u003cp\u003eADMET of 6a-o, 7a-l and its best docked compound\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8908469/v1/c1a2e9f845b77aa388fcc627.jpg"},{"id":105688707,"identity":"020b653a-b24c-4a9c-8a0a-98203e4f02a8","added_by":"auto","created_at":"2026-03-30 01:10:20","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2711038,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8908469/v1/9fa8eaa0-1849-461d-9225-66d57a0876c4.pdf"},{"id":103976599,"identity":"3761cddd-98c5-472d-ab06-d807583516c8","added_by":"auto","created_at":"2026-03-05 08:42:26","extension":"zip","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":7519028,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementryfile.zip","url":"https://assets-eu.researchsquare.com/files/rs-8908469/v1/b498a8ea400cdcf2bd6bc936.zip"},{"id":103976608,"identity":"5838d4e8-0436-439c-be79-9c239ae8d4d6","added_by":"auto","created_at":"2026-03-05 08:42:29","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":298092,"visible":true,"origin":"","legend":"","description":"","filename":"Table2Ligand.docx","url":"https://assets-eu.researchsquare.com/files/rs-8908469/v1/244c46514a235f5771e97014.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eComputational Design and Evaluation of MAO-B Inhibitors for Parkinson’s Disease: Molecular Docking, Qsar Model Pharmacophore Modeling and Admet Prediction\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD), a chronic progressive neurodegenerative disorder, mainly manifests due to the degeneration of dopaminergic neurons in the substantia nigra pars compacta, ultimately reducing the dopaminergic level in the striatum [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The consequent altered neurochemical homeostasis manifests both motor symptoms like resting tremors, rigidity, bradykinesia, and postural instability and non-motor symptoms like dementia, depression, sleep problems, and autonomic dysfunction [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. With the ever-increasing global population and vulnerability to environmental factors, the spread of PD is dramatically escalating; therefore, the demand for effective therapeutic approaches is urgently needed [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In the molecular pathophysiology of PD, the enzyme monoamine oxidase-B (MAO-B) has been found to be of prime importance. It catalyzes the process of dopamine\u0026rsquo;s oxidative deamination, which results in the formation of hydrogen peroxide along with other ROS, ultimately leading to oxidative stress, mitochondrial damage, and neuronal loss [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Therefore, selective MAO-B inhibition has been identified as an effective strategy not only to delay the synaptic depletion of dopamine but also to reduce subsequent oxidative neuronal damage [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The already existing FDA-approved selective MAO-B inhibitors, like Selegiline, Rasagiline, and Safinamide, is effective for providing symptomatic relief, which has been used as either the primary or the add-on therapy for the treatment of PD along with levodopa [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Even then, the existing drugs are not selective with several side effects that are dependent on the doses as well as differing ability to pass the BBB, which clearly indicates the search for the most optimized molecules with better pharmacological profiles is required [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eComputational drug design has grown to be an invaluable aid in the quest to expedite the discovery of central nervous system CNS active compounds [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Computer-aided approaches allow for the precise calculation of compound properties, binding affinity, and biological activity, thus eliminating the need for laboratory synthesis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The current work relies on a comprehensive and integrated computer-aided approach that involves the use of Molecular Docking, QSAR modeling, Pharmacophore Analysis and ADMET to examine the Tacrine and Selegiline hybrid compounds with the potential of acting as inhibitors of the MAO-B enzyme [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The hybrid compounds are proposed to combine the dopaminergic and neuroprotective attributes of Selegiline with the cholinesterase inhibitory activity of Tacrine for the development of multi-target directed ligands to be utilized against neurodegenerative disorders (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThrough correlating structural descriptors with inhibitory activity, analyzing critical binding interactions in the MAO-B active site, and recognizing important elements in the pharmacophore for inhibiting MAO-B, the research endeavor seeks to investigate the structural elements that define MAO-B inhibition [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In the end, the results may provide mechanistic information for the rational design of next-generation compounds for inhibiting MAO-B with increased selectivity, activity, and drug-like character for the potential therapeutic management of Parkinson's disease [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Levodopa, Pramipexole, Ropinirole, Amantadine \u0026amp; Selegiline MAO-B inhibitors used in Parkinson\u0026rsquo;s disease.\u003c/p\u003e \u003cp\u003eSelegiline, a selective MAO-B inhibitor, is used to treat Parkinson's disease and depression. By increasing dopamine levels in the brain, it enhances mood and motor control. Selegiline can be administered orally or Trans dermally, and it may be prescribed alone or in conjunction with other medications [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e"},{"header":"METHODOLOGY","content":"\u003cp\u003eThe study investigates a series of Tacrine-Selegiline hybrid compounds (6a\u0026ndash;o and 7a\u0026ndash;l) synthesized and characterized as multi-target directed ligands (MTDLs) with dual inhibitory activity against cholinesterase (AChE, BuChE) and monoamine oxidases (MAO-A, MAO-B), with a particular focus on their potential as MAO-B inhibitors [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For computational analysis, compounds with reported IC₅₀ values against MAO-B were selected, including the lead molecule 7d, which demonstrated notable inhibitory potency (IC₅₀ = 4.75 \u0026micro;M) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Chemical structures were redrawn using ChemDraw based on the synthetic schemes and NMR data provided in the original article, followed by 3D geometry optimization using the MMFF94 force field in Avogadro [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The IC₅₀ values were annotated as biological endpoints for QSAR modeling, Molecular docking simulations were conducted to assess the binding affinity and interaction profiles of the selected hybrids with MAO-B [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The crystal structure of human MAO-B (PDB ID: 2V5Z) was retrieved from the RCSB Protein Data Bank, and prepared by removing water molecules, adding polar hydrogens, and assigning Kollman charges using AutoDock Tools [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Ligands were converted to PDBQT format after energy minimization, and docking was performed using AutoDock Vina with the grid box centered on key active site residues Tyr398, Tyr435, and the FAD cofactor [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMolecular docking\u003c/h2\u003e \u003cp\u003eMolecular docking is a structure-based computational method used to predict the three-dimensional structure of an active site complex formed between a target protein and a bound ligand [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. It predicts binding affinity through a scoring function, with more negative scores reflecting stronger predicted interactions [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Docking helps identify key features that contribute to binding, compare the efficiencies of various ligands, and supports the design of potent MAO-B inhibitors [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The docking scores of different Tacrine-selegine compounds are shown in the form of Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDocking Score of Tacrine-Selegiline hybrid compounds with IC\u003csub\u003e50\u003c/sub\u003eValue\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCompound Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eIC\u003csub\u003e50\u003c/sub\u003e(\u0026micro;M) \u003csup\u003ea\u003c/sup\u003e \u0026plusmn;SD\u003c/p\u003e \u003cp\u003ehMAO -B\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDocking\u003c/p\u003e \u003cp\u003eScore (Kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6a\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.94\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-12.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6b\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-11.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6c\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-14.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6d\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-12.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6e\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-15.26\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6f\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-14.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6g\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.04\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-11.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6h\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-12.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6i\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-13.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e 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align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-8.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6l\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.63\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-12.62\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6m\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-10.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6n\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e65.61\u0026thinsp;\u0026plusmn;\u0026thinsp;1.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-14.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6o\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.39\u0026thinsp;\u0026plusmn;\u0026thinsp;1.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-9.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e 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align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.70\u0026thinsp;\u0026plusmn;\u0026thinsp;0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-14.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7c\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-10.00\u003c/p\u003e \u003c/td\u003e 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\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-10.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7f\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.78\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-11.40\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7g\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-13.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7h\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-13.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7i\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.77\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-9.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7j\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-14.47\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7k\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-11.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7l\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-10.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eRasagiline\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-10.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eIproniazid\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-7.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTacrine\u0026ndash;Selegiline hybrid-6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-12.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTacrine\u0026ndash;Selegiline hybrid-7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCH\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-13.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe results of how the Tacrine-Selegiline hybrid compounds work with the human monoamine oxidase-B enzyme are shown in Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e. These tables show things like how the compounds stick to the enzyme and how much energy they need to do that. We also look at which amino acids in the enzyme the compounds interact with and how well they can stop the enzyme from working. The scores we get from docking the compounds into the enzyme tell us how well each one can bind to the oxidase-B enzyme. A negative score means the compound sticks to the enzyme really well and the connection, between them is strong and stable. We use these scores to compare the Selegiline hybrid compounds to other things that are known to inhibit the monoamine oxidase-B enzyme.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the docking scores and how they compare to the IC₅₀ values that were found in experiments for compounds 6a to 6o and 7a to 7l. It also includes the reference standards Rasagiline and Iproniazid well as the internal standards \u003cb\u003eTacrine\u0026ndash;Selegiline hybrid-6\u003c/b\u003e and \u003cb\u003eTacrine\u0026ndash;Selegiline hybrid-7\u003c/b\u003e. The docking scores for compounds 6a to 6o and 7a, to 7l are important to look at. Some special mixes of chemicals did well in tests. They worked better than Rasagiline and Iproniazid. This means they can bind to MAO-B easily. The compounds 6e, 6f, 6n, 7b and 7d were really good. They had scores like \u0026minus;\u0026thinsp;15.267 kcal/mol, -14.2556 kcal/mol, -14.858 kcal/mol\u0026thinsp;\u0026minus;\u0026thinsp;14.6758 kcal/mol and \u0026minus;\u0026thinsp;14.9156 kcal/mol. These were better than the drugs and the basic structures they were based on which are \u003cb\u003eTacrine\u0026ndash;Selegiline hybrid 6\u003c/b\u003e and \u003cb\u003eTacrine\u0026ndash;Selegiline hybrid 7\u003c/b\u003e with scores\u0026thinsp;\u0026minus;\u0026thinsp;12.9068 kcal/mol and \u0026minus;\u0026thinsp;13.3117 kcal/mol. The special mixes of chemicals, like compounds 6e, 6f, 6n, 7b and 7d did a job. The docking performance of these derivatives is really good compared to \u003cb\u003eTacrine\u0026ndash;Selegiline hybrid 6\u003c/b\u003e and \u003cb\u003eTacrine\u0026ndash;Selegiline hybrid 7\u003c/b\u003e. This shows that making some changes, to the structure of these derivatives makes them bind to MAO-B better. We also see that when the IC₅₀ values are lower the docking scores are more negative. This means that our docking method is working well and that changing the structure of these derivatives based on what we learn from the docking results is a way to do things in this study. The MAO-B binding of these derivatives is what we are trying to improve.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e gives us a look at how ligands interact with proteins. It lists the amino acid residues that are involved in binding within the MAO-B site. The two standards, \u003cb\u003eTacrine\u0026ndash;Selegiline hybrid 6\u003c/b\u003e and \u003cb\u003eTacrine\u0026ndash;Selegiline hybrid 7\u003c/b\u003e interact with residues like LEU171, CYS172, ILE199 and GLN206. This shows that \u003cb\u003eTacrine\u0026ndash;Selegiline hybrid 6\u003c/b\u003e and \u003cb\u003eTacrine\u0026ndash;Selegiline hybrid 7\u003c/b\u003e are reference compounds because they have interactions with key catalytic residues like LEU171, CYS172, ILE199 and GLN206, in the MAO-B active site. The derivatives that work well like 6e, 6n and 7d seem to interact more with important parts of the molecule including ILE316 and TYR326. These parts are crucial for the derivatives to do their job and stop the enzyme from working. The derivatives 6e, 6n and 7d have consistent interactions, with these critical parts, which is important for them to be effective.\u003c/p\u003e \u003cp\u003eThe thing that really stands out is how often certain amino acids like LEU171, CYS172, ILE199, GLN206, ILE316 and TYR326 are involved in the compounds that score the highest. We see this in \u003cb\u003eTacrine\u0026ndash;Selegiline hybrid 6\u003c/b\u003e and \u003cb\u003eTacrine\u0026ndash;Selegiline hybrid 7\u003c/b\u003e. This shows just how important these amino acids are when it comes to stopping MAO-B from working.\u003c/p\u003e \u003cp\u003eSome compounds are really good at interacting with MAO-B. They have better binding energies than the standard compounds. This means that these compounds fit into the MAO-B binding pocket in a way that's just right, for them, which is a key part of how they work [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Overall, comparison with standards \u003cb\u003eTacrine\u0026ndash;Selegiline hybrid 6\u003c/b\u003e and \u003cb\u003eTacrine\u0026ndash;Selegiline hybrid 7\u003c/b\u003e clearly demonstrates that enhanced MAO-B inhibition is governed not only by binding strength but also by the quality, consistency, and multiplicity of interactions with critical active-site residues. These findings further confirm the success of rational structural optimization in generating potent MAO-B inhibitors with improved inhibitory potential over standard reference compounds.\u003c/p\u003e \u003cp\u003eThe molecular docking interaction of the Tacrine\u0026ndash;Selegiline hybrid compound \u003cb\u003e6n\u003c/b\u003e in the active site of human MAO-B. The figure above highlights the location of the ligand in the binding pocket as well as its interaction with the key amino acid residues involved in the catalytic function of MAO-B.\u003c/p\u003e \u003cp\u003eThe best docked pose of compound 6n in the MAO-B active site, with a docking score of \u003cb\u003e\u0026minus;\u0026thinsp;14.858 kcal/mol.\u003c/b\u003e The binding conformation and interactions of the compound with key residues such as \u003cb\u003eLEU171, CYS172, ILE199, GLN206, ILE316, and TYR326\u003c/b\u003e reveal high binding affinity and validate the compound as a potential effective MAO-B inhibitor.\u003c/p\u003e \u003cp\u003eThe best docked pose of the Tacrine\u0026ndash;Selegiline hybrid compound \u003cb\u003e7d\u003c/b\u003e in the active site of human MAO-B. The compound has a strong binding affinity with a docking score of \u003cb\u003e\u0026minus;\u0026thinsp;14.9156 kcal/mol\u003c/b\u003e, which reveals a stable binding. The presence of key interactions with the crucial active site residues \u003cb\u003eLEU171, CYS172, ILE199, GLN206, ILE316, and TYR326\u003c/b\u003e ensures its high inhibitory potential against MAO-B.\u003c/p\u003e \u003cp\u003eThe specific ligand-protein interaction profile of compound 7d within the MAO-B binding pocket. The figure emphasizes the position of the ligand and its interactions with the conserved catalytic residues, ensuring a best-fit scenario and optimal stabilization within the enzyme pocket. These interactions also confirm compound 7d as a potential MAO-B inhibitor.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.2 QSAR Modeling\u003c/b\u003e:\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.2.1 Computational structural predictors\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eQuantitative structure\u0026ndash;activity relationship (QSAR) studies were conducted to identify the correlation between molecular descriptors and the biological activity (pIC50 values) of the 27 Tacrine-Selegiline hybrids obtained from a literature [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. All the structures were drawn and saved in a .cdx file format as two-dimensional (2D), utilizing the ChemDraw Ultra 12.0 module of Chem Office software [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Then, these 2D structures were converted to three-dimensional (3D) structures and optimized using the Molecular Mechanics 2 (MM2) force field, which is employed to remove steric clashes and convert them into the lowest-energy stable conformation, utilizing the Chem3D Pro 12.0 module of the same software {CambridgeSoft Corporation \u003cem\u003eChemDraw Ultra\u003c/em\u003e, Version 12.0; CambridgeSoft: Cambridge, MA, 2011}[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These 3D structures were used to calculate structural predictors (also referred to as molecular descriptors (Figure no. 3) using the PaDEL Descriptor Computation software, an open source molecular descriptor calculation free software currently available [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. It can compute a total of 1875 descriptors, comprising 1444 one-dimensional (1D) and two-dimensional (2D) descriptors, together with 431 three-dimensional (3D) descriptors, and 12 kinds of fingerprints, amounting to 16,092 bits in total [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The molecular descriptor is the outcome of a logical and mathematical process that converts chemical information, represented symbolically, into a valuable numerical value or the result of a standardized experiment [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Molecular descriptors are essential in chemistry, pharmaceutical sciences, environmental policy, health research, and quality control, as they convert molecules, regarded as tangible entities, into numerical representations, facilitating mathematical analysis of the chemical information inherent in the molecule [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. It measures the molecular framework and quantifies several features of molecular structure, including size, shape, symmetry, complexity, branching, cyclicity, and stereo electronic character. Hence playing a pivotal role in QSAR and molecular drug design [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"716\" height=\"773\"\u003e\u003c/p\u003e\n\u003ch3\u003eMolecular descriptors used in QSAR modeling\u003c/h3\u003e\n\u003cp\u003e \u003cb\u003e2.2.2 Statistical model generation\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eA total of 1,875 descriptors were calculated, and before the creation of the QSAR model, the descriptor set was condensed to 207 [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Descriptors exhibiting perfect constancy and strong intercorrelation were eliminated, based on variance and correlation coefficient thresholds of 0.0001 and 0.99, using the V-WSP algorithm [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSeveral QSAR models with statistically significant results were created. Two of these models were the most reliable and had the best predictive capacity (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. These versions were named Model A and Model B. Both models met the specified standards for internal and external validation of QSAR performance [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The abundance of structural predictors significantly surpasses that of compounds, necessitating the identification of critical predictors for QSAR modeling [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The genetic algorithm-multiple linear regression (GA-MLR) has been used to create a QSAR model using a reduced set of predictor variables after variable selection by the genetic algorithm technique [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]{Broadhurst D., \u003cem\u003eet al\u003c/em\u003e. \u0026ldquo;Genetic algorithms as a method for variable selection in multiple linear regression and partial least squares regression with applications to pyrolysis mass spectrometry\u0026rdquo; [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. The fitness function is computed using the default settings specified in the NanoBridges program. Total iterations\u0026thinsp;=\u0026thinsp;100, equation length\u0026thinsp;=\u0026thinsp;5, crossover probability\u0026thinsp;=\u0026thinsp;1, mutation probability\u0026thinsp;=\u0026thinsp;0.5, initial equations created\u0026thinsp;=\u0026thinsp;100, best equations picked\u0026thinsp;=\u0026thinsp;20, smoothing parameters (LOF computations)\u0026thinsp;=\u0026thinsp;10 [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. A population of 100 distinct random combinations of the computed molecular descriptors is formed [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. A QSAR model is constructed using each parent combination of descriptors for the whole dataset via multiple linear regression (MLR) [De Campos LJ and De Melo EB. \u0026ldquo;Modeling structure-activity relationships of prodiginines with antimalarial activity using GA/MLR and OPS/PLS [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eQSAR Model of Hybrid MAO-B inhibitors.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eQSAR MODEL A\u003c/span\u003e\u003c/p\u003e \u003cp\u003eMLR Eq.\u0026nbsp;9\u003c/p\u003e \u003cp\u003ePic50 = -41.89909(+/-10.5767)\u0026thinsp;+\u0026thinsp;8.87531(+/-1.48459) SaaN\u0026thinsp;+\u0026thinsp;0.21797(+/-0.66238) AATS8s -0.00141(+/-0.00016) ATSC5m\u0026thinsp;+\u0026thinsp;0.17343(+/-0.89449) AATS8e\u003c/p\u003e \u003cp\u003eTest Molecules: 06 (Compound No. are as 1, 4, 12, 14, 22, 19)\u0026thinsp;=\u0026thinsp;22.22%\u003c/p\u003e \u003cp\u003eTraining Molecules: 21 (Compound No. are as 2, 3, 5, 6, 7, 8, 9, 10, 11, 13, 15, 16, 17,\u003c/p\u003e \u003cp\u003e18, 20, 21, 23, 24, 25, 26, 27)\u0026thinsp;=\u0026thinsp;77.77%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eQSAR MODEL B\u003c/span\u003e\u003c/p\u003e \u003cp\u003eMLR Equation:\u003c/p\u003e \u003cp\u003ePic50 = -37.6699(+/-3.49996) -0.00001(+/-0.00001) ATS7i\u0026thinsp;+\u0026thinsp;8.46927(+/-0.70642) SaaN\u0026thinsp;\u0026minus;\u0026thinsp;0.00005(+/-0.00005) VR1_Dzp\u0026thinsp;\u0026minus;\u0026thinsp;0.00124(+/-0.00023) ATSC5m\u003c/p\u003e \u003cp\u003eTotal Test Compounds: 07 (Compound No. are as 5, 8, 10, 12, 14, 21, 23)\u0026thinsp;=\u0026thinsp;25.92%\u003c/p\u003e \u003cp\u003eTotal Training Compounds: 20 (Compound No. are as 1, 2, 3, 4, 6, 7, 9, 11, 13, 15, 16,\u003c/p\u003e \u003cp\u003e17, 18, 19, 20, 22, 24, 27, 25, 26)\u0026thinsp;=\u0026thinsp;77.77%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eQSAR MODEL C\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMLR Equation\u003c/b\u003e:\u003c/p\u003e \u003cp\u003ePic50 = -38.7502(+/-3.56455) +0.43886(+/-2.45053) GGI10\u0026thinsp;+\u0026thinsp;6.16573(+/-1.24601) GATS5m\u0026thinsp;+\u0026thinsp;0.38609(+/-0.47574) GATS7i\u0026thinsp;+\u0026thinsp;7.3407(+/-0.74816) SaaN\u003c/p\u003e \u003cp\u003e\u003cb\u003eTotal Test Compounds\u003c/b\u003e: 07 (Compound No. are as 5, 8, 10, 12, 14, 21, 23)\u0026thinsp;=\u0026thinsp;25.92%\u003c/p\u003e \u003cp\u003e\u003cb\u003eTotal Training Compounds\u003c/b\u003e: 20 (Compound No. are as 1, 2, 3, 4, 6, 7, 9, 11, 13, 15, 16,\u003c/p\u003e \u003cp\u003e17, 18, 19, 20, 22, 24, 27, 25, 26)\u0026thinsp;=\u0026thinsp;77.77%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStatistical parameter\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eThreshold\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eModel A\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eModel B\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eModel C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFitting-criteria\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInternal-validation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR\u0026sup2;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.92099\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSEE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.11114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.11158\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR\u0026sup2;adj\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.93728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.90071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89993\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eF-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt; theoretical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ\u0026sup2;LOO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.92424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.86788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003erm\u0026sup2;LOO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.89392\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.79574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.80532\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eΔrm\u0026sup2;\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.00719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.00232\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExternal-validation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eR\u0026sup2;\u003c/b\u003e\u003csub\u003e\u003cb\u003eext\u003c/b\u003e\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.92193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.91823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003erm\u0026sup2;(test)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.77765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.87085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.85171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eΔrm\u0026sup2; (test)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02881\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05586\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003erpred\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ\u0026sup2;F1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.8386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.89396\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eQ\u0026sup2;F2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.81461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88798\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThree QSAR models (Model A, Model B, and Model C) were assessed utilizing standard fitting, internal validation, and external validation criteria. All models met the requisite criteria (R\u0026sup2; \u0026gt; 0.6, SEE\u0026thinsp;\u0026lt;\u0026thinsp;0.3, Q\u0026sup2; \u0026gt; 0.5, r\u0026sup2;(ext)\u0026thinsp;\u0026gt;\u0026thinsp;0.6), hence validating the statistical acceptability of each model. Model A exhibited superior internal performance within the group, evidenced by the greatest R\u0026sup2; (0.9498), the lowest SEE (0.0889), the highest Q\u0026sup2;LOO (0.9242), and the most favorable Δrm\u0026sup2; (0.007). This indicates that it was highly resilient and did not overfit. Model B had superior external predictivity, evidenced by the highest r\u0026sup2;(ext) (0.9219), rm\u0026sup2;(test) (0.87085), and rpred\u0026sup2; (0.8979) (Table no: 3). This indicates it was more effective in forecasting previously unobserved molecules. Model C performed adequately; however, it was slightly inferior to Models A and B in both internal and external evaluations. Descriptor significance analysis indicated that SaaN, GATS5m, and ATSC5m were the most dependable predictors of activity. The sum of nitrogen E-states (SaaN) exerted a significant and favorable influence on MAO-B inhibitory activity. This indicates that electronically active nitrogen environments are significant. \u0026ldquo;GATS5m\u0026rdquo; (Geary autocorrelation weighted by mass at lag 5) yielded a strong positive coefficient, underscoring the importance of appropriate mass distribution and spatial correlations. \u0026ldquo;ATSC5m\u0026rdquo; exerted a considerable adverse impact, indicating that excessive mass autocorrelation at lag 5 impedes activity (Table no: 4), these descriptors collectively clarify the structural features that govern enhanced MAO-B inhibition relevant to antiparkinsonian efficacy.\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.3 Pharmacophore Modelling\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eThe pharmacophore models the 3D characteristics in the molecules that will define the biological nature of this molecule [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. These include hydrogen bond donor or acceptors, hydrophobic characteristics, aromatic rings or charged groups [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The pharmacophore can be used during drug discovery as a means to understand the ligand\u0026rsquo;s interaction within the target protein and to develop and design an effective ligand based upon that understanding [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. There are currently programs such as LigandScout that automatically take protein-ligand complex data as input and generate three-dimensional pharmacophore models based on experimental information (e.g., X-ray crystal structure or docking analysis) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. The program creates pharmacophore models from ligand-protein interaction data, thus providing a convenient graphical interface for visualizing the three-dimensional pharmacophore and further analysis through hypothesis generation and library screening of compounds for leads [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003e2.4 Toxicity and Safety Considerations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eToxicity and safety evaluation are very important in the development of novel MAO-B inhibitors, especially for compounds that would be administered over a long period of time in chronic neurological diseases like Parkinson\u0026rsquo;s disease [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Theoretically, multi-target Tacrine\u0026ndash;based scaffolds display relatively promising pharmacological actions; however, the parent drug Tacrine is historically associated with clinically significant hepatotoxicity, cholinergic adverse effects, and gastrointestinal intolerance [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Thus, hybrid molecules incorporating Tacrine motifs have to be painstakingly optimized to minimize off-target interactions, reduce metabolic activation to reactive intermediates, and enhance brain selectivity while sustaining therapeutic potency [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eIn silico prediction tools give important preliminary information on safety liabilities that can be expected well before actual experimental testing is performed [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Absorption, distribution, metabolism, excretion, and toxicity parameters can be predicted to forecast blood-brain barrier permeability, cytochrome P450 inhibition, mutagenic or carcinogenic risk, hepatotoxic potential, among others [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Molecules violating more than one drug-likeness rule or showing high predicted toxicity indices may be deprioritized at the initial stage [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBesides, selectivity to MAO-B over MAO-A is a very important determinant of safety. Poor selectivity may raise the risk of hypertensive crises owing to dietary tyramine interactions [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Ideally, rationally designed MAO-B inhibitors should exhibit high central nervous system permeability, favorable therapeutic index, absence of strong CYP inhibition, low hepatotoxicity, and negligible cardiotoxic or genotoxic signals [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. These considerations emphasize the need to integrate toxicity-aware design with computational optimization and subsequent in vitro validation [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMolecular descriptor and Physical interpretation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.N.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSignificant Descriptors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRegression coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eContribution effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePhysical interpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSaaN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;8.87531,\u003c/p\u003e \u003cp\u003e+\u0026thinsp;8.46927,\u003c/p\u003e \u003cp\u003e+\u0026thinsp;7.34070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong positive contribution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAtom-type E-state indices : Sum of aaN E-states\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATSC5m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.00141,\u003c/p\u003e \u003cp\u003e\u0026ndash;0.00124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong negative contribution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCentered Broto-Moreau autocorrelation of lag 5 weighted by mass\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGATS5m\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;6.16573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStrong positive contribution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGeary autocorrelation of lag 5 weighted by mass\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eQSAR modeling yielded three statistically robust and predictive models. Model A had superior internal robustness, whereas Model B demonstrated optimal external predictivity. Descriptor analysis confirmed that SaaN, GATS5m, and ATSC5m are key structural drivers of MAO-B inhibitory activity, emphasizing the importance of nitrogen electronic states and uniform topological mass distribution [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The validated statistical parameters and descriptor significance indicate that these models are effective instruments for the design and enhancement of novel MAO-B inhibitors for the treatment of Parkinson's disease [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLigand-based pharmacophore models were generated using LigandScout 4.4. Active compounds were aligned to identify common features: Aromatization (AI), Hydrogen bond acceptors (HBA), Hydrogen bond Donor (HBD), hydrophobic centers. Fit scores were calculated and validated against known MAO-B inhibitors. This reflects the drug\u0026ndash;receptor interactions [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePharmacophoric features of Standard Hybrid Tacrine-Selegiline-6 \u0026amp; 7\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCompound\u003c/p\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChemical\u003c/p\u003e \u003cp\u003eFormula\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMolecular\u003c/p\u003e \u003cp\u003eWeight (gm/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDocking score\u003c/p\u003e \u003cp\u003e(kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003ePharmacophoric features\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHBA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHBD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e1.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHybrid Tacrine-Selegiline-\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003csub\u003e27\u003c/sub\u003e H\u003csub\u003e31\u003c/sub\u003e N\u003csub\u003e3\u003c/sub\u003e O\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e413.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-12.9068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e2.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6n\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003csub\u003e30\u003c/sub\u003e H\u003csub\u003e36\u003c/sub\u003e N\u003csub\u003e3\u003c/sub\u003e O\u003csub\u003e1\u003c/sub\u003e F\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e473.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-14.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1. \u003cb\u003e3.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHybrid Tacrine-Selegiline-\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003csub\u003e28\u003c/sub\u003e H\u003csub\u003e33\u003c/sub\u003e N\u003csub\u003e3\u003c/sub\u003e O\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e427.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-13.3117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003e4.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7d\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003csub\u003e29\u003c/sub\u003e H\u003csub\u003e37\u003c/sub\u003e N\u003csub\u003e5\u003c/sub\u003e O\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e471.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-14.9156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe things that make a drug work called features and other properties of some special compounds that combine Tacrine and Selegiline were looked at and compared to the standard compounds we know which all is shown in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e. We looked at things, like what the compound's made of, how heavy it is how well it fits into a special spot and what makes it special like parts that can form hydrogen bonds, which are called hydrogen bond acceptors and hydrogen bond donors and other parts like non-ionizable groups and π-interaction sites. Compound 6-std is a chemical with a weight of 413.565 grams per mole. It has a code that is C₂₇H₃₁N₃O₁. When we did some tests, on Compound 6-STD it showed a docking score of \u0026minus;\u0026thinsp;12.9068 kcal/mol. We also looked at what makes Compound 6-std work. Found that it has some important features. Compound 6-std has two places where it can accept hydrogen bonds, one place where it can give a hydrogen bond and one place where it can have a kind of interaction called a π-interaction. Compound 6n is really interesting. It has a weight of 473.636 g/mol and its formula is C₃₀H₃₆N₃O₁F₁. When we looked at Compound 6n we found that it had a better docking score, which was \u0026minus;\u0026thinsp;14.858 kcal/mol. This is better, than what we saw with 6-std. Compound 6n has some features, like three places where it can accept hydrogen bonds, one place where it can give a hydrogen bond and one place where it can have a π-interaction. This makes Compound 6n really unique.\u003c/p\u003e \u003cp\u003eCompound 7-std is a thing with a weight of 427.592 grams per mole. It has a code of C₂₈H₃₃N₃O₁. When we tested Compound 7-std it had a docking score of -13.3117 kilocalories per mole. This Compound 7-std has some features that are important for its job. It has two parts that can take hydrogen bonds one part that can give a hydrogen bond and one part that can do a π-interaction. Compound 7-std is really good, at doing its thing because of these features. Compound 7d is an interesting thing. It has a weight of 471.649 grams per mole. The formula for Compound 7d is C₂₉H₃₇N₅O₁. When we looked at how it fits with other things Compound 7d had a really good score of \u0026minus;\u0026thinsp;14.9156 kilocalories per mole. This was the score out of all the compounds we picked.\u003c/p\u003e \u003cp\u003eWe also did something called analysis on Compound 7d. This analysis showed that Compound 7d has one place where it can accept hydrogen and four places where it can give hydrogen. We did not see any interactions with pi electrons, in Compound 7d. Overall, compounds 6n and 7d demonstrated more favorable docking scores than their corresponding standards, 6-std and 7std, respectively. Differences in hydrogen bonding capacity and Pharmacophoric features were observed among the compounds, corresponding to variations in predicted binding affinity toward the MAO-B active site.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eComparison with Standard Drugs\u003c/h2\u003e \u003cp\u003eTo better understand the performance of the synthesized Tacrine\u0026ndash;Selegiline derivatives, their docking, physicochemical, and pharmacological profiles can be compared against established and successful MAO-B inhibitors, Rasagiline, and Selegiline [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. In this current work, several derivatives have a docking score equal to or better than Rasagiline, which indicates a profound interaction between these derivatives and Tyr398, Tyr435, and the FAD cofactor site of the MAO-B enzyme [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eUnlike single-target approved drugs, hybrid molecules possess dual or multi-target properties to address more than one pathological pathway involved in dopaminergic breakdown and cholinergic system impairment [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Yet, traditional drugs have already established clinical safety, pharmacokinetic profiles, and a history of successful treatment for many decades. A newly developed molecule must establish its advantages in more than one aspect: binding affinity and selectivity/solubility and safety profiles.\u003c/p\u003e \u003cp\u003eFinally, the computational advantages observed for hybrid compounds underscore the great promise of these entities for next-generation agents [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. However, rigorous in vitro studies for enzyme activity, cell toxicity assays, and pharmacology tests on animals should be conducted to allow proper comparisons to current therapeutic standards.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eThe values and explanation of 6 standard compound:\u003c/h3\u003e\n\u003cp\u003eThe compound violated the Pfizer and GSK rules (score\u0026thinsp;=\u0026thinsp;1.0 for both). Golden Triangle rule (score\u0026thinsp;=\u0026thinsp;0.00). The absence of PAINS alerts (0 alerts) confirms that the compound does not contain substructures associated with false-positive biological activity [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Absorption analysis revealed good intestinal permeability, as evidenced by the predicted Caco-2 permeability value (\u0026minus;\u0026thinsp;4.956) and moderate MDCK permeability (\u0026minus;\u0026thinsp;4.601). The compound was neither a P-glycoprotein inhibitor (0.0) nor a significant substrate (0.004), indicating minimal efflux liability, and it demonstrated good human intestinal absorption (\u0026gt;\u0026thinsp;30%) [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Distribution studies showed very high plasma protein binding (98.27%), which may limit the free circulating drug fraction, while the predicted volume of distribution (VDss\u0026thinsp;=\u0026thinsp;0.312) suggests moderate tissue distribution [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Additionally, the high blood\u0026ndash;brain barrier penetration probability (BBB\u0026thinsp;=\u0026thinsp;0.912) indicates the compound\u0026rsquo;s ability to readily access the central nervous system. Metabolic profiling indicated strong interactions with cytochrome P450 enzymes, with the compound acting as a potent inhibitor of CYP1A2 (0.998), CYP2D6 (0.997), and CYP3A4 (0.923), while also serving as a substrate for CYP1A2 (1.0), CYP2C19 (0.996), and CYP3A4 (1.0), suggesting a high potential for metabolic instability and drug\u0026ndash;drug interactions; mild to moderate interactions were also predicted for CYP2B6 and CYP2C8 [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. The predicted elimination half-life was very short (T\u0026frac12; = 0.315 h), indicating rapid systemic clearance. Toxicity predictions revealed significant safety concerns, including a high probability of hERG channel blockade (0.82 and 0.808), suggesting potential cardiotoxicity, along with elevated risks of respiratory toxicity (0.997), hepatotoxicity (0.817), nephrotoxicity (0.983), neurotoxicity, ototoxicity (0.869), and genotoxicity (0.783). Moderate risks were observed for AMES mutagenicity (0.768), carcinogenicity (0.644), skin sensitization (0.749), hematotoxicity (0.602), and rat oral acute toxicity (0.402). In contrast, eye corrosion (0.0) and eye irritation (0.006) risks were negligible, while immunotoxicity in RPMI-8226 cells (0.251) and cytotoxicity toward A549 cells (0.224) were low; however, elevated cytotoxicity was predicted in HEK-293 cells (0.77), indicating potential renal cellular toxicity.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eThe values and explanation of 6n compound:\u003c/h2\u003e \u003cp\u003eThe compound failed both the Pfizer and GSK rules (score 1.0 for each), so there\u0026rsquo;s some real concern about high lipophilicity and molecular size\u0026mdash;both red flags for tricky ADMET properties. The Golden Triangle rule didn\u0026rsquo;t get met either (score 0.0). On the bright side, no PAINS alerts popped up, so there aren\u0026rsquo;t any obvious substructures that usually mess with assays or cause false positives. The compound shows good intestinal permeability\u0026mdash;Caco-2 value at \u0026minus;\u0026thinsp;5.032 backs that up\u0026mdash;and moderate MDCK permeability (\u0026minus;\u0026thinsp;4.579). PAMPA permeability checks out too (0.843), pointing to solid passive diffusion. It\u0026rsquo;s not predicted to be a P-glycoprotein inhibitor (0.006) or much of a substrate (0.004), so efflux shouldn\u0026rsquo;t be a big issue. Human intestinal absorption seems fine (HIA probability 0.003, so HIA\u0026thinsp;\u0026ge;\u0026thinsp;30%). But when it comes to bioavailability, it\u0026rsquo;s probably on the lower side\u0026mdash;there\u0026rsquo;s a strong chance it\u0026rsquo;s under 50% (F50% = 0.883). For distribution, the compound practically glues itself to plasma proteins (PPB\u0026thinsp;=\u0026thinsp;98.43%). That means not much of it is floating around in a free, active form. Volume of distribution is moderate (VDss\u0026thinsp;=\u0026thinsp;0.244 L/kg), so it spreads out a bit but not wildly. It\u0026rsquo;s got a decent shot at crossing the blood\u0026ndash;brain barrier (BBB\u0026thinsp;=\u0026thinsp;0.746), so it could reach the central nervous system. As for hepatic transporters, it\u0026rsquo;s predicted to block OATP1B3 (0.783), but has little effect on OATP1B1, BCRP, and MRP1.Metabolism-wise, the compound really goes after cytochrome P450 enzymes. It\u0026rsquo;s a strong inhibitor of CYP1A2 (0.945) and CYP2D6 (0.879), but only weakly inhibits CYP2C19, CYP2C9, CYP3A4, CYP2B6, and CYP2C8. At the same time, it acts as a substrate for several CYP isoforms\u0026mdash;CYP1A2 (1.0), CYP2C19 (0.998), and CYP3A4 (0.999)\u0026mdash;which means it\u0026rsquo;s likely to be metabolized pretty thoroughly and could cause drug\u0026ndash;drug interactions. Human liver microsomal instability is high (0.899), so it probably won\u0026rsquo;t last long in the liver. When it comes to excretion, plasma clearance is moderate (CL\u0026thinsp;=\u0026thinsp;6.013 mL/min/kg), but the half-life is extremely short (T\u0026frac12; = 0.328 h). So, this compound doesn\u0026rsquo;t stick around\u0026mdash;it\u0026rsquo;s eliminated fast. The toxicity profile, though, is concerning. There\u0026rsquo;s a high chance it blocks hERG channels (0.874 and 0.781 at 10 \u0026micro;M), which raises red flags for cardiotoxicity. Risks for respiratory toxicity (0.998), nephrotoxicity (0.996), neurotoxicity (0.993), ototoxicity (0.931), skin sensitization (0.927), and human hepatotoxicity (0.822) are all high. Moderate risks show up for AMES mutagenicity (0.653), carcinogenicity (0.579), hematotoxicity (0.591), and rat oral acute toxicity (0.37). On the other hand, eye corrosion and irritation risks are close to zero. As for cytotoxicity, there\u0026rsquo;s not much to worry about with A549 (0.078) and RPMI-8226 cells (0.245), but HEK-293 cells show higher sensitivity (0.717), hinting at possible kidney cell toxicity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eThe values and explanation of 7 standard compound:\u003c/h2\u003e \u003cp\u003eThis compound breaks both the Pfizer and GSK rules (scoring a full 1.0 on each), which points to possible trouble with ADMET, especially because of its high lipophilicity. It also falls short on the Golden Triangle rule (0.00 score). On the bright side, there are no PAINS alerts, so the molecule doesn\u0026rsquo;t have those usual substructures that mess with assays or cause fake biological hits. The predicted Caco-2 permeability value (\u0026minus;\u0026thinsp;5.032) sits comfortably in the acceptable range, and the MDCK permeability (\u0026minus;\u0026thinsp;4.579) shows the compound should move across membranes without much trouble. It barely interacts with P-glycoprotein, either as an inhibitor (0.006) or a substrate (0.004), so you don\u0026rsquo;t have to worry about much efflux. Human intestinal absorption checks out too (HIA probability\u0026thinsp;=\u0026thinsp;0.003, meaning absorption should be at least 30%).Distribution throws up some issues, though. The compound binds very tightly to plasma proteins (PPB\u0026thinsp;=\u0026thinsp;98.43%), which means less of the drug stays free in the bloodstream. The predicted volume of distribution (VDss\u0026thinsp;=\u0026thinsp;0.244 L/kg) suggests it reaches tissues moderately well. It does have a strong chance of crossing into the brain (BBB\u0026thinsp;=\u0026thinsp;0.746), which means it can likely get into the central nervous system. On the metabolism front, things get complicated. The compound is predicted to strongly inhibit CYP1A2 (0.945) and CYP2D6 (0.879), and it moderately inhibits CYP3A4 (0.395). It\u0026rsquo;s also a substrate for CYP1A2 (1.0), CYP2C19 (0.998), and CYP3A4 (0.999), so it\u0026rsquo;s likely to get metabolized quickly and could interact with a lot of other drugs. It doesn\u0026rsquo;t do much with CYP2B6 or CYP2C8. Plus, it looks like it\u0026rsquo;s unstable in human liver microsomes (HLM instability\u0026thinsp;=\u0026thinsp;0.899), so it probably won\u0026rsquo;t stick around long in the body. Elimination is fast\u0026mdash;really fast. The predicted half-life is just 0.328 hours, so it gets cleared from the system almost right away. This makes it an ultra-short half-life drug. Toxicity is where most of the red flags pop up. There\u0026rsquo;s a high risk of blocking the hERG channel (0.874 and 0.781), which raises concerns about possible cardiotoxicity. The software also predicts high risks for respiratory toxicity (0.998), liver damage (0.822), kidney toxicity (0.996), neurotoxicity (0.993), and ototoxicity (0.931). Risks for AMES mutagenicity (0.653), carcinogenicity (0.579), skin sensitization (0.927), hematotoxicity (0.591), and rat oral acute toxicity (0.37) sit in the moderate range. Eye corrosion (0.0) and irritation (0.003) aren\u0026rsquo;t an issue at all. Cytotoxicity is low in RPMI-8226 (0.245) and A549 cells (0.078), but it jumps up in HEK-293 cells (0.717), hinting at some kidney cell toxicity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe values and explanation of 7d compound:\u003c/h2\u003e \u003cp\u003eThe compound broke both the Pfizer and GSK rules (score 1.0 for each) and didn\u0026rsquo;t meet the Golden Triangle rule at all (score 0.00). On the bright side, it didn\u0026rsquo;t trigger any PAINS alerts, so there aren\u0026rsquo;t any red flags for false-positive bioactivity. The compound seems to get through the gut wall pretty well, backed up by a solid Caco-2 value (\u0026minus;\u0026thinsp;4.974) and moderate MDCK permeability (\u0026minus;\u0026thinsp;4.57). It doesn\u0026rsquo;t act as a P-glycoprotein inhibitor or substrate (both 0.0), so it\u0026rsquo;s not likely to get pumped out of cells, and it shows good human intestinal absorption\u0026mdash;over 30%.For distribution, it binds strongly to plasma proteins (97.69%), which means less free drug in the bloodstream. The predicted volume of distribution (VDss\u0026thinsp;=\u0026thinsp;0.439) points to moderate tissue spread. Interestingly, it\u0026rsquo;s very likely to cross the blood\u0026ndash;brain barrier (BBB\u0026thinsp;=\u0026thinsp;1.0), so it gets into the central nervous system with ease. The compound interacts with a bunch of cytochrome P450 enzymes. It\u0026rsquo;s a strong inhibitor of CYP1A2, CYP2C19, CYP2C9, CYP2D6, CYP3A4, CYP2B6, and CYP2C8, and it\u0026rsquo;s also a substrate for CYP1A2, CYP2C19, and CYP3A4. That\u0026rsquo;s a recipe for metabolic instability and all sorts of drug\u0026ndash;drug interactions. Elimination happens fast here, with a really short predicted half-life (T\u0026frac12; = 0.295 hours). The body clears it out quickly. The compound scores high for hERG channel blockade (0.97 and 0.881), which points to possible heart risks. There\u0026rsquo;s also a big chance of respiratory toxicity (0.998), liver toxicity (0.881), kidney toxicity (0.999), nervous system toxicity (0.997), ear toxicity (0.923), and genotoxicity (0.894). Risks for AMES mutagenicity (0.615), cancer (0.558), skin sensitization (0.816), blood toxicity (0.603), and acute oral toxicity in rats (0.741) are moderate. On the plus side, eye corrosion (0.0) and irritation (0.001) look like non-issues, and immunotoxicity in RPMI-8226 cells (0.366) and cytotoxicity toward A549 cells (0.261) are low. But it does show high cytotoxicity in HEK-293 cells (0.935), which could mean real trouble for kidney cells.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe computational research conclude that the Hybrid Tacrine-Selegine \u003cb\u003eCompound-6n\u003c/b\u003e and \u003cb\u003e7d\u003c/b\u003e show better interaction and robust predictive performance (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.94982) for MAO-B inhibition. The In-silico ADMET assessment showed positive results for pharmacokinetic and safety attributes which confirmed that the proposed candidates for Parkinson disease. The Pharmacophore model showed the main structural elements similarity with standard that enabled In-silico promised novel compound MAO-B inhibition.\u003c/p\u003e"},{"header":"LIST OF ABBREVIATION","content":"\u003cp\u003ePD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Parkinson\u0026rsquo;s disease\u003c/p\u003e\n\u003cp\u003eMAO-B \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Monoamine Oxidase B\u003c/p\u003e\n\u003cp\u003eROS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Reactive Oxygen Species\u003c/p\u003e\n\u003cp\u003eFDA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Food and Drug Administration\u003c/p\u003e\n\u003cp\u003eBBB \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Blood-Brain Barrier\u003c/p\u003e\n\u003cp\u003eCNS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Central Nervous System\u003c/p\u003e\n\u003cp\u003eQSAR\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Quantitative Structural-Activity Relationship\u003c/p\u003e\n\u003cp\u003eADMET \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Absorption, Distribution, Metabolism, Excretion and Toxicity\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMTDLs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Multi-target directed ligand\u003c/p\u003e\n\u003cp\u003eAchE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Acetylcholinesterase\u003c/p\u003e\n\u003cp\u003eBuChE \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Butyrylcholinesterase\u003c/p\u003e\n\u003cp\u003eMAO-A \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Monoamine Oxidase A\u003c/p\u003e\n\u003cp\u003eNMR\u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Nuclear Magnetic Resonance\u003c/p\u003e\n\u003cp\u003eMMFF94 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Merck Molecular Force Field 94\u003c/p\u003e\n\u003cp\u003eRCSB\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Research Collaboratory for Structural Bioinformatics\u003c/p\u003e\n\u003cp\u003ePDBQT \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Protein Data Bank, Partial Charge (Q), \u0026amp; Atom Type (T)\u003c/p\u003e\n\u003cp\u003eFAD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Flavin Adenine Dinucleotide\u003c/p\u003e\n\u003cp\u003eIC50 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Half-Maximal Inhibitory Concentration\u003c/p\u003e\n\u003cp\u003ePIC50 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Negative Logarithm of the Half-Maximal Inhibitory Concentration\u003c/p\u003e\n\u003cp\u003eMM2 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Molecular Mechanics 2\u003c/p\u003e\n\u003cp\u003ePaDEL \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Pharma Data Exploration Laboratory\u003c/p\u003e\n\u003cp\u003eGA-MLR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Genetics algorithm- multiple linear regression\u003c/p\u003e\n\u003cp\u003eLOF \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Local Outlier Factor\u003c/p\u003e\n\u003cp\u003eMLR \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Multiple linear regression\u003c/p\u003e\n\u003cp\u003eGA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Genetic Algorithm\u003c/p\u003e\n\u003cp\u003eOPS\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Operation and Procedure Classification System\u003c/p\u003e\n\u003cp\u003ePLS \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Primary Lateral Sclerosis\u003c/p\u003e\n\u003cp\u003eCYP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Cytochrome P450\u003c/p\u003e\n\u003cp\u003eAI \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Aromatization\u003c/p\u003e\n\u003cp\u003eHBA \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hydrogen bond acceptors\u003c/p\u003e\n\u003cp\u003eHBD \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Hydrogen bond donor\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors conveyed special thanks to Mr. Jitender Joshi (President), and Prof. (Dr.) Dharam Buddhi (Vice Chancellor) of Uttaranchal University for their research-associated encouragement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA.H. and R.K. wrote the main manuscript text, M.K., K.K. wrote Conceptualization and methodology, A.H., A.K. prepared figures, and M.K, K.K \u0026amp; A.K reviewed and edited. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eETHICAL APPROVALS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study does not involve experiments on animals or human subjects.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBloem BR, Okun MS, Klein C (Jun. 2021) Parkinson\u0026rsquo;s disease. 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Pharm Res 34(3):544\u0026ndash;551. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s11095-016-2086-y\u003c/span\u003e\u003cspan address=\"10.1007/s11095-016-2086-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIacopetta D et al (2023) Impact of Cytochrome P450 Enzymes on the Phase I Metabolism of Drugs, \u003cem\u003eApplied Sciences\u003c/em\u003e Vol. 13, vol. 13, no. 10, May 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/app13106045\u003c/span\u003e\u003cspan address=\"10.3390/app13106045\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table 2","content":"\u003cp\u003eTable 2 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"MAO-B Inhibitors, Parkinson disease, Computational Drug Design, QSAR, Molecular Docking, Pharmacophore Modelling","lastPublishedDoi":"10.21203/rs.3.rs-8908469/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8908469/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eParkinson\u0026rsquo;s disease (PD) remains a pressing neurodegenerative challenge, with monoamine oxidase B (MAO-B) inhibitors offering therapeutic promise by mitigating oxidative stress and dopaminergic neuron loss. This study integrates molecular docking, Quantitative structure-activity relationship (QSAR), Pharmacophore modeling \u0026amp; ADMET to design and evaluate a hybrid Tacrine-Selegiline MAO-B inhibitors for Parkinson disease. A dataset of structurally diverse Hybrid Tacrine-Selegiline MAO-B inhibitors compounds was curated from literature sources and subjected to high-throughput virtual screening via molecular docking against the MAO-B active site (PDB ID: 2V5Z), yielding binding affinities and Key interactions for Novel Hybrid drug. QSAR analysis employed multiple linear regression and algorithms to correlate molecular descriptors with IC50 data, achieving robust predictive performance (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.94982). Complementary ADMET \u0026amp; Pharmacophore modeling identified critical Pharmacophoric features- such as HBA, HBD and AI validated against known inhibitors. Results highlight top-ranking hybrid derivatives of \u003cb\u003eTacrine-Selegine 6n \u0026amp; 7d\u003c/b\u003e compound with enhanced potency, superior binding scores, outperforming reference standard Selegiline which results as Novel drug for Parkinson Disease.\u003c/p\u003e","manuscriptTitle":"Computational Design and Evaluation of MAO-B Inhibitors for Parkinson’s Disease: Molecular Docking, Qsar Model Pharmacophore Modeling and Admet Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-05 08:41:03","doi":"10.21203/rs.3.rs-8908469/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"665870e6-e548-479f-a59d-b8fb7356efe4","owner":[],"postedDate":"March 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T01:09:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-05 08:41:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8908469","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8908469","identity":"rs-8908469","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}