Molecular docking studies for investigating and evaluating some active compounds as potent anti-tubercular agents against InhA Inhibitors: In-Silico design, MD Simulation, DFT and Pharmacokinetics studies

Molecular docking studies for investigating and evaluating some active compounds as potent anti-tubercular agents against InhA Inhibitors: In-Silico design, MD Simulation, DFT and Pharmacokinetics studies | 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 Molecular docking studies for investigating and evaluating some active compounds as potent anti-tubercular agents against InhA Inhibitors: In-Silico design, MD Simulation, DFT and Pharmacokinetics studies Thomas Aondofa Nyijime, Gideon Adamu Shallangwa, Adamu Uzairu, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6726135/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract The ongoing global challenge posed by drug-resistant strains of Mycobacterium tuberculosis underscores the urgent need for novel therapeutic strategies. In this study, a comprehensive in silico approach was utilized to design, analyse, and evaluate a series of small-molecule inhibitors targeting the InhA enzyme, a critical component in mycolic acid biosynthesis. A total of 47 ligands were analysed using molecular docking, quantum chemical calculations, Molecular dynamics simulation, and ADMET profiling. Compounds 10, 12, and 14 exhibited superior binding affinities compared to reference drugs, with compound 14 emerging as the most promising based on MolDock scores, MM/GBSA binding energy (-70.08 kcal/mol), and dynamic stability from a 250 ns molecular dynamics (MD) simulation. Principal component analysis confirmed enhanced conformational stability for compound 14. Based on its favourable binding and non-toxic ADMET profile, compound 14 was chosen as a template compound for the design of two new derivatives. These analogues demonstrated improved docking scores (-132.579 and − 125.894 kcal/mol), high intestinal absorption (> 88%), and no predicted toxicity. The findings support compound 14 and its derivatives as viable InhA inhibitors for further preclinical development in TB therapy. Enoyl-Acyl Carrier Protein Reductase MolDock score MM/GBSA binding energy Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1 Introduction Tuberculosis (TB) remains one of the top causes of death worldwide, exacerbated with the rise of multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains of Mycobacterium tuberculosis ( M . tuberculosis) [ 1 ]. The persistent global burden of TB has necessitated the exploration of new therapeutic targets and the development of antimicrobial compounds capable of effectively inhibit essential enzymes in the bacterial lifecycle. One of such critical targets is the enzyme enoyl-acyl carrier protein reductase (InhA), which is integral to the biosynthesis of mycolic acids a process critical for the bacterium’s survival [ 2 ]. Mycolic acids are essential components of the M . tuberculosis cell wall, contributing to its pathogenicity and resistance to environmental stressors and host immune responses [ 3 ]. Isoniazid, a first-line anti-TB drug, targets InhA indirectly through activation by the catalase-peroxidase enzyme KatG. However, resistance to isoniazid, often due to mutations in katG or the inhA promoter region, has significantly reduced its clinical efficacy [ 4 ], [ 5 ]. This challenge highlights the need to design direct InhA inhibitors that do not rely on prodrug activation, thereby potentially overcoming common resistance mechanisms [ 5 ]. Recent advances in computational drug discovery, particularly molecular docking and molecular dynamics (MD) simulations, offer powerful tools to rationally design and evaluate novel inhibitors with high affinity and specificity for InhA [ 6 ]. Molecular dynamics simulations enable atomistic-level analysis into the conformational flexibility, stability, and binding interactions of protein-ligand complexes at the atomic level. When integrated with structure-based drug design techniques, MD simulations enhance the accuracy of binding affinity predictions and facilitate the refinement of candidate drug molecules [ 7 ], [ 8 ], [ 9 ], [ 10 ]. In this context, computational strategies not only streamline the drug development pipeline streamline the drug development pipeline by minimizing reliance on time-intensive and costly experimental procedures. This study aims to employ a comprehensive in silico workflow comprising virtual compound screening, molecular docking analyses, ADMET prediction, and MD simulations to identify and evaluate potential small-molecule inhibitors targeting InhA from M. tuberculosis . Table 1 present the docking performances of these M . tuberculosis inhibitors with the reference drugs. Table 1 Docking results of Mycobacterium tuberculosis drugs against InhA target protein Drug ID MolDock Score (kcal/mol) Re-rank Score (kcal/mol) H-Bond (kcal/mol) 1 -99.2353 -77.6991 -1.14073 2 -97.931 -82.0614 0.00000 3 -112.839 -92.6879 -0.93284 4 -114.003 -93.3001 0.00000 5 -96.5694 -58.5543 -0.69252 6 -104.387 -87.6198 0.00000 7 -100.267 -80.1269 -1.02493 8 -115.257 -95.7227 0.00000 9 -114.537 -93.7879 -1.59318 10 -117.203 -98.4996 -2.50000 11 -114.035 -95.9645 0.00000 12 -117.631 -96.0656 -1.63809 13 -110.807 -92.2517 -2.50000 14 -118.234 -97.6073 0.00000 15 -108.799 -89.1328 -2.50000 16 -97.6441 -84.3543 -2.45188 17 -106.349 -87.7181 0.00000 18 -111.56 -92.786 -2.47387 19 -104.827 -88.7823 0.00000 20 -97.9745 -81.6951 -2.48928 21 -114.73 -97.8192 -4.67292 22 -114.777 -95.9138 -2.05552 23 -111.409 -92.9195 0.00000 24 -92.347 -76.5387 0.00000 25 -103.201 -89.0137 -2.12106 26 -105.251 -89.0794 0.00000 27 -92.748 -81.9999 0.00000 28 -93.4391 -75.6177 0.00000 29 -104.009 -88.597 -0.76352 30 -102.53 -82.2891 -2.33426 31 -105.239 -85.1229 -2.83433 32 -94.3671 -84.0711 0.00000 33 -99.979 -84.6702 -0.60222 34 -97.3025 -80.5492 0.00000 35 -101.475 -84.4914 -1.12064 36 -81.1207 -68.641 0.00000 37 -109.604 -88.2691 -2.50102 38 -88.7122 -72.3615 0.00000 39 -99.39 -80.5972 -2.11261 40 -108.369 -93.5316 -1.21673 41 -102.885 -84.4272 0.00000 42 -95.1905 -79.424 -0.45905 43 -94.0762 -82.3044 -2.51324 44 -105.909 -89.0231 0.00000 45 -107.728 -84.434 -1.87512 46 -114.584 -96.3517 0.00000 47 -100.231 -82.9213 -0.36641 Isoniazid -61.0128 -56.9407 -3.47069 Ethambutol -93.0599 -78.3219 -6.62007 Pyrazinamide -56.0593 -49.5988 -2.27517 2 Materials and Methods 2.1 Collection of experimental findings We sourced the chemical compound information for the 47 studied derivatives from PubChem (AID_1943325) and compiled in Table 1 . 2.2 Geometry optimization of ligands Ugbe et al [ 11 ] method was utilized for the optimization of the 47 chemical structures as well as the reference drugs. 2.3 Quantum descriptor evaluation Density functional theory calculations were performed following the method previously reported elsewhere [ 11 ] 2.4 Molecular docking Figure 1 illustrates the enzyme structure of InhA (PBD ID: 4U0J) obtained from RCSB [ 12 ]. Molegro Virtual docker (MVD) V6.0 was utilized for this docking studies [ 11 ], [ 12 ]. Initial preparation involved converting protein and ligand structures into PDB format, followed by the removal of extraneous molecules from the receptor. The binding cavity of the receptor having the largest volume was use (558.592), surface (1181.44), radius (15), coordinates (x = 44.79, y = 52.92, z = 81.58). The MolDock SE was use as search parameter with 10 runs, 0.90 flexibility strength, 1.10 Å tolerance, 100.00 as energy penalty score based on RMSD threshold of 2.00 Å, 1500 cycles docking algorithm, 50 simplex evolution size, 50 minimum simulation processes to generate 10 poses respectively [ 11 ], [ 12 ]. 2.5 Molecular dynamics simulation To evaluate the dynamic behaviour of protein-ligand binding, molecular dynamics (MD) simulations were performed exclusively for the top-performing docked complexes (10_4U0J, 12_4U0J and 4U0J) using Schrödinger’s Desmond software [ 11 ], [ 13 ], [ 14 ], [ 15 ], [ 16 ]. The OPLS_2005 force field governed the atomic interactions within each complex. Each system was immersed in a cubic water box (TIP3P model) with buffer zones of 12 Å along the x, y, and z axes, generated via Desmond’s system builder. Charge neutrality was achieved by introducing counterions, with ionic strength calibrated to 0.15 M NaCl. Before the main simulation, energy minimization was carried out over 10,000 steepest descent steps iterations, followed by gradual temperature equilibration (0–300 K) under the NVT ensemble. Thermal and pressure equilibration steps employed the Nose-Hoover Chain thermostat (5 ns) and Martyna-Tobias-Klein barostat (5 ns), respectively. Finally, a 250 ns NPT ensemble simulation was executed with a 12 Å non-bonded cutoff. Simulation snapshots were captured every 10 ps, yielding 5,000 frames for post-simulation analysis [ 16 ], [ 17 ]. 2.6 Pharmacokinetic and drug-likeness predictions To evaluate the therapeutic potential of the novel InhA inhibitors, pharmacokinetic (ADMET: absorption, distribution, metabolism, excretion and toxicity) properties and drug-likeness parameters were computationally assessed. The SwissADME web server ( http://www.swissadme.ch ) were evaluated to predict key ADMET metrics, including oral bioavailability, blood-brain barrier penetration, and cytochrome P450 enzyme interactions [ 18 ]. Lipophilicity (LogP) critical determinant of membrane permeability, were calculated using the WLOGP3 integrated into SwissADME [ 19 ]. Drug-likeness was evaluated using Lipinski’s Rule of Five (molecular weight ≤ 500 Da, LogP ≤ 5, hydrogen bond donors ≤ 5, acceptors ≤ 10) and Veber’s criteria (rotatable bonds ≤ 10, PSA ≤ 140 Ų) to prioritize orally bioavailable candidates [ 20 ], [ 21 ]. Additionally, the pkCSM platform ( https://biosig.lab.uq.edu.au/pkcsm/ ) was utilized to predict toxicity endpoints such as Skin Sensitisation, and AMES mutagenicity, ensuring safety profiles aligned with preclinical requirements [ 22 ]. 3 Results and discussion 3.1 Quantum chemical parameters The calculated energy levels of the highest occupied (HOMO) and lowest unoccupied (LUMO) molecular orbitals for each compound yielded essential data on their chemical reactivity patterns. Negative HOMO and LUMO values across the studied compounds confirmed their thermodynamic stability [ 23 ]. The energy gap (difference between HOMO and LUMO) directly influences molecular reactivity and stability, with smaller energy gaps typically favouring reactivity [ 24 ]. Compound 14 exhibited the smallest gap, while the stability trend followed 12 > 10 > 14, based on gap magnitudes. These energy shifts also hinted at intramolecular charge transfer interactions. To further validate reactivity trends, key parameters such as chemical potential (µ), electronegativity (χ), global softness (S), global hardness (η), and electrophilicity index (ω)[ 9 ] were analysed (Table 2 ). The chemical softness (S) values were similar across compounds, likely due to shared functional groups, reinforcing their stability profiles. Softness governs reactivity, higher values enhance reactivity, whereas lower values promote stability [ 12 , 25 ]. Electrophilicity (ω), which quantifies a molecule’s capacity to attract electrons, showed Compound 12 as the most thermodynamically stable candidate, owing to its notably lower ω value compared to others (Table 2 ) [ 9 ], [ 12 ], [ 25 ]. Figure 2 visualizes the HOMO-LUMO orbitals, critical for understanding electronic transitions and reactivity of molecules. The HOMO indicates the regions of a molecule most likely to donate electrons, while the LUMO highlights areas that are prone to accept electrons [ 9 ]. In compound 10, the HOMO is predominantly localized around the aromatic core, suggesting potential nucleophilic interaction at this site. Compound 12 exhibits a more dispersed orbital distribution across its structure, potentially explaining its lower predicted activity due to reduced orbital overlap. Meanwhile, compound 14 shows a balanced distribution between HOMO and LUMO, implying favourable electronic transitions and possibly greater biological interaction efficiency. Table 2 Quantum chemical descriptors of the studied compounds Variables 10 12 14 E HOMO (eV) -7.00 -6.89 -6.87 E LUMO (eV) -1.39 -0.76 -1.35 Energy bandgap (eV) 5.61 6.13 5.52 Electron affinity (eV) 1.39 0.76 1.35 Ionization energy (eV) 7.00 6.89 6.87 Chemical softness (eV) 0.36 0.33 0.36 Global hardness (eV) 2.81 3.07 2.76 Electronegativity (eV) 4.21 3.83 4.11 Chemical potential (eV) -4.21 -3.83 -4.11 Electrophilicity index (eV) 3.15 2.39 3.06 3.2 QSAR Parameters Quantitative Structure-Activity Relationship (QSAR) studies and molecular orbital analyses are central to structure-based drug development, establishing correlation between molecular properties with biological activity [ 26 ]. The QSAR parameters in Table 3 offer quantitative insights that align with the orbital features observed. Compound 10 has the largest surface area (334.52 Ų) and volume (306.71 Ų), attributes that can enhance passive diffusion across biological membranes [ 27 ]. Its log P value of 3.43 indicates high lipophilicity, which is favourable for oral bioavailability according to Lipinski's Rule of Five [ 28 ]. In contrast, compound 12 exhibits the lowest log P (0.09) and a less negative solvation energy (-11.47 kJ/mol), suggesting poor partitioning into lipid environments and limited bioavailability. Compound 14, despite its moderate log P (2.20), shows the most negative solvation energy (-23.04 kJ/mol), implying strong solute-solvent interactions which may affect its absorption characteristics [ 29 ]. Furthermore, dipole moment and polarizability values indicate each compound's ability to interact with polar biological environments. Compound 12, with the highest dipole moment (3.16 Debye), might exhibit enhanced orientation-based interactions with target receptors [ 30 ]. However, compound 10, despite its lower dipole moment (1.37 Debye), possesses the highest polarizability (64.95 ų), which could contribute to induced-dipole stabilization in hydrophobic pockets of the receptor. Integrating HOMO-LUMO trends (Fig. 2 ) with QSAR descriptors (Table 3 ) suggest that compound 10 has the most favourable pharmacokinetic and electronic profile. Its properties point to good membrane permeability, reasonable stability, and potential biological activity, positioning it as a strong candidate for lead optimization in drug design. Table 3 Computed QSAR results of the studied compounds Parameters 10 12 14 Surface area (Ų) 334.52 291.21 315.18 Polar surface area (Ų) 21.14 15.391 21.208 Volume (Ų) 306.71 262.24 292.61 Solvation energy (kJ/mol) -14.97 -11.47 -23.04 Log P 3.43 0.09 2.20 Polarizability (ų) 64.95 61.20 63.73 Mass (amu) 354.294 311.225 338.732 Dipole Moment (Debye) 1.37 3.16 2.34 3.3 Molecular docking analysis Molecular docking studies were performed to evaluate the binding interactions of all ligands listed in Table 1 , with compounds 10, 12, and 14 exhibiting the most promising binding affinities. The crystal structure of the InhA enzyme (PDB ID: 4U0J) served as the target protein for the docking studies. The three-dimensional structures of the selected top hit compounds with InhA are illustrated in Figs. 3 . The binding interactions between InhA and the test compounds (10, 12, and 14), as well as standard reference drugs, are depicted in Figs. 4 and 5 , respectively. Table 4 provides insight into the molecular interactions between ligands (10, 12, and 14) and the target protein, along with comparisons to standard anti-tubercular drugs: isoniazid, ethambutol, and pyrazinamide. Interaction analysis was performed using Discovery Studio to quantify hydrogen bonds, electrostatic and other non-covalent contacts, and hydrophobic interactions, which are fundamental in determining the binding affinity of ligands to their biological targets [ 9 ]. Results presented in Table 4 , shows that compound 10 exhibits TYR158 conventional hydrogen bond and demonstrates a rich network of hydrophobic interactions with PRO193, LEU218, ILE215, and PHE149 residues. These interactions suggest a strong anchoring of the compound within the hydrophobic pocket of the protein, likely enhancing its binding stability. Additionally, electrostatic contacts with PRO156 and ALA191 residues may contribute to the orientation and proper alignment of the ligand within the active site [ 31 ]. Compound 12 forms multiple hydrogen bonds, including conventional bonding with ILE194 and C-H interactions with ILE215, which indicates enhanced polar interactions compared to compound 10. It also engages with key hydrophobic residues including PHE149 and MET199. These combined interactions suggest that compound 12 is stabilized both by polar and non-polar contacts, which may enhance its binding efficiency and bioactivity [ 32 ]. Compound 14 is distinctive in that it lacks conventional hydrogen bonding but compensates with multiple electrostatic interactions, notably with LYS165 and GLY192. Its extensive hydrophobic contacts spanning residues with MET199, MET147, LEU218, and PHE149 imply deep embedding into the protein’s hydrophobic cleft. The absence of hydrogen bonds might reduce specificity, but the strength of hydrophobic anchoring could offset this limitation. When compared to standard anti-TB drugs, isoniazid primarily utilizes hydrogen bonding (VAL65, GLY96) and shows limited hydrophobic interactions, which aligns with its known small, polar molecular profile. Ethambutol also demonstrates a reliance on polar contacts, especially with ILE194 and PRO156, but shows fewer hydrophobic interactions, possibly reflecting its lower lipophilicity. Pyrazinamide, on the other hand, makes similar contacts as isoniazid, with hydrogen bonding (GLY96) and weak hydrophobic contributions from residues with PHE41 and ILE95. Table 4 Protein-Ligand Interaction Analysis Hydrogen Bond Electrostatic and other interactions Hydrophobic interactions Drug ID Conventional C-H interactions 10 TYR158 - PRO156, ALA191, ASP148 PRO193, LEU218, ILE215, PHE149, ILE21 MET199, MET155 12 ILE194 ILE215 ASP148, MET147 PHE149, MET199, PRO193, LEU218 14 - LYS165, GLY192 PRO156 PRO193, MET155, LEU218, ILE215, PHE149, MET199, MET147, ALA191 Isoniazid VAL65, GLY96 ASP64 - PHE41, ILE122, ILE95 Ethambutol ILE194, PRO156 - - PHE149, ALA157, ILE215, MET103, ILE202 Pyrazinamide GLY96 ASP64 - VAL65, PHE41, ILE95 ILE122 3.4 Drug-Likeness and ADMET Profile Evaluation Table 5 summarizes the predicted drug-likeness properties of the studied compounds based on key physicochemical criteria. All five compounds demonstrated molecular weights (MW) below the generally accepted threshold of 500 g/mol, aligning well with Lipinski’s Rule of Five for drug-likeness [ 28 ]. Notably, none of the compounds violated any of Lipinski's rules, suggesting favourable oral bioavailability. Hydrogen bond acceptors ranged from 6 to 8, while none of the compounds contained hydrogen bond donors, an unusual but acceptable profile depending on the target receptor's binding environment. The calculated WLOGP values ranged from 4.95 to 6.18, indicating moderate to high lipophilicity. Although higher log P values can suggest better membrane permeability, excessive lipophilicity may reduce solubility and increase toxicity [ 33 ]. Despite this, all compounds achieved a bioavailability score of 0.55, which is considered acceptable for oral drugs [ 12 ]. Additionally, synthetic accessibility values fell between 1.89 and 2.31, indicating that these compounds are relatively easy to synthesize a favourable attribute in drug development. Table 6 provides insights into the pharmacokinetic and toxicity profiles of ligands (10, 12, and 14) in comparison to standard anti-tubercular agents. All three test compounds displayed high predicted human intestinal absorption values (> 90%), comparable to isoniazid and exceeding that of ethambutol, which had the lowest absorption (66.96%). This suggests good oral uptake prospect for the studied ligands [ 34 ]. Blood–brain barrier (BBB) permeability results obtained showed that, compounds 10, 12, and 14 exhibited moderate distribution values (0.32–0.49), indicating a limited but notable capacity to cross the BBB. In contrast, isoniazid showed minimal permeability (0.002), and ethambutol was even less permeable (-0.22), while pyrazinamide demonstrated the highest CNS permeability (-0.20). Although CNS penetration is not the primary goal for anti-tubercular agents, moderate permeability may enhance treatment of TB infections in sanctuary sites like the brain [ 35 ]. None of the compounds were identified as substrates or inhibitors of key cytochrome P450 isoforms (CYP2D6 and CYP3A4), except for compound 12, which was flagged as a potential substrate for CYP3A4. This may imply a higher risk of drug-drug interactions and metabolic instability for compound 12 than others [ 8 ]. As for excretion, total clearance rates varied, with compound 10 showing the highest clearance (0.77 mL/min/kg) among the tested ligands, suggesting a faster elimination profile. None of the ligands were identified as substrates for the renal OCT2 transporter, suggesting a lower risk of renal toxicity. Toxicological predictions showed no AMES mutagenicity or skin sensitization for any of the test compounds. However, ethambutol was flagged for potential skin sensitization, indicating a less favourable toxicity profile compared to the studied molecules. Table 5 Anticipated Drug-like properties of the studied compounds Drug ID MW H-bond acceptors H-bond donors WLOGP Lipinski violations Bioavailability Score Synthetic Accessibility 1 293.23 6.00 0.00 4.95 0.00 0.55 1.89 2 354.29 8.00 0.00 6.18 0.00 0.55 2.31 3 354.29 8.00 0.00 6.18 0.00 0.55 2.15 4 311.22 7.00 0.00 5.51 0.00 0.55 1.90 5 338.73 6.00 0.00 5.06 0.00 0.55 2.31 Table 6 ADMET results of ligands and reference drugs ADMET Parameters 10 12 14 Isoniazid Ethambutol Pyrazinamide Intestinal absorption (human) 91.14 91.71 90.26 92.60 66.96 84.84 BBB permeability (distribution) 0.32 0.47 0.49 0.002 -0.22 0.70 CNS permeability (distribution) -2.91 -2.21 -3.21 -3.35 -3.56 -0.20 CYP2D6 substrate (metabolism) No No No No No No CYP3A4 substrate (metabolism) No Yes No No No No CYP2D6 inhibitor (metabolism) No No No No No No CYP3A4 inhibitor (metabolism) No No No No No No Total Clearance (excretion) 0.77 0.21 0.38 0.72 1.23 0.69 Renal OCT2 substrate (excretion) No No No No No No AMES toxicity No No No No No No Skin Sensitisation No No No No Yes No 3.5 Molecular dynamics simulation Based on the robust binding affinities identified through computational docking studies, compounds 10, 12, and 14 were selected as the most promising ligands for subsequent molecular dynamics (MD) simulations. A 250-nanosecond MD simulation was performed to access the temporal dynamic stability and conformational behaviour of the protein-ligand complexes, structural stability, root mean square deviation (RMSD) and root mean square fluctuation (RMSF) analyses were conducted 3.5.1 Binding Energy Analysis Table 7 presents the binding free energy results derived from Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) calculations for the three protein–ligand complexes: 10_4U0J, 12_4U0J, and 14_4U0J. Among these, Complex 14 exhibited the most favourable total binding free energy (-70.08 kcal/mol), indicating stronger ligand-protein stability compared to Complexes 10 (-57.38 kcal/mol) and 12 (-59.78 kcal/mol). These values reflect the cumulative contributions of van der Waals forces, electrostatics, solvation effects, and lipophilic interactions [ 36 ]. A breakdown of the energy components shows that van der Waals interactions were dominant in all three complexes, particularly in Complex 14 (-51.62 kcal/mol), suggesting strong non-polar interactions within the binding pocket. The lipophilic contribution also supported binding stability, with Complex 14 again leading at -21.32 kcal/mol. These findings highlight the role of hydrophobic forces in stabilizing the ligand within the target site [ 37 ]. Although covalent and hydrogen bonding energies were relatively minor across all complexes, they still provided subtle contributions to binding specificity. For instance, the covalent binding component was slightly higher in Complex 14 (1.53 kcal/mol), while hydrogen bonding energies remained negligible in all cases, consistent with docking data indicating limited polar contacts. Table 7 MD Simulation results of Binding Energies of the studied complexes Parameters Complex 10 Complex 12 Complex 14 MMGBSA dG Bind -57.38 -59.78 -70.08 MMGBSA dG Bind Coulomb -17.51 -9.13 -13.84 MMGBSA dG Bind Covalent 1.067 0.17 1.53 MMGBSA dG Bind Hbond -0.51 -0.53 -0.01 MMGBSA dG Bind Lipo -15.48 -20.08 -21.32 MMGBSA dG Bind Packing -0.0015 -2.04 -0.06 MMGBSA dG Bind Solv GB 15.90 8.36 15.24 MMGBSA dG Bind vdW -40.83 -36.54 -51.62 3.5.2 Protein RMSD and RMSF Figures 6 through 11 illustrate dynamic behaviours and interaction patterns throughout the 250 ns simulation for the studied complexes. Molecular dynamics (MD) simulations were carried out for the top-performing complexes (10_4U0J, 12_4U0J, and 14_4U0J). Each protein–ligand complex was simulated to evaluate their dynamic behaviour, specifically through RMSD and RMSF analyses (Figs. 6 A–D and 7 A–D). In the 10_4U0J complex, the protein’s RMSD peaked at approximately 2.6 Å around 120 ns, after which it gradually stabilized at 2.4 Å. The ligand exhibited initial fluctuations, reaching a peak RMSD of 5.4 Å, and later settled to a more stable conformation at 4.8 Å by 180 ns (Fig. 6 A). For the 12_4U0J complex, the protein backbone deviation reached a maximum of 2.7 Å at 80 ns, then a gradual decline and stabilization at 2.4 Å when the simulation end. The ligand showed early fluctuations, peaking at 4.8 Å at 180 ns, before settling at around 4.2 Å (Fig. 6 B). In the 14_4U0J complex, the protein RMSD peaked at 3.2 Å at 150 ns and eventually stabilized at 2.8 Å. The ligand in this complex also showed notable fluctuations in the early phase, reaching 4.8 Å, before achieving a stable conformation of 4.2 Å at 250 ns (Fig. 6 C). The root Mean square deviation (RMSD) plots for the simulated protein–ligand complexes are presented in Fig. 7 (A-D). Fluctuations in RMSD values during molecular dynamics trajectories typically reflect conformational adjustments within the protein–ligand system. Furthermore, RMSD data provide insight into the overall structural stability, root mean square fluctuation (RMSF) values highlight residue-level movements that can influence the compactness and flexibility of the complex structure (Figs. 6 and 7 ) [ 11 ]. Of the three complexes analysed, Complex 10_4U0J exhibited the most stable behaviour throughout the simulation period. This complex demonstrated fewer deviations compared to Complexes 12_4U0J and 14_4U0J. Specifically, in Complex 10_4U0J, the RMSD increased moderately from 1.0 Å to 1.5 Å during the initial 160 ns, followed by a gradual stabilization around an average of 3.5 Å after 200 ns (Fig. 7 A). In contrast, Complex 12_4U0J displayed more pronounced early fluctuations, with RMSD values oscillating between 1.2 Å and 3.0 Å within the first 100 ns before stabilizing at an average of 3.6 Å near the 200 ns region (Fig. 7 B). The 14_4U0J complex exhibited the widest fluctuations across the entire trajectory, with deviations ranging from 0.6 Å to 3.0 Å and reaching a relatively stable plateau around 5.0 Å (Fig. 7 C). These RMSD profiles confirm that all three ligands maintained reasonable structural stability with the 4U0J protein, though Complex 10_4U0J demonstrated superior consistency. RMSF analysis supported this observation, with Fig. 6 A showing that Compound 10 induced fewer positional shifts in the protein residues, indicating a more stable binding mode. 3.5.3 Protein-ligand interactions Based on molecular docking outcomes revealing favourable binding affinities, compounds 10, 12, and 14 were selected for further analysis of their interaction profiles with the InhA protein (PDB ID: 4U0J), focusing on hydrogen bonds, hydrophobic interactions, water bridges, and ionic contacts, as illustrated in Figs. 8 A–C. Hydrogen bonding critical for specificity and strength of ligand binding, due to their significance in drug design. For ligand 10, hydrogen bond interactions with TYR-158 and GLY-104 residues. Ligand 12 formed hydrogen bonds with TYR-158, ILE-194, and THR-196, while ligand 14 bond with LYS-165 and ILE-194 residues. Regarding hydrophobic contacts, ligand 10 established interactions with a broad array of nonpolar residues, including ILE-21, MET-103, PHE-149, MET-155, TYR-158, ALA-191, PRO-193, ILE-194, LEU-197, MET-199, ILE-202, and ILE-215. Ligand 12 engaged hydrophobically with residues including ILE-21, MET-103, MET-147, PHE-149, TYR-158, ALA-191, ALA-198, MET-199, ILE-215, and LEU-218. The most extensive hydrophobic interactions were recorded for ligand 14, which bound with ILE-21, MET-147, PHE-149, MET-155, TYR-158, MET-161, LYS-165, ALA-191, PRO-193, ILE-194, ALA-198, MET-199, ILE-202, ILE-215, LEU-218, and MET-232. Water bridge interactions, which often contribute to the stabilization of ligand–receptor complexes, were also observed. Ligand 10 formed water-mediated interactions with GLN-100, ASP-140, ALA-157, TYR-158, LYS-165, and THR-196. Ligand 12 interacted via water bridges with ASP-148, LYS-165, GLY-192, ILE-194, THR-196, LEU-197, ALA-198, MET-199, and GLU-219. For ligand 14, water bridges involved ILE-95, MET-147, and LYS-165. Notably, none of the ligands exhibited ionic contacts during their binding with the protein’s active site residues. To evaluate the time-dependent stability of the observed interactions, protein-ligand contact frequencies across the simulation trajectory were quantitatively evaluated and visualized in Fig. 9 A–C. The contact counts for all three complexes fluctuated between 0 and 6, indicating dynamic but sustained binding throughout the 250 ns simulation. Moreover, the contact maps revealed multiple deep and continuous interaction bands, suggesting robust and stable associations between each ligand and a range of amino acid residues. 3.5.4 Ligands properties analysis The structural and dynamic behaviours of the ligands were assessed by analysing several key properties, including ligand root mean square deviation (RMSD), molecular surface area (MolSA), radius of gyration (rGyr), polar surface area (PSA), and solvent-accessible surface area (SASA). The results are illustrated in Fig. 10 A–C. For all three complexes, 10_4U0J, 12_4U0J, and 14_4U0J. Ligand RMSD values showed initial fluctuations during the first 10 nanoseconds, after which they gradually stabilized. RMSD values ranged between approximately 0.6 Å and 1.8 Å, with equilibrium values reaching 1.9 Å for complexes 10_4U0J and 14_4U0J, and 1.5 Å for complex 12_4U0J (Fig. 10 A-C). The radius of gyration (rGyr), which reflects the compactness of a ligand’s structure, also displayed minimal fluctuations before stabilizing. The rGyr values spanned from 4.25–5.0 Å for 10_4U0J (equilibrating at 5.2 Å), 3.6–4.4 Å for 12_4U0J (with final equilibrium at 4.4 Å), and 3.6–4.5 Å for 14_4U0J (equilibrating at 4.8 Å). MolSA values remained relatively steady, reflecting minor changes in the overall molecular surface. Specifically, MolSA values ranged from 290 to 300 Ų, stabilizing at 304 Ų for ligand 10; 252 to 270 Ų, stabilizing around 275 Ų for ligand 12; and 264 to 288 Ų, reaching 290 Ų at equilibrium for ligand 14 (Fig. 10 A–C). furthermore, SASA measurements showed initial variability followed by stabilization, indicating steady solvent exposure over time. SASA values for 10_4U0J ranged from 0 to 45 Ų, equilibrating at 50 Ų. For 12_4U0J, the values ranged up to 90 Ų, with equilibrium at 92 Ų, while 14_4U0J showed a similar pattern, equilibrating at 47 Ų. Lastly, the polar surface area (PSA), which reflects the prospect for hydrogen bonding and solubility, stabilized across all complexes after initial adjustments. PSA values ranged between 30 and 48 Ų, stabilizing at 50 Ų for ligand 10; between 0 and 42 Ų, with final equilibrium at 45 Ų for ligand 12; and between 0 and 56 Ų, reaching 60 Ų for ligand 14. Thus, the ligand property profiles across the simulation demonstrated only mild fluctuations during the early stages, followed by consistent equilibrium, indicating that each ligand remained stably bound within the protein’s active site throughout the 250 ns simulation. 3.5.5 Principal Component Analysis (PCA) To investigate structural flexibility, PCA was applied to the Cα atoms of both the unbound (apo) protein and the protein-ligand complexes. The goal was to investigate collective and correlated atomic motions that occur before and after ligand interaction, utilizing eigenvectors derived from the covariance matrix. This method was validated by analysing the corresponding eigenvalues magnitudes, with higher values signifying pronounced collective motions linked to the protein’s functional dynamics [ 38 ]. The significance of each eigenvalue was ranked based on the proportion of variance it contributed throughout the 250 ns molecular dynamics simulation, as illustrated in Fig. 11. In Fig. 11A, the apo protein's first principal component (PC1) accounted for 29.3% of the overall motion, while the second and third components each contributed 12.3%. When examining the transition from the apo form to the complex with ligand 12 (Fig. 11B), PC1 variance dropped to 18.77%, with PC2 and PC3 both at 11.24%. These lower values in the apo form suggest that its Cα atoms exhibit slower conformational dynamics compared to their counterparts in the ligand-bound complex. For ligand 14, as seen in Fig. 11C, the apo protein showed a variance of 35.82% in PC1, and 13.18% in both PC2 and PC3. This higher variance in the apo structure’s major components implies greater flexibility and mobility of its backbone atoms than the ligand-bound form. To further explore the dynamics of protein-ligand interactions, we generated two-dimensional projection plots using the first two principal components (PC1 and PC2). These 2D graphs, depicted in Fig. 11A, illustrate the conformational sampling of protein-ligand complexes with ligands 10, 12, and 14. In this visualization, stable complexes are identified by a smaller occupied phase space, while unstable complexes cover a larger area. The simulation data across the three systems revealed that protein complexes with ligands 12 and 14 occupied more confined phase spaces, suggesting higher conformational stability. In contrast, the complex with ligand 10 demonstrated a broader phase space, indicative of lower stability. The observed trends aligned with established analytical metrics (RMSD, RMSF, and radius of gyration (Rg)), reinforcing the reliability and robustness of the structural dynamics’ interpretation. 3.5.6 Hydrogen-Bond Dynamics during Molecular Simulations Key hydrogen-bonding interactions observed in MD simulations for ligands 10, 12, and 14 are compiled in Table 8 . These bonds play a crucial role in maintaining ligand positioning and preserving structural coherence within the protein’s active site during simulations [ 39 ]. Ligand 10, exhibited a single H-bond between its hydroxyl (-OH) group and TYR158, with a donor-acceptor distance of 2.71 Å and an interaction energy of -1.1 kcal/mol. This This measurement aligns with the optimal favourable range for hydrogen bonding (typically 2.5–3.5 Å), indicating a moderately strong interaction [ 39 ]. For ligand 12, formed a hydrogen bond via its nitrogen atom with the backbone of ILE194 (3.25 Å, -0.7 kcal/mol). Although this bond is slightly weaker compared to ligand 10, it still indicates a stabilizing effect, especially if maintained consistently throughout the simulation. Weak hydrogen bonds of this nature can still significantly influence ligand orientation and protein-ligand affinity when combined with other non-covalent interactions [ 40 ]. Ligand 14 demonstrated a bidirectional hydrogen-bonding mechanism, where both the ligand’s oxygen atom and the side chain nitrogen of ILE194 acted as donors and acceptors. This dual interaction included bonding with ILE95 and ILE194, exhibiting distances of 3.25 Å and 3.11 Å, respectively, and interaction energies of − 0.4 kcal/mol and − 3.4 kcal/mol. Notably, the second interaction, with a more negative energy value, indicates a relatively stronger hydrogen bond, contributing significantly to the overall stabilization of the complex. This suggests that ligand 14 forms a more robust hydrogen bonding network. These interactions are critical for ligand retention and activity, as they often play a dominant role in the specificity and strength of molecular recognition. Among the three ligands, ligand 14 displayed the most favourable hydrogen bonding profile, both in terms of bond strength and multiplicity, which correlate with its higher binding stability, as also indicated by the results from PCA and RMSD analyses. Table 8 Summary of Hydrogen bond interactions during MD simulation of complexes 10, 12, 14 Interactions Distance E(kcal/mol) Ligand Donor Acceptor 10 - OH TYR 158 2.71 -1.1 12 - N ILE 194 3.25 -0.7 3.6 Structure based design Structure-based drug design (SBDD) is a rational and iterative approach that leverages detailed knowledge of the three-dimensional structure of a target protein to design or optimize bioactive compounds with improved affinity, specificity, and pharmacokinetic properties [ 41 ]. By integrating molecular docking, dynamic simulations, and binding free energy analyses, SBDD facilitates the identification of lead molecules capable of targeting key functional residues within a protein’s active site. In this investigation, compound 14 emerged as a particularly promising candidate due to its superior MolDock score and favourable interaction profile, as confirmed through integrated computational assessments (docking/MD simulations) analyses. Notably, compound 14 established multiple synergistic stabilization via hydrogen bonds and hydrophobic forces, which contributed to its high binding energy. Such findings align with earlier findings underscoring the strength and multiplicity of such interactions are key determinants of ligand efficacy [ 42 ]. Beyond its binding performance, compound 14 also exhibited a non-toxic profile, as predicted by in silico ADMET screening tools. This absence of toxicity is a critical consideration in early-stage drug development, where compounds with promising bioactivity often fail due to adverse pharmacological properties [ 43 ]. The dual advantage of strong binding and low toxicity made compound 14 as an ideal scaffold for analogue development. Using compound 14 as a structural scaffold, two optimized derivatives of compound 14 were designed to augment target engagement, refine pharmacokinetics, and mitigate resistance or off-target effects. This approach aligns with the core principles of structure-based design, where modification of functional groups or introduction of specific substituents can fine-tune the physicochemical characteristics of the lead compound [ 44 ]. The newly generated compounds were subjected to docking and in silico screening to evaluate their potential as improved analogues of the parent molecule. The selection of compound 14 as a lead template exemplifies how a systematic structure-based approach can drive the discovery and refinement of novel therapeutic candidates, paving the way for further preclinical validation and optimization. 3.7 Docking Results of design compounds Table 9 presents the molecular docking outcomes for two newly designed compounds evaluated using the MolDock scoring function and the Re-Rank scoring system. The MolDock scores, which represent the estimated ligand-target binding affinity, were − 132.579 kcal/mol and − 125.894 kcal/mol for compounds 1 and 2, respectively. Correspondingly, the Re-Rank scores, which provide a refined estimate by considering additional energy terms such as steric clashes and hydrogen bonding contributions, were − 95.309 kcal/mol and − 92.140 kcal/mol. A more negative MolDock or Re-Rank score suggests a stronger and more favourable interaction between the ligand-protein [ 45 ]. In this context, compound 1 exhibited superior docking performance, indicating a more stable and potentially more efficacious interaction with the receptor than compound 2. This stronger interaction could be attributed to better geometric complementarity or a higher number of favourable interactions (such as hydrogen bonds, hydrophobic contacts) between compound 1 and the residues of the target protein. Furthermore, the docking performance of these novel compounds can be contextualized by comparing them to reference inhibitors or co-crystallized ligands from previous studies. Compounds with MolDock scores more negative than − 120 kcal/mol are empirically linked to high-affinity binding in prior SBDD campaigns [ 46 ]. Both compounds in this study surpass this threshold, indicating their strong potential as inhibitors or modulators of the target protein. Table 9: Docking results of the newly designed compounds 3.8 Drug-Likeness and ADMET Evaluation of design compounds Early-phase drug discovery relies heavily on in silico evaluation of pharmacokinetic and safety profiles to prioritize candidates with optimal drug-like properties. This approach reduces the cost and time associated with experimental testing and helps to eliminate candidates with suboptimal properties [ 47 ]. Table 10 presents the drug-likeness characteristics of the newly designed compounds. Both compounds demonstrated compliance with Lipinski’s Rule of Five, a benchmark for predicting oral bioavailability [ 48 ]. Compound 1 had one violation, with a WLOGP (octanol-water partition coefficient) of 6.51, slightly exceeding the recommended threshold of 5, indicating a higher degree of lipophilicity. In contrast, compound 2 fully complied with all Lipinski criteria, including molecular weight, hydrogen bond donors/acceptors, and logP. Both molecules demonstrated moderate oral bioavailability potential (bioavailability score: 0.55) and favourable synthetic feasibility (SA scores: 2.59 and 2.39), with lower scores reflecting simpler synthesis pathways [ 48 ]. Table 11 summarizes the predicted ADMET properties of the two designed compounds. Both compounds exhibited high human intestinal absorption, with values of 88.45% for compound 1 and 93.51% for compound 2. These values suggest that the compounds are likely to be efficiently absorbed through the gastrointestinal tract when administered orally. In terms of distribution, both compounds demonstrated moderate to low blood-brain barrier (BBB) permeability, with compound 1 showing a value of 0.35 and compound 2 a significantly lower value of 0.05. Correspondingly, both compounds showed negative CNS permeability values, with − 2.84 for compound 1 and − 2.32 for compound 2, placing them outside the CNS-permeable range, which is consistent with limited CNS exposure [ 49 ]. In the metabolism domain, both compounds were non-substrates and non-inhibitors of CYP2D6, minimizing potential for drug–drug interactions involving this enzyme. However, both were predicted to be substrates of CYP3A4, which is responsible for the metabolism of a large proportion of clinically used drugs. Fortunately, neither compound inhibited CYP3A4, reducing concerns about CYP3A4-related metabolic interference. For excretion, the compounds displayed comparable total clearance values (0.82 and 0.84 mL/min/kg), and neither was identified as a renal OCT2 substrate, suggesting elimination through non-OCT2-dependent pathways. Importantly, both compounds were non-toxic based on AMES mutagenicity and skin sensitization predictions. The absence of genotoxicity and dermal reactivity enhances the safety profile of these molecules, supporting their advancement to further stages of development [ 12 ]. Table 10 Drug-likeness characteristics results of the designed compounds S/N MW H-bond acceptors H-bond donors WLOGP Lipinski violations Bioavailability Score Synthetic Accessibility 1 419.87 5.00 1.00 6.51 1.00 0.55 2.59 2 359.39 5.00 1.00 2.91 0.00 0.55 2.39 Table 11 Summary of ADMET results of designed compounds ADMET Parameters 1 2 Intestinal absorption (human) 88.45 93.51 BBB permeability (distribution) 0.35 0.05 CNS permeability (distribution) -2.84 -2.32 CYP2D6 substrate (metabolism) No No CYP3A4 substrate (metabolism) Yes Yes CYP2D6 inhibitor (metabolism) No No CYP3A4 inhibitor (metabolism) No No Total Clearance (excretion) 0.82 0.84 Renal OCT2 substrate (excretion) No No AMES toxicity No No Skin Sensitisation No No 4 Conclusion This study employed an integrated computational strategy to identify promising inhibitors of the Mycobacterium tuberculosis InhA enzyme, a validated drug target involved in mycolic acid synthesis. Through molecular docking, quantum chemical calculations, pharmacokinetic profiling, and 250 ns molecular dynamics simulations, compounds 10, 12, and 14 emerged as potent candidates with strong binding affinity, structural stability, and favourable ADMET properties. Among them, compound 14 stood out for its superior binding energy and interaction profile, making it a suitable lead for structural optimization. Two novel analogues derived from this scaffold demonstrated even greater docking performance and maintained acceptable drug-likeness and safety characteristics. These findings support the utility of structure-based design in anti-tubercular drug discovery and pave the way for experimental validation and further optimization of the proposed compounds as potential therapeutic agents. Declarations Funding This work received no external funding Authorship contribution: TAN: Writing, review & editing, Writing original draft, Conceptualization; GAS: Supervision, Methodology, review & editing; AU: Supervision, Review & editing, Methodology; ABU: Supervision, Review & editing, Methodology; MTI: Supervision, Software, Methodology; MA: Software, Methodology; HAM: Review & editing; AH: Review & editing; JB: Review & editing. All the authors review and accept the manuscript. Competing of interest The authors have no known competing financial interests Ethics approval and consent to participate Not applicable Consent to publish Not applicable Data availability All relevant data from this study are included in the content of this published article. References B. H. Gulumbe, A. Abdulrahim, S. K. Ahmad, K. A. Lawan, and M. B. Danlami, “WHO report signals tuberculosis resurgence: Addressing systemic failures and revamping control strategies,” Decoding Infection and Transmission , vol. 3, p. 100044, 2025, doi: 10.1016/j.dcit.2025.100044. A. Diab, H. Dickerson, and O. Al Musaimi, “Targeting the Heart of Mycobacterium: Advances in Anti-Tubercular Agents Disrupting Cell Wall Biosynthesis,” Pharmaceuticals , vol. 18, no. 1, p. 70, Jan. 2025, doi: 10.3390/ph18010070. S. Maddipatla, P. K. Agnivesh, B. Bakchi, S. Nanduri, N. P. Kalia, and V. M. Yaddanapudi, “New pyrazole-based derivatives targeting MmpL3 transporter in Mycobacterium tuberculosis: design, synthesis, biological evaluation and molecular docking studies,” Mol Divers , Mar. 2025, doi: 10.1007/s11030-025-11152-3. M. T. Stevens, P. M. E. Hawkins, T. Wang, R. J. Payne, and W. J. Britton, “Analogue of the natural product ecumicin causes sustained growth inhibition of Mycobacterium tuberculosis under multiple growth conditions,” Tuberculosis , vol. 151, p. 102594, Mar. 2025, doi: 10.1016/j.tube.2024.102594. V. N. Nono et al. , “Prevalence of katG and inhA mutations associated with isoniazid resistance in Mycobacterium tuberculosis clinical isolates in Cameroon,” BMC Microbiol , vol. 25, no. 1, p. 127, Mar. 2025, doi: 10.1186/s12866-025-03816-9. V. S. Sukhachev et al. , “Assessment of the Efficiency of Selecting Promising Compounds During Virtual Screening Based on Various Estimations of Drug-Likeness,” Pharm Chem J , vol. 58, no. 9, pp. 1388–1396, Dec. 2024, doi: 10.1007/s11094-025-03286-3. P. Xie, X. Wang, Y. Deng, C. Zhang, Y. Zhang, and L. Huang, “Deciphering non-covalent binding mechanism of olive biophenolic secoiridoids interaction with β-lactoglobulin: Multi-spectroscopies, thermodynamics, molecular docking, and molecular dynamics simulation,” Food Hydrocoll , vol. 162, p. 110897, May 2025, doi: 10.1016/j.foodhyd.2024.110897. K. Tabti, A. Sbai, H. Maghat, T. Lakhlifi, and M. Bouachrine, “Computational assessment of the reactivity and pharmaceutical potential of novel triazole derivatives: An approach combining DFT calculations, molecular dynamics simulations, and molecular docking,” Arabian Journal of Chemistry , vol. 17, no. 1, Jan. 2024, doi: 10.1016/j.arabjc.2023.105376. M. A. Alamri et al. , “Development of newer generation Vascular endothelial growth factor Receptor-2 Inhibitors: Pharmacophore based design, virtual Screening, molecular Docking, molecular dynamic Simulation, and DFT analyses,” J King Saud Univ Sci , vol. 36, no. 8, p. 103285, Sep. 2024, doi: 10.1016/j.jksus.2024.103285. A. Moulishankar and T. Sundarrajan, “Pharmacophore, QSAR, molecular docking, molecular dynamics and ADMET study of trisubstituted benzimidazole derivatives as potent anti-tubercular agents,” Chemical Physics Impact , vol. 8, p. 100512, Jun. 2024, doi: 10.1016/j.chphi.2024.100512. F. A. Ugbe et al. , “Computational evaluation of the inhibitory potential of some urea, thiourea, and selenourea derivatives of diselenides against leishmaniasis: 2D-QSAR, pharmacokinetics, molecular docking, and molecular dynamics simulation,” J Mol Struct , vol. 1302, Apr. 2024, doi: 10.1016/j.molstruc.2023.137473. S. H. Abdullahi et al. , “Molecular docking studies of some benzoxazole and benzothiazole derivatives as VEGFR-2 target inhibitors: In silico design, MD simulation, pharmacokinetics and DFT studies,” Intelligent Pharmacy , vol. 2, no. 2, pp. 232–250, Apr. 2024, doi: 10.1016/j.ipha.2023.11.010. M. Abdalla et al. , “In silico studies on phytochemicals to combat the emerging COVID-19 infection,” Journal of Saudi Chemical Society , vol. 25, no. 12, p. 101367, Dec. 2021, doi: 10.1016/j.jscs.2021.101367. M. Abdalla, A. Khalid, J. Hedayati, and M. N. Ghayur, “Cholinesterase Inhibitory Activity of Paeoniflorin: Molecular Dynamics Simulation, and In Vitro Mechanistic Investigation,” Biochem Res Int , vol. 2024, no. 1, Jan. 2024, doi: 10.1155/bri/9192496. D. Wu et al. , “The stability of the SARS-COV-2 structure in the presence of variable external heat flux in the vicinity of the water/silver nanofluid: A molecular dynamics simulation,” Case Studies in Thermal Engineering , vol. 69, p. 105994, May 2025, doi: 10.1016/j.csite.2025.105994. M. El fadili et al. , “In silico insights into the design of novel NR2B-selective NMDA receptor antagonists: QSAR modeling, ADME-toxicity predictions, molecular docking, and molecular dynamics investigations,” BMC Chem , vol. 18, no. 1, Dec. 2024, doi: 10.1186/s13065-024-01248-6. K. Bhattacharya, N. R. Chanu, P. Khanal, A. Bhattacharjee, M. Chakraborty, and D. Das, “Etonogestrel and Alzheimer’s: A multi-scale computational approach to acetylcholinesterase inhibition,” Next Materials , vol. 8, p. 100711, Jul. 2025, doi: 10.1016/j.nxmate.2025.100711. A. Daina, O. Michielin, and V. Zoete, “SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules,” Sci Rep , vol. 7, no. 1, p. 42717, Mar. 2017, doi: 10.1038/srep42717. S. R. Mahapatra et al. , “RETRACTED: The potential of plant-derived secondary metabolites as novel drug candidates against Klebsiella pneumoniae: Molecular docking and simulation investigation,” South African Journal of Botany , vol. 149, pp. 789–797, Sep. 2022, doi: 10.1016/j.sajb.2022.04.043. C. A. Lipinski, F. Lombardo, B. W. Dominy, and P. J. Feeney, “Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings,” Adv Drug Deliv Rev , vol. 64, pp. 4–17, Dec. 2012, doi: 10.1016/j.addr.2012.09.019. D. Vengadesan, N. Arumugam, E. Manikandan, and A. Mandal, “Evaluation of Nanomaterials as Effective Carriers Targeted to Traverse Blood–Brain Barrier for Theragnostics of Neurodegenerative Diseases: An Overview,” Bionanoscience , vol. 15, no. 1, p. 143, Mar. 2025, doi: 10.1007/s12668-024-01773-7. D. E. V. Pires, T. L. Blundell, and D. B. Ascher, “pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures,” J Med Chem , vol. 58, no. 9, pp. 4066–4072, May 2015, doi: 10.1021/acs.jmedchem.5b00104. S. Choudhari, S. K. Patil, and S. Rathod, “Identification of hits as anti-obesity agents against human pancreatic lipase via docking, drug-likeness, in-silico ADME(T), pharmacophore, DFT, molecular dynamics, and MM/PB(GB)SA analysis,” J Biomol Struct Dyn , vol. 42, no. 20, pp. 10688–10710, Dec. 2024, doi: 10.1080/07391102.2023.2258407. M. K. Usha et al. , “Synthesis, crystal structure, Hirshfeld surface analysis, energy frameworks, and DFT calculations of (2 e )-3-(dimethylamino)-1-[5-methyl-1-(4-nitrophenyl)-1 h -1,2,3-triazol-4-yl]prop-2-en-1-one,” Molecular Crystals and Liquid Crystals , vol. 768, no. 6, pp. 11–26, Apr. 2024, doi: 10.1080/15421406.2024.2326337. M. K. Usha et al. , “Synthesis, crystal structure, Hirshfeld surface analysis, energy frameworks, and DFT calculations of (2 e )-3-(dimethylamino)-1-[5-methyl-1-(4-nitrophenyl)-1 h -1,2,3-triazol-4-yl]prop-2-en-1-one,” Molecular Crystals and Liquid Crystals , vol. 768, no. 6, pp. 11–26, Apr. 2024, doi: 10.1080/15421406.2024.2326337. J. Shaikh et al. , “Pharmacophore mapping, 3D QSAR, molecular docking, and ADME prediction studies of novel Benzothiazinone derivatives,” In Silico Pharmacol , vol. 12, no. 2, p. 79, Aug. 2024, doi: 10.1007/s40203-024-00255-8. D. Smith et al. , “Passive Lipoidal Diffusion and Carrier-Mediated Cell Uptake Are Both Important Mechanisms of Membrane Permeation in Drug Disposition,” Mol Pharm , vol. 11, no. 6, pp. 1727–1738, Jun. 2014, doi: 10.1021/mp400713v. C. A. Lipinski, “Rule of five in 2015 and beyond: Target and ligand structural limitations, ligand chemistry structure and drug discovery project decisions,” Adv Drug Deliv Rev , vol. 101, pp. 34–41, Jun. 2016, doi: 10.1016/j.addr.2016.04.029. P. Mehnati, H. Khoshsima, M. S. Zakerhamidi, R. Kian, and M. Sahrai, “Solution environment effects on the photo-physical behavior of Daunorubicin and Mitoxantrone: Anthraquinone-based drugs,” J Mol Struct , vol. 1310, p. 138317, Aug. 2024, doi: 10.1016/j.molstruc.2024.138317. T. A. Nyijime, A. M. Ayuba, and H. F. Chahul, “Experimental and computational studies on activated Bambara groundnut (Vigna subterranean) hulls for the adsorptive removal of herbicides from aqueous solution,” Bull Natl Res Cent , vol. 45, no. 1, p. 189, Dec. 2021, doi: 10.1186/s42269-021-00648-1. L. H. S. Santos, R. S. Ferreira, and E. R. Caffarena, “Integrating Molecular Docking and Molecular Dynamics Simulations,” 2019, pp. 13–34. doi: 10.1007/978-1-4939-9752-7_2. J. Caballero, “The latest automated docking technologies for novel drug discovery,” Expert Opin Drug Discov , vol. 16, no. 6, pp. 625–645, Jun. 2021, doi: 10.1080/17460441.2021.1858793. T. W. Johnson, R. A. Gallego, and M. P. Edwards, “Lipophilic Efficiency as an Important Metric in Drug Design,” J Med Chem , vol. 61, no. 15, pp. 6401–6420, Aug. 2018, doi: 10.1021/acs.jmedchem.8b00077. J. Shaikh et al. , “Pharmacophore mapping, 3D QSAR, molecular docking, and ADME prediction studies of novel Benzothiazinone derivatives,” In Silico Pharmacol , vol. 12, no. 2, p. 79, Aug. 2024, doi: 10.1007/s40203-024-00255-8. Y. H. Momin et al. , “Computational investigation of 2, 4-Di Tert Butyl Phenol as alpha amylase inhibitor isolated from Coccinia grandis (L.) Voigt using molecular docking, and ADMET parameters,” Comput Biol Chem , vol. 110, p. 108087, Jun. 2024, doi: 10.1016/j.compbiolchem.2024.108087. T. Ongtanasup, S. Wanmasae, S. Srisang, C. Manaspon, S. Net-anong, and K. Eawsakul, “In silico investigation of ACE2 and the main protease of SARS-CoV-2 with phytochemicals from Myristica fragrans (Houtt.) for the discovery of a novel COVID-19 drug,” Saudi J Biol Sci , vol. 29, no. 9, p. 103389, Sep. 2022, doi: 10.1016/j.sjbs.2022.103389. G. Klebe, “Protein–Ligand Interactions as the Basis for Drug Action,” in Drug Design , Berlin, Heidelberg: Springer Berlin Heidelberg, 2024, pp. 39–65. doi: 10.1007/978-3-662-68998-1_4. E. I. Edache, H. A. Dawi, and F. A. Ugbe, “3D-QSAR, Molecular Docking, Molecular Dynamics Simulations and Structural Studies of Some Selected Inhibitors of the Glycoprotein (GPC) of Lassa Virus,” Journal of Applied Organometallic Chemistry , vol. 3, no. 3, pp. 224–244, Jul. 2023, doi: 10.48309/jaoc.2023.410946.1103. R. Shukla and T. Tripathi, “Molecular Dynamics Simulation of Protein and Protein–Ligand Complexes,” in Computer-Aided Drug Design , Singapore: Springer Singapore, 2020, pp. 133–161. doi: 10.1007/978-981-15-6815-2_7. K. Liu, E. Watanabe, and H. Kokubo, “Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations,” J Comput Aided Mol Des , vol. 31, no. 2, pp. 201–211, Feb. 2017, doi: 10.1007/s10822-016-0005-2. C. S. C. Canales, A. R. Pavan, J. L. dos Santos, and F. R. Pavan, “In silico drug design strategies for discovering novel tuberculosis therapeutics,” Expert Opin Drug Discov , vol. 19, no. 4, pp. 471–491, Apr. 2024, doi: 10.1080/17460441.2024.2319042. Y. Isyaku, A. Uzairu, S. Uba, M. T. Ibrahim, and A. B. Umar, “QSAR, molecular docking, and design of novel 4-(N,N-diarylmethyl amines) Furan-2(5H)-one derivatives as insecticides against Aphis craccivora,” Bull Natl Res Cent , vol. 44, no. 1, p. 44, Dec. 2020, doi: 10.1186/s42269-020-00297-w. B. Chandrasekaran, S. N. Abed, O. Al-Attraqchi, K. Kuche, and R. K. Tekade, “Computer-Aided Prediction of Pharmacokinetic (ADMET) Properties,” in Dosage Form Design Parameters , vol. 2, Elsevier, 2018, pp. 731–755. doi: 10.1016/B978-0-12-814421-3.00021-X. N. R. Khedkar, M. Sindkhedkar, and A. Joseph, “Fragment-Based Drug Discovery: Small Fragments, Big Impact − Success Stories of Approved Oncology Therapeutics,” Bioorg Chem , vol. 156, p. 108197, Mar. 2025, doi: 10.1016/j.bioorg.2025.108197. M. S. Raghu et al. , “Design, synthesis and docking studies of new molecular hybrids bearing benzimidazole and thiazolidine-2,4-dione as potential antitubercular agents,” Journal of the Indian Chemical Society , vol. 101, no. 10, p. 101346, 2024, doi: https://doi.org/10.1016/j.jics.2024.101346. A. Ajala et al. , “QSAR application of natural therapeutics inhibitors against Alzheimer’s disease through in-silico virtual-screening, docking-simulation, molecular dynamics, and pharmacokinetic prediction analysis,” Intelligent Pharmacy , vol. 2, no. 4, pp. 505–515, 2024, doi: https://doi.org/10.1016/j.ipha.2023.12.004. A. Ajala et al. , “In-silico screening and ADMET evaluation of therapeutic MAO-B inhibitors against Parkinson disease,” Intelligent Pharmacy , vol. 2, no. 4, pp. 554–564, 2024, doi: https://doi.org/10.1016/j.ipha.2023.12.008. A.-K. Sohlenius-Sternbeck and Y. Terelius, “Evaluation of ADMET Predictor in Early Discovery Drug Metabolism and Pharmacokinetics Project Work,” Drug Metabolism and Disposition , vol. 50, no. 2, pp. 95–104, Feb. 2022, doi: 10.1124/dmd.121.000552. S. N. Adawara, G. A. Shallangwa, P. A. Mamza, and A. Ibrahim, “Molecular docking and QSAR theoretical model for prediction of phthalazinone derivatives as new class of potent dengue virus inhibitors,” Beni Suef Univ J Basic Appl Sci , vol. 9, no. 1, Dec. 2020, doi: 10.1186/s43088-020-00073-9. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Jun, 2025 Reviews received at journal 23 Jun, 2025 Reviews received at journal 18 Jun, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviewers agreed at journal 13 Jun, 2025 Reviewers invited by journal 12 Jun, 2025 Editor invited by journal 11 Jun, 2025 Editor assigned by journal 25 May, 2025 Submission checks completed at journal 25 May, 2025 First submitted to journal 22 May, 2025 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6726135","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470753603,"identity":"c2aeddbc-ae9a-4f53-a6b7-f02e26b8a083","order_by":0,"name":"Thomas Aondofa Nyijime","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYDACCSjNxnCwgYGhAshgJ6CDB1XLGSCDmVgtYMDYBiQIabGXbj664cefO4l9jIdbN3ycVyvPx8zA+OFjDh5bZI6l3exte5bYxnCw7ebMbccN25gZmCVnbsPnsByzG7wNh8FabvNuO8YI1MLGzEtAy80/f6Ba/s45Zk+Ults8bFAtjA01iYS13EhLuy3bdtgY7JeeYweS25gZm/H6hX1G8rGbb/4clp0/4/izGz9q6mzntzcf/PARjxYYcGyQOACiDwMxYwNh9UBgz8APVlhHlOpRMApGwSgYWQAAioBZXxtjOCcAAAAASUVORK5CYII=","orcid":"","institution":"Joseph Sarwuan Tarka University","correspondingAuthor":true,"prefix":"","firstName":"Thomas","middleName":"Aondofa","lastName":"Nyijime","suffix":""},{"id":470753604,"identity":"ea8bd5a0-12d2-410e-8a78-ed38a19be66d","order_by":1,"name":"Gideon Adamu Shallangwa","email":"","orcid":"","institution":"Ahmadu Bello University","correspondingAuthor":false,"prefix":"","firstName":"Gideon","middleName":"Adamu","lastName":"Shallangwa","suffix":""},{"id":470753605,"identity":"ef906f35-fc64-4c51-b954-12ed5783de2d","order_by":2,"name":"Adamu Uzairu","email":"","orcid":"","institution":"Ahmadu Bello University","correspondingAuthor":false,"prefix":"","firstName":"Adamu","middleName":"","lastName":"Uzairu","suffix":""},{"id":470753606,"identity":"905517f2-6746-4d32-9195-509c79770c52","order_by":3,"name":"Abdullahi Bello Umar","email":"","orcid":"","institution":"Ahmadu Bello University","correspondingAuthor":false,"prefix":"","firstName":"Abdullahi","middleName":"Bello","lastName":"Umar","suffix":""},{"id":470753607,"identity":"9a434c17-a3fc-4867-8cc9-00ac3dfe84c3","order_by":4,"name":"Muhammad Tukur Ibrahim","email":"","orcid":"","institution":"Ahmadu Bello University","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Tukur","lastName":"Ibrahim","suffix":""},{"id":470753608,"identity":"59917adf-0838-410e-a777-a23f4723de36","order_by":5,"name":"Mohnad Abdalla","email":"","orcid":"","institution":"Children’s Hospital Affiliated to Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Mohnad","middleName":"","lastName":"Abdalla","suffix":""},{"id":470753609,"identity":"b9b80a46-8d82-4cdf-8aa9-46ef0cc52ec9","order_by":6,"name":"Abdullah Hamadi","email":"","orcid":"","institution":"University of Tabuk","correspondingAuthor":false,"prefix":"","firstName":"Abdullah","middleName":"","lastName":"Hamadi","suffix":""},{"id":470753610,"identity":"fc3ef012-1288-4acd-a7a9-8002b20cafb6","order_by":7,"name":"Jameel Barnawi","email":"","orcid":"","institution":"University of Tabuk","correspondingAuthor":false,"prefix":"","firstName":"Jameel","middleName":"","lastName":"Barnawi","suffix":""},{"id":470753611,"identity":"e2a2ca8e-671e-4aa9-bb72-fd9e5402fc21","order_by":8,"name":"Hassan A Madkhali","email":"","orcid":"","institution":"Prince Sattam Bin Abdulaziz University","correspondingAuthor":false,"prefix":"","firstName":"Hassan","middleName":"A","lastName":"Madkhali","suffix":""}],"badges":[],"createdAt":"2025-05-22 14:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6726135/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6726135/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84707798,"identity":"fd596ab8-f655-4b6a-86e6-a515065630de","added_by":"auto","created_at":"2025-06-16 12:50:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":121848,"visible":true,"origin":"","legend":"\u003cp\u003e2D snapshot of the co-crystallized ligand (PDB ID: 4U0J)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6726135/v1/130bc6a156f2814edc600fbd.png"},{"id":84707799,"identity":"8809bd17-121a-4c4f-9952-e660e5fe396d","added_by":"auto","created_at":"2025-06-16 12:50:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":265768,"visible":true,"origin":"","legend":"\u003cp\u003eSnapshot of HOMO-LUMO orbitals of compounds 10, 12 and 14\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6726135/v1/5ff42f41017dea1a128c1ba0.png"},{"id":84708922,"identity":"195bbfd7-4a52-4462-82a5-d03777ea4b06","added_by":"auto","created_at":"2025-06-16 12:58:05","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":299920,"visible":true,"origin":"","legend":"\u003cp\u003e3D ligand-receptor interaction structures of compounds with the co-crystal (PDB: 4U0J)\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6726135/v1/7b795c5273fd71cdf50d184f.jpeg"},{"id":84707801,"identity":"260a754b-7b3e-4696-88cc-4ab8b9044e3a","added_by":"auto","created_at":"2025-06-16 12:50:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":228196,"visible":true,"origin":"","legend":"\u003cp\u003e3D Structural visualization of protein 4U0J alongside 2D binding interaction with compounds 10, 12 and 14\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6726135/v1/b6c7a52156b4db3ae666e2b1.png"},{"id":84707803,"identity":"41ddcbd7-a35a-47fe-9f82-10a8677d564d","added_by":"auto","created_at":"2025-06-16 12:50:05","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":128446,"visible":true,"origin":"","legend":"\u003cp\u003eThe 2D ligand-protein interaction diagrams (4U0J_reference drugs)\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6726135/v1/990a1b8bbe012e4a8564b03d.png"},{"id":84707804,"identity":"d007e3b7-f296-451a-955a-5246d7b01cb3","added_by":"auto","created_at":"2025-06-16 12:50:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":208834,"visible":true,"origin":"","legend":"\u003cp\u003eRMSD results of complexes (A) 10_4U0J (B) 12_4U0J (C) 14_4U0J\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6726135/v1/5add65103706e253ae661537.png"},{"id":84707815,"identity":"481e01ec-012b-4a77-811c-dadf7d9d2232","added_by":"auto","created_at":"2025-06-16 12:50:05","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":104163,"visible":true,"origin":"","legend":"\u003cp\u003eResults of RMSF of complexes (A) 10_4U0J (B) 12_4U0J (C) 14_4U0J\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-6726135/v1/9afaaae45bcdf6c61abb1f9b.png"},{"id":84708927,"identity":"b025327a-a61b-44b8-a962-e6f283033ca8","added_by":"auto","created_at":"2025-06-16 12:58:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":93766,"visible":true,"origin":"","legend":"\u003cp\u003eResults of Protein-ligands contacts of complexes (A) 10_4U0J (B) 12_4U0J (C) 14_4U0J\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-6726135/v1/2d317fcd6e4d05abb9f0cc6c.png"},{"id":84707825,"identity":"3959f5ae-6349-4506-9277-a31e2cc09695","added_by":"auto","created_at":"2025-06-16 12:50:05","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":202203,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of Ligand-protein contact of the studied complexes\u003c/p\u003e","description":"","filename":"floatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-6726135/v1/a038a7d52a4d6dcca8683acc.png"},{"id":84708933,"identity":"c6ed4707-b8e1-4cb4-aa12-c0272193b687","added_by":"auto","created_at":"2025-06-16 12:58:06","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":275248,"visible":true,"origin":"","legend":"\u003cp\u003eMD results of ligand property of complexes (A) 10_4U0J (B) 12_4U0JN(C) 14_4U0JTop of FormBottom of Form\u003c/p\u003e","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-6726135/v1/ce983b9d965e7a72ece0aeb0.png"},{"id":84708934,"identity":"06b73207-266d-4331-80ee-813ebcbad303","added_by":"auto","created_at":"2025-06-16 12:58:06","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":1139614,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of protein backbone of complexes (A) 10_4U0J (B) 12_4U0J (C) 14_4U0J\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-6726135/v1/be48fbb92e5e22362c24aadb.png"},{"id":84710272,"identity":"f7bfc595-fa41-49fe-97fb-c8c5b33f208f","added_by":"auto","created_at":"2025-06-16 13:14:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4560037,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6726135/v1/0e4ab350-921f-428b-b569-e67328105549.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eMolecular docking studies for investigating and evaluating some active compounds as potent anti-tubercular agents against InhA Inhibitors: \u003cem\u003eIn-Silico\u003c/em\u003e design, MD Simulation, DFT and Pharmacokinetics studies\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eTuberculosis (TB) remains one of the top causes of death worldwide, exacerbated with the rise of multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e (\u003cem\u003eM\u003c/em\u003e. tuberculosis) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The persistent global burden of TB has necessitated the exploration of new therapeutic targets and the development of antimicrobial compounds capable of effectively inhibit essential enzymes in the bacterial lifecycle. One of such critical targets is the enzyme enoyl-acyl carrier protein reductase (InhA), which is integral to the biosynthesis of mycolic acids a process critical for the bacterium\u0026rsquo;s survival [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Mycolic acids are essential components of the \u003cem\u003eM\u003c/em\u003e. tuberculosis cell wall, contributing to its pathogenicity and resistance to environmental stressors and host immune responses [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIsoniazid, a first-line anti-TB drug, targets InhA indirectly through activation by the catalase-peroxidase enzyme KatG. However, resistance to isoniazid, often due to mutations in \u003cem\u003ekatG\u003c/em\u003e or the \u003cem\u003einhA\u003c/em\u003e promoter region, has significantly reduced its clinical efficacy [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This challenge highlights the need to design direct InhA inhibitors that do not rely on prodrug activation, thereby potentially overcoming common resistance mechanisms [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Recent advances in computational drug discovery, particularly molecular docking and molecular dynamics (MD) simulations, offer powerful tools to rationally design and evaluate novel inhibitors with high affinity and specificity for InhA [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMolecular dynamics simulations enable atomistic-level analysis into the conformational flexibility, stability, and binding interactions of protein-ligand complexes at the atomic level. When integrated with structure-based drug design techniques, MD simulations enhance the accuracy of binding affinity predictions and facilitate the refinement of candidate drug molecules [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In this context, computational strategies not only streamline the drug development pipeline streamline the drug development pipeline by minimizing reliance on time-intensive and costly experimental procedures. This study aims to employ a comprehensive \u003cem\u003ein silico\u003c/em\u003e workflow comprising virtual compound screening, molecular docking analyses, ADMET prediction, and MD simulations to identify and evaluate potential small-molecule inhibitors targeting InhA from \u003cem\u003eM. tuberculosis\u003c/em\u003e. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e present the docking performances of these \u003cem\u003eM\u003c/em\u003e. tuberculosis inhibitors with the reference drugs.\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 results of \u003cem\u003eMycobacterium\u003c/em\u003e tuberculosis drugs against InhA target protein\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMolDock Score (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRe-rank Score (kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH-Bond (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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-99.2353\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-77.6991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.14073\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-97.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-82.0614\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-112.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-92.6879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.93284\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-114.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-93.3001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-96.5694\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-58.5543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.69252\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-104.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-87.6198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-100.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-80.1269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.02493\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-115.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-95.7227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-114.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-93.7879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.59318\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-117.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-98.4996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.50000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-114.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-95.9645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-117.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-96.0656\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.63809\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-110.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-92.2517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.50000\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-118.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-97.6073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-108.799\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-89.1328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.50000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-97.6441\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-84.3543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.45188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-106.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-87.7181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-111.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-92.786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.47387\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-104.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-88.7823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-97.9745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-81.6951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.48928\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-114.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-97.8192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.67292\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-114.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-95.9138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.05552\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-111.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-92.9195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-92.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-76.5387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-103.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-89.0137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.12106\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-105.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-89.0794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-92.748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-81.9999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-93.4391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-75.6177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-104.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-88.597\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.76352\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-102.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-82.2891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.33426\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=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-105.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-85.1229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.83433\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-94.3671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-84.0711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-99.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-84.6702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.60222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-97.3025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-80.5492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-101.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-84.4914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.12064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-81.1207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-68.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-109.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-88.2691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.50102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-88.7122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-72.3615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-99.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-80.5972\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.11261\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-108.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-93.5316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.21673\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-102.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-84.4272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-95.1905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-79.424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.45905\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-94.0762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-82.3044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.51324\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-105.909\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-89.0231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-107.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-84.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.87512\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-114.584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-96.3517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-100.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-82.9213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.36641\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsoniazid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-61.0128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-56.9407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.47069\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEthambutol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-93.0599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-78.3219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-6.62007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePyrazinamide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-56.0593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-49.5988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.27517\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"2 Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Collection of experimental findings\u003c/h2\u003e \u003cp\u003eWe sourced the chemical compound information for the 47 studied derivatives from PubChem (AID_1943325) and compiled in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Geometry optimization of ligands\u003c/h2\u003e \u003cp\u003eUgbe et al [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] method was utilized for the optimization of the 47 chemical structures as well as the reference drugs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Quantum descriptor evaluation\u003c/h2\u003e \u003cp\u003eDensity functional theory calculations were performed following the method previously reported elsewhere [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Molecular docking\u003c/h2\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the enzyme structure of InhA (PBD ID: 4U0J) obtained from RCSB [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Molegro Virtual docker (MVD) V6.0 was utilized for this docking studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Initial preparation involved converting protein and ligand structures into PDB format, followed by the removal of extraneous molecules from the receptor. The binding cavity of the receptor having the largest volume was use (558.592), surface (1181.44), radius (15), coordinates (x\u0026thinsp;=\u0026thinsp;44.79, y\u0026thinsp;=\u0026thinsp;52.92, z\u0026thinsp;=\u0026thinsp;81.58). The MolDock SE was use as search parameter with 10 runs, 0.90 flexibility strength, 1.10 \u0026Aring; tolerance, 100.00 as energy penalty score based on RMSD threshold of 2.00 \u0026Aring;, 1500 cycles docking algorithm, 50 simplex evolution size, 50 minimum simulation processes to generate 10 poses respectively [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Molecular dynamics simulation\u003c/h2\u003e \u003cp\u003eTo evaluate the dynamic behaviour of protein-ligand binding, molecular dynamics (MD) simulations were performed exclusively for the top-performing docked complexes (10_4U0J, 12_4U0J and 4U0J) using Schr\u0026ouml;dinger\u0026rsquo;s Desmond software [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The OPLS_2005 force field governed the atomic interactions within each complex. Each system was immersed in a cubic water box (TIP3P model) with buffer zones of 12 \u0026Aring; along the x, y, and z axes, generated via Desmond\u0026rsquo;s system builder. Charge neutrality was achieved by introducing counterions, with ionic strength calibrated to 0.15 M NaCl. Before the main simulation, energy minimization was carried out over 10,000 steepest descent steps iterations, followed by gradual temperature equilibration (0\u0026ndash;300 K) under the NVT ensemble. Thermal and pressure equilibration steps employed the Nose-Hoover Chain thermostat (5 ns) and Martyna-Tobias-Klein barostat (5 ns), respectively. Finally, a 250 ns NPT ensemble simulation was executed with a 12 \u0026Aring; non-bonded cutoff. Simulation snapshots were captured every 10 ps, yielding 5,000 frames for post-simulation analysis [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Pharmacokinetic and drug-likeness predictions\u003c/h2\u003e \u003cp\u003eTo evaluate the therapeutic potential of the novel InhA inhibitors, pharmacokinetic (ADMET: absorption, distribution, metabolism, excretion and toxicity) properties and drug-likeness parameters were computationally assessed. The SwissADME web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swissadme.ch\u003c/span\u003e\u003cspan address=\"http://www.swissadme.ch\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were evaluated to predict key ADMET metrics, including oral bioavailability, blood-brain barrier penetration, and cytochrome P450 enzyme interactions [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Lipophilicity (LogP) critical determinant of membrane permeability, were calculated using the WLOGP3 integrated into SwissADME [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Drug-likeness was evaluated using Lipinski\u0026rsquo;s Rule of Five (molecular weight\u0026thinsp;\u0026le;\u0026thinsp;500 Da, LogP\u0026thinsp;\u0026le;\u0026thinsp;5, hydrogen bond donors\u0026thinsp;\u0026le;\u0026thinsp;5, acceptors\u0026thinsp;\u0026le;\u0026thinsp;10) and Veber\u0026rsquo;s criteria (rotatable bonds\u0026thinsp;\u0026le;\u0026thinsp;10, PSA\u0026thinsp;\u0026le;\u0026thinsp;140 \u0026Aring;\u0026sup2;) to prioritize orally bioavailable candidates [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additionally, the pkCSM platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biosig.lab.uq.edu.au/pkcsm/\u003c/span\u003e\u003cspan address=\"https://biosig.lab.uq.edu.au/pkcsm/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was utilized to predict toxicity endpoints such as Skin Sensitisation, and AMES mutagenicity, ensuring safety profiles aligned with preclinical requirements [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results and discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Quantum chemical parameters\u003c/h2\u003e\n \u003cp\u003eThe calculated energy levels of the highest occupied (HOMO) and lowest unoccupied (LUMO) molecular orbitals for each compound yielded essential data on their chemical reactivity patterns. Negative HOMO and LUMO values across the studied compounds confirmed their thermodynamic stability [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. The energy gap (difference between HOMO and LUMO) directly influences molecular reactivity and stability, with smaller energy gaps typically favouring reactivity [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. Compound 14 exhibited the smallest gap, while the stability trend followed 12\u0026thinsp;\u0026gt;\u0026thinsp;10\u0026thinsp;\u0026gt;\u0026thinsp;14, based on gap magnitudes. These energy shifts also hinted at intramolecular charge transfer interactions. To further validate reactivity trends, key parameters such as chemical potential (\u0026micro;), electronegativity (\u0026chi;), global softness (S), global hardness (\u0026eta;), and electrophilicity index (\u0026omega;)[\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e] were analysed (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The chemical softness (S) values were similar across compounds, likely due to shared functional groups, reinforcing their stability profiles. Softness governs reactivity, higher values enhance reactivity, whereas lower values promote stability [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. Electrophilicity (\u0026omega;), which quantifies a molecule\u0026rsquo;s capacity to attract electrons, showed Compound 12 as the most thermodynamically stable candidate, owing to its notably lower \u0026omega; value compared to others (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e], [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. Figure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e visualizes the HOMO-LUMO orbitals, critical for understanding electronic transitions and reactivity of molecules. The HOMO indicates the regions of a molecule most likely to donate electrons, while the LUMO highlights areas that are prone to accept electrons [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. In compound 10, the HOMO is predominantly localized around the aromatic core, suggesting potential nucleophilic interaction at this site. Compound 12 exhibits a more dispersed orbital distribution across its structure, potentially explaining its lower predicted activity due to reduced orbital overlap. Meanwhile, compound 14 shows a balanced distribution between HOMO and LUMO, implying favourable electronic transitions and possibly greater biological interaction efficiency.\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eQuantum chemical descriptors of the studied compounds\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE\u003csub\u003eHOMO\u003c/sub\u003e (eV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eE\u003csub\u003eLUMO\u003c/sub\u003e (eV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnergy bandgap (eV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectron affinity (eV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIonization energy (eV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemical softness (eV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGlobal hardness (eV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectronegativity (eV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChemical potential (eV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eElectrophilicity index (eV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 QSAR Parameters\u003c/h2\u003e\n \u003cp\u003eQuantitative Structure-Activity Relationship (QSAR) studies and molecular orbital analyses are central to structure-based drug development, establishing correlation between molecular properties with biological activity [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. The QSAR parameters in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e offer quantitative insights that align with the orbital features observed. Compound 10 has the largest surface area (334.52 \u0026Aring;\u0026sup2;) and volume (306.71 \u0026Aring;\u0026sup2;), attributes that can enhance passive diffusion across biological membranes [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. Its log P value of 3.43 indicates high lipophilicity, which is favourable for oral bioavailability according to Lipinski\u0026apos;s Rule of Five [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. In contrast, compound 12 exhibits the lowest log P (0.09) and a less negative solvation energy (-11.47 kJ/mol), suggesting poor partitioning into lipid environments and limited bioavailability. Compound 14, despite its moderate log P (2.20), shows the most negative solvation energy (-23.04 kJ/mol), implying strong solute-solvent interactions which may affect its absorption characteristics [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eFurthermore, dipole moment and polarizability values indicate each compound\u0026apos;s ability to interact with polar biological environments. Compound 12, with the highest dipole moment (3.16 Debye), might exhibit enhanced orientation-based interactions with target receptors [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. However, compound 10, despite its lower dipole moment (1.37 Debye), possesses the highest polarizability (64.95 \u0026Aring;\u0026sup3;), which could contribute to induced-dipole stabilization in hydrophobic pockets of the receptor. Integrating HOMO-LUMO trends (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) with QSAR descriptors (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) suggest that compound 10 has the most favourable pharmacokinetic and electronic profile. Its properties point to good membrane permeability, reasonable stability, and potential biological activity, positioning it as a strong candidate for lead optimization in drug design.\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComputed QSAR results of the studied compounds\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSurface area (\u0026Aring;\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e334.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e291.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e315.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolar surface area (\u0026Aring;\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVolume (\u0026Aring;\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e306.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e262.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e292.61\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSolvation energy (kJ/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-14.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-11.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-23.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLog P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePolarizability (\u0026Aring;\u0026sup3;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMass (amu)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e354.294\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e311.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e338.732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDipole Moment (Debye)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Molecular docking analysis\u003c/h2\u003e\n \u003cp\u003eMolecular docking studies were performed to evaluate the binding interactions of all ligands listed in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, with compounds 10, 12, and 14 exhibiting the most promising binding affinities. The crystal structure of the InhA enzyme (PDB ID: 4U0J) served as the target protein for the docking studies. The three-dimensional structures of the selected top hit compounds with InhA are illustrated in Figs. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The binding interactions between InhA and the test compounds (10, 12, and 14), as well as standard reference drugs, are depicted in Figs. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, respectively. Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e provides insight into the molecular interactions between ligands (10, 12, and 14) and the target protein, along with comparisons to standard anti-tubercular drugs: isoniazid, ethambutol, and pyrazinamide. Interaction analysis was performed using Discovery Studio to quantify hydrogen bonds, electrostatic and other non-covalent contacts, and hydrophobic interactions, which are fundamental in determining the binding affinity of ligands to their biological targets [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. Results presented in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, shows that compound 10 exhibits TYR158 conventional hydrogen bond and demonstrates a rich network of hydrophobic interactions with PRO193, LEU218, ILE215, and PHE149 residues. These interactions suggest a strong anchoring of the compound within the hydrophobic pocket of the protein, likely enhancing its binding stability. Additionally, electrostatic contacts with PRO156 and ALA191 residues may contribute to the orientation and proper alignment of the ligand within the active site [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eCompound 12 forms multiple hydrogen bonds, including conventional bonding with ILE194 and C-H interactions with ILE215, which indicates enhanced polar interactions compared to compound 10. It also engages with key hydrophobic residues including PHE149 and MET199. These combined interactions suggest that compound 12 is stabilized both by polar and non-polar contacts, which may enhance its binding efficiency and bioactivity [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eCompound 14 is distinctive in that it lacks conventional hydrogen bonding but compensates with multiple electrostatic interactions, notably with LYS165 and GLY192. Its extensive hydrophobic contacts spanning residues with MET199, MET147, LEU218, and PHE149 imply deep embedding into the protein\u0026rsquo;s hydrophobic cleft. The absence of hydrogen bonds might reduce specificity, but the strength of hydrophobic anchoring could offset this limitation. When compared to standard anti-TB drugs, isoniazid primarily utilizes hydrogen bonding (VAL65, GLY96) and shows limited hydrophobic interactions, which aligns with its known small, polar molecular profile. Ethambutol also demonstrates a reliance on polar contacts, especially with ILE194 and PRO156, but shows fewer hydrophobic interactions, possibly reflecting its lower lipophilicity. Pyrazinamide, on the other hand, makes similar contacts as isoniazid, with hydrogen bonding (GLY96) and weak hydrophobic contributions from residues with PHE41 and ILE95.\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eProtein-Ligand Interaction Analysis\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eHydrogen Bond\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElectrostatic and other interactions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHydrophobic interactions\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrug ID\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConventional\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC-H interactions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTYR158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRO156, ALA191, ASP148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRO193, LEU218, ILE215, PHE149, ILE21 MET199, MET155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILE194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILE215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASP148, MET147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHE149, MET199, PRO193, LEU218\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLYS165, GLY192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRO156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePRO193, MET155, LEU218, ILE215, PHE149, MET199, MET147, ALA191\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIsoniazid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVAL65, GLY96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASP64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHE41, ILE122, ILE95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEthambutol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eILE194, PRO156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePHE149, ALA157, ILE215, MET103, ILE202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePyrazinamide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGLY96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eASP64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVAL65, PHE41, ILE95 ILE122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Drug-Likeness and ADMET Profile Evaluation\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e summarizes the predicted drug-likeness properties of the studied compounds based on key physicochemical criteria. All five compounds demonstrated molecular weights (MW) below the generally accepted threshold of 500 g/mol, aligning well with Lipinski\u0026rsquo;s Rule of Five for drug-likeness [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. Notably, none of the compounds violated any of Lipinski\u0026apos;s rules, suggesting favourable oral bioavailability. Hydrogen bond acceptors ranged from 6 to 8, while none of the compounds contained hydrogen bond donors, an unusual but acceptable profile depending on the target receptor\u0026apos;s binding environment. The calculated WLOGP values ranged from 4.95 to 6.18, indicating moderate to high lipophilicity. Although higher log P values can suggest better membrane permeability, excessive lipophilicity may reduce solubility and increase toxicity [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. Despite this, all compounds achieved a bioavailability score of 0.55, which is considered acceptable for oral drugs [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, synthetic accessibility values fell between 1.89 and 2.31, indicating that these compounds are relatively easy to synthesize a favourable attribute in drug development.\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e provides insights into the pharmacokinetic and toxicity profiles of ligands (10, 12, and 14) in comparison to standard anti-tubercular agents. All three test compounds displayed high predicted human intestinal absorption values (\u0026gt;\u0026thinsp;90%), comparable to isoniazid and exceeding that of ethambutol, which had the lowest absorption (66.96%). This suggests good oral uptake prospect for the studied ligands [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. Blood\u0026ndash;brain barrier (BBB) permeability results obtained showed that, compounds 10, 12, and 14 exhibited moderate distribution values (0.32\u0026ndash;0.49), indicating a limited but notable capacity to cross the BBB. In contrast, isoniazid showed minimal permeability (0.002), and ethambutol was even less permeable (-0.22), while pyrazinamide demonstrated the highest CNS permeability (-0.20). Although CNS penetration is not the primary goal for anti-tubercular agents, moderate permeability may enhance treatment of TB infections in sanctuary sites like the brain [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. None of the compounds were identified as substrates or inhibitors of key cytochrome P450 isoforms (CYP2D6 and CYP3A4), except for compound 12, which was flagged as a potential substrate for CYP3A4. This may imply a higher risk of drug-drug interactions and metabolic instability for compound 12 than others [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eAs for excretion, total clearance rates varied, with compound 10 showing the highest clearance (0.77 mL/min/kg) among the tested ligands, suggesting a faster elimination profile. None of the ligands were identified as substrates for the renal OCT2 transporter, suggesting a lower risk of renal toxicity. Toxicological predictions showed no AMES mutagenicity or skin sensitization for any of the test compounds. However, ethambutol was flagged for potential skin sensitization, indicating a less favourable toxicity profile compared to the studied molecules.\u003c/p\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAnticipated Drug-like properties of the studied compounds\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDrug ID\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMW\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eH-bond acceptors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eH-bond donors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWLOGP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLipinski violations\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBioavailability Score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSynthetic Accessibility\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e293.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e354.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e354.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e311.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e338.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eADMET results of ligands and reference drugs\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eADMET Parameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIsoniazid\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEthambutol\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePyrazinamide\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntestinal absorption (human)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e84.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBBB permeability (distribution)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCNS permeability (distribution)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCYP2D6 substrate (metabolism)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCYP3A4 substrate (metabolism)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCYP2D6 inhibitor (metabolism)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCYP3A4 inhibitor (metabolism)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Clearance (excretion)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRenal OCT2 substrate (excretion)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAMES toxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkin Sensitisation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Molecular dynamics simulation\u003c/h2\u003e\n \u003cp\u003eBased on the robust binding affinities identified through computational docking studies, compounds 10, 12, and 14 were selected as the most promising ligands for subsequent molecular dynamics (MD) simulations. A 250-nanosecond MD simulation was performed to access the temporal dynamic stability and conformational behaviour of the protein-ligand complexes, structural stability, root mean square deviation (RMSD) and root mean square fluctuation (RMSF) analyses were conducted\u003c/p\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.1 Binding Energy Analysis\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e presents the binding free energy results derived from Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) calculations for the three protein\u0026ndash;ligand complexes: 10_4U0J, 12_4U0J, and 14_4U0J. Among these, Complex 14 exhibited the most favourable total binding free energy (-70.08 kcal/mol), indicating stronger ligand-protein stability compared to Complexes 10 (-57.38 kcal/mol) and 12 (-59.78 kcal/mol). These values reflect the cumulative contributions of van der Waals forces, electrostatics, solvation effects, and lipophilic interactions [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. A breakdown of the energy components shows that van der Waals interactions were dominant in all three complexes, particularly in Complex 14 (-51.62 kcal/mol), suggesting strong non-polar interactions within the binding pocket. The lipophilic contribution also supported binding stability, with Complex 14 again leading at -21.32 kcal/mol. These findings highlight the role of hydrophobic forces in stabilizing the ligand within the target site [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. Although covalent and hydrogen bonding energies were relatively minor across all complexes, they still provided subtle contributions to binding specificity. For instance, the covalent binding component was slightly higher in Complex 14 (1.53 kcal/mol), while hydrogen bonding energies remained negligible in all cases, consistent with docking data indicating limited polar contacts.\u003c/p\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMD Simulation results of Binding Energies of the studied complexes\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComplex 10\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComplex 12\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComplex 14\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMGBSA dG Bind\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-57.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-59.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-70.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMGBSA dG Bind Coulomb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-17.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-9.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-13.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMGBSA dG Bind Covalent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMGBSA dG Bind Hbond\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMGBSA dG Bind Lipo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-15.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-20.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-21.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMGBSA dG Bind Packing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.0015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMGBSA dG Bind Solv GB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMMGBSA dG Bind vdW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-40.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-36.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-51.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.2 Protein RMSD and RMSF\u003c/h2\u003e\n \u003cp\u003eFigures \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e through 11 illustrate dynamic behaviours and interaction patterns throughout the 250 ns simulation for the studied complexes. Molecular dynamics (MD) simulations were carried out for the top-performing complexes (10_4U0J, 12_4U0J, and 14_4U0J). Each protein\u0026ndash;ligand complex was simulated to evaluate their dynamic behaviour, specifically through RMSD and RMSF analyses (Figs. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA\u0026ndash;D and \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA\u0026ndash;D). In the 10_4U0J complex, the protein\u0026rsquo;s RMSD peaked at approximately 2.6 \u0026Aring; around 120 ns, after which it gradually stabilized at 2.4 \u0026Aring;. The ligand exhibited initial fluctuations, reaching a peak RMSD of 5.4 \u0026Aring;, and later settled to a more stable conformation at 4.8 \u0026Aring; by 180 ns (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA). For the 12_4U0J complex, the protein backbone deviation reached a maximum of 2.7 \u0026Aring; at 80 ns, then a gradual decline and stabilization at 2.4 \u0026Aring; when the simulation end. The ligand showed early fluctuations, peaking at 4.8 \u0026Aring; at 180 ns, before settling at around 4.2 \u0026Aring; (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eB). In the 14_4U0J complex, the protein RMSD peaked at 3.2 \u0026Aring; at 150 ns and eventually stabilized at 2.8 \u0026Aring;. The ligand in this complex also showed notable fluctuations in the early phase, reaching 4.8 \u0026Aring;, before achieving a stable conformation of 4.2 \u0026Aring; at 250 ns (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eC).\u003c/p\u003e\n \u003cp\u003eThe root Mean square deviation (RMSD) plots for the simulated protein\u0026ndash;ligand complexes are presented in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e (A-D). Fluctuations in RMSD values during molecular dynamics trajectories typically reflect conformational adjustments within the protein\u0026ndash;ligand system. Furthermore, RMSD data provide insight into the overall structural stability, root mean square fluctuation (RMSF) values highlight residue-level movements that can influence the compactness and flexibility of the complex structure (Figs. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e) [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eOf the three complexes analysed, Complex 10_4U0J exhibited the most stable behaviour throughout the simulation period. This complex demonstrated fewer deviations compared to Complexes 12_4U0J and 14_4U0J. Specifically, in Complex 10_4U0J, the RMSD increased moderately from 1.0 \u0026Aring; to 1.5 \u0026Aring; during the initial 160 ns, followed by a gradual stabilization around an average of 3.5 \u0026Aring; after 200 ns (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eA). In contrast, Complex 12_4U0J displayed more pronounced early fluctuations, with RMSD values oscillating between 1.2 \u0026Aring; and 3.0 \u0026Aring; within the first 100 ns before stabilizing at an average of 3.6 \u0026Aring; near the 200 ns region (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eB). The 14_4U0J complex exhibited the widest fluctuations across the entire trajectory, with deviations ranging from 0.6 \u0026Aring; to 3.0 \u0026Aring; and reaching a relatively stable plateau around 5.0 \u0026Aring; (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003eC).\u003c/p\u003e\n \u003cp\u003eThese RMSD profiles confirm that all three ligands maintained reasonable structural stability with the 4U0J protein, though Complex 10_4U0J demonstrated superior consistency. RMSF analysis supported this observation, with Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003eA showing that Compound 10 induced fewer positional shifts in the protein residues, indicating a more stable binding mode.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.3 Protein-ligand interactions\u003c/h2\u003e\n \u003cp\u003eBased on molecular docking outcomes revealing favourable binding affinities, compounds 10, 12, and 14 were selected for further analysis of their interaction profiles with the InhA protein (PDB ID: 4U0J), focusing on hydrogen bonds, hydrophobic interactions, water bridges, and ionic contacts, as illustrated in Figs. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003eA\u0026ndash;C. Hydrogen bonding critical for specificity and strength of ligand binding, due to their significance in drug design. For ligand 10, hydrogen bond interactions with TYR-158 and GLY-104 residues. Ligand 12 formed hydrogen bonds with TYR-158, ILE-194, and THR-196, while ligand 14 bond with LYS-165 and ILE-194 residues.\u003c/p\u003e\n \u003cp\u003eRegarding hydrophobic contacts, ligand 10 established interactions with a broad array of nonpolar residues, including ILE-21, MET-103, PHE-149, MET-155, TYR-158, ALA-191, PRO-193, ILE-194, LEU-197, MET-199, ILE-202, and ILE-215. Ligand 12 engaged hydrophobically with residues including ILE-21, MET-103, MET-147, PHE-149, TYR-158, ALA-191, ALA-198, MET-199, ILE-215, and LEU-218. The most extensive hydrophobic interactions were recorded for ligand 14, which bound with ILE-21, MET-147, PHE-149, MET-155, TYR-158, MET-161, LYS-165, ALA-191, PRO-193, ILE-194, ALA-198, MET-199, ILE-202, ILE-215, LEU-218, and MET-232.\u003c/p\u003e\n \u003cp\u003eWater bridge interactions, which often contribute to the stabilization of ligand\u0026ndash;receptor complexes, were also observed. Ligand 10 formed water-mediated interactions with GLN-100, ASP-140, ALA-157, TYR-158, LYS-165, and THR-196. Ligand 12 interacted via water bridges with ASP-148, LYS-165, GLY-192, ILE-194, THR-196, LEU-197, ALA-198, MET-199, and GLU-219. For ligand 14, water bridges involved ILE-95, MET-147, and LYS-165. Notably, none of the ligands exhibited ionic contacts during their binding with the protein\u0026rsquo;s active site residues.\u003c/p\u003e\n \u003cp\u003eTo evaluate the time-dependent stability of the observed interactions, protein-ligand contact frequencies across the simulation trajectory were quantitatively evaluated and visualized in Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eA\u0026ndash;C. The contact counts for all three complexes fluctuated between 0 and 6, indicating dynamic but sustained binding throughout the 250 ns simulation. Moreover, the contact maps revealed multiple deep and continuous interaction bands, suggesting robust and stable associations between each ligand and a range of amino acid residues.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.4 Ligands properties analysis\u003c/h2\u003e\n \u003cp\u003eThe structural and dynamic behaviours of the ligands were assessed by analysing several key properties, including ligand root mean square deviation (RMSD), molecular surface area (MolSA), radius of gyration (rGyr), polar surface area (PSA), and solvent-accessible surface area (SASA). The results are illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eA\u0026ndash;C. For all three complexes, 10_4U0J, 12_4U0J, and 14_4U0J. Ligand RMSD values showed initial fluctuations during the first 10 nanoseconds, after which they gradually stabilized. RMSD values ranged between approximately 0.6 \u0026Aring; and 1.8 \u0026Aring;, with equilibrium values reaching 1.9 \u0026Aring; for complexes 10_4U0J and 14_4U0J, and 1.5 \u0026Aring; for complex 12_4U0J (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eA-C).\u003c/p\u003e\n \u003cp\u003eThe radius of gyration (rGyr), which reflects the compactness of a ligand\u0026rsquo;s structure, also displayed minimal fluctuations before stabilizing. The rGyr values spanned from 4.25\u0026ndash;5.0 \u0026Aring; for 10_4U0J (equilibrating at 5.2 \u0026Aring;), 3.6\u0026ndash;4.4 \u0026Aring; for 12_4U0J (with final equilibrium at 4.4 \u0026Aring;), and 3.6\u0026ndash;4.5 \u0026Aring; for 14_4U0J (equilibrating at 4.8 \u0026Aring;).\u003c/p\u003e\n \u003cp\u003eMolSA values remained relatively steady, reflecting minor changes in the overall molecular surface. Specifically, MolSA values ranged from 290 to 300 \u0026Aring;\u0026sup2;, stabilizing at 304 \u0026Aring;\u0026sup2; for ligand 10; 252 to 270 \u0026Aring;\u0026sup2;, stabilizing around 275 \u0026Aring;\u0026sup2; for ligand 12; and 264 to 288 \u0026Aring;\u0026sup2;, reaching 290 \u0026Aring;\u0026sup2; at equilibrium for ligand 14 (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003eA\u0026ndash;C). furthermore, SASA measurements showed initial variability followed by stabilization, indicating steady solvent exposure over time. SASA values for 10_4U0J ranged from 0 to 45 \u0026Aring;\u0026sup2;, equilibrating at 50 \u0026Aring;\u0026sup2;. For 12_4U0J, the values ranged up to 90 \u0026Aring;\u0026sup2;, with equilibrium at 92 \u0026Aring;\u0026sup2;, while 14_4U0J showed a similar pattern, equilibrating at 47 \u0026Aring;\u0026sup2;. Lastly, the polar surface area (PSA), which reflects the prospect for hydrogen bonding and solubility, stabilized across all complexes after initial adjustments. PSA values ranged between 30 and 48 \u0026Aring;\u0026sup2;, stabilizing at 50 \u0026Aring;\u0026sup2; for ligand 10; between 0 and 42 \u0026Aring;\u0026sup2;, with final equilibrium at 45 \u0026Aring;\u0026sup2; for ligand 12; and between 0 and 56 \u0026Aring;\u0026sup2;, reaching 60 \u0026Aring;\u0026sup2; for ligand 14. Thus, the ligand property profiles across the simulation demonstrated only mild fluctuations during the early stages, followed by consistent equilibrium, indicating that each ligand remained stably bound within the protein\u0026rsquo;s active site throughout the 250 ns simulation.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.5 Principal Component Analysis (PCA)\u003c/h2\u003e\n \u003cp\u003eTo investigate structural flexibility, PCA was applied to the C\u0026alpha; atoms of both the unbound (apo) protein and the protein-ligand complexes. The goal was to investigate collective and correlated atomic motions that occur before and after ligand interaction, utilizing eigenvectors derived from the covariance matrix. This method was validated by analysing the corresponding eigenvalues magnitudes, with higher values signifying pronounced collective motions linked to the protein\u0026rsquo;s functional dynamics [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe significance of each eigenvalue was ranked based on the proportion of variance it contributed throughout the 250 ns molecular dynamics simulation, as illustrated in Fig.\u0026nbsp;11. In Fig.\u0026nbsp;11A, the apo protein\u0026apos;s first principal component (PC1) accounted for 29.3% of the overall motion, while the second and third components each contributed 12.3%. When examining the transition from the apo form to the complex with ligand 12 (Fig.\u0026nbsp;11B), PC1 variance dropped to 18.77%, with PC2 and PC3 both at 11.24%. These lower values in the apo form suggest that its C\u0026alpha; atoms exhibit slower conformational dynamics compared to their counterparts in the ligand-bound complex.\u003c/p\u003e\n \u003cp\u003eFor ligand 14, as seen in Fig.\u0026nbsp;11C, the apo protein showed a variance of 35.82% in PC1, and 13.18% in both PC2 and PC3. This higher variance in the apo structure\u0026rsquo;s major components implies greater flexibility and mobility of its backbone atoms than the ligand-bound form.\u003c/p\u003e\n \u003cp\u003eTo further explore the dynamics of protein-ligand interactions, we generated two-dimensional projection plots using the first two principal components (PC1 and PC2). These 2D graphs, depicted in Fig.\u0026nbsp;11A, illustrate the conformational sampling of protein-ligand complexes with ligands 10, 12, and 14. In this visualization, stable complexes are identified by a smaller occupied phase space, while unstable complexes cover a larger area.\u003c/p\u003e\n \u003cp\u003eThe simulation data across the three systems revealed that protein complexes with ligands 12 and 14 occupied more confined phase spaces, suggesting higher conformational stability. In contrast, the complex with ligand 10 demonstrated a broader phase space, indicative of lower stability. The observed trends aligned with established analytical metrics (RMSD, RMSF, and radius of gyration (Rg)), reinforcing the reliability and robustness of the structural dynamics\u0026rsquo; interpretation.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\n \u003ch2\u003e3.5.6 Hydrogen-Bond Dynamics during Molecular Simulations\u003c/h2\u003e\n \u003cp\u003eKey hydrogen-bonding interactions observed in MD simulations for ligands 10, 12, and 14 are compiled in Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e. These bonds play a crucial role in maintaining ligand positioning and preserving structural coherence within the protein\u0026rsquo;s active site during simulations [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]. Ligand 10, exhibited a single H-bond between its hydroxyl (-OH) group and TYR158, with a donor-acceptor distance of 2.71 \u0026Aring; and an interaction energy of -1.1 kcal/mol. This This measurement aligns with the optimal favourable range for hydrogen bonding (typically 2.5\u0026ndash;3.5 \u0026Aring;), indicating a moderately strong interaction [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eFor ligand 12, formed a hydrogen bond via its nitrogen atom with the backbone of ILE194 (3.25 \u0026Aring;, -0.7 kcal/mol). Although this bond is slightly weaker compared to ligand 10, it still indicates a stabilizing effect, especially if maintained consistently throughout the simulation. Weak hydrogen bonds of this nature can still significantly influence ligand orientation and protein-ligand affinity when combined with other non-covalent interactions [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eLigand 14 demonstrated a bidirectional hydrogen-bonding mechanism, where both the ligand\u0026rsquo;s oxygen atom and the side chain nitrogen of ILE194 acted as donors and acceptors. This dual interaction included bonding with ILE95 and ILE194, exhibiting distances of 3.25 \u0026Aring; and 3.11 \u0026Aring;, respectively, and interaction energies of \u0026minus;\u0026thinsp;0.4 kcal/mol and \u0026minus;\u0026thinsp;3.4 kcal/mol. Notably, the second interaction, with a more negative energy value, indicates a relatively stronger hydrogen bond, contributing significantly to the overall stabilization of the complex. This suggests that ligand 14 forms a more robust hydrogen bonding network. These interactions are critical for ligand retention and activity, as they often play a dominant role in the specificity and strength of molecular recognition. Among the three ligands, ligand 14 displayed the most favourable hydrogen bonding profile, both in terms of bond strength and multiplicity, which correlate with its higher binding stability, as also indicated by the results from PCA and RMSD analyses.\u003c/p\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of Hydrogen bond interactions during MD simulation of complexes 10, 12, 14\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eInteractions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDistance\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eE(kcal/mol)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLigand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDonor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAcceptor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOH TYR 158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eN ILE 194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Structure based design\u003c/h2\u003e\n \u003cp\u003eStructure-based drug design (SBDD) is a rational and iterative approach that leverages detailed knowledge of the three-dimensional structure of a target protein to design or optimize bioactive compounds with improved affinity, specificity, and pharmacokinetic properties [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]. By integrating molecular docking, dynamic simulations, and binding free energy analyses, SBDD facilitates the identification of lead molecules capable of targeting key functional residues within a protein\u0026rsquo;s active site.\u003c/p\u003e\n \u003cp\u003eIn this investigation, compound 14 emerged as a particularly promising candidate due to its superior MolDock score and favourable interaction profile, as confirmed through integrated computational assessments (docking/MD simulations) analyses. Notably, compound 14 established multiple synergistic stabilization via hydrogen bonds and hydrophobic forces, which contributed to its high binding energy. Such findings align with earlier findings underscoring the strength and multiplicity of such interactions are key determinants of ligand efficacy [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]. Beyond its binding performance, compound 14 also exhibited a non-toxic profile, as predicted by in silico ADMET screening tools. This absence of toxicity is a critical consideration in early-stage drug development, where compounds with promising bioactivity often fail due to adverse pharmacological properties [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]. The dual advantage of strong binding and low toxicity made compound 14 as an ideal scaffold for analogue development.\u003c/p\u003e\n \u003cp\u003eUsing compound 14 as a structural scaffold, two optimized derivatives of compound 14 were designed to augment target engagement, refine pharmacokinetics, and mitigate resistance or off-target effects. This approach aligns with the core principles of structure-based design, where modification of functional groups or introduction of specific substituents can fine-tune the physicochemical characteristics of the lead compound [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]. The newly generated compounds were subjected to docking and in silico screening to evaluate their potential as improved analogues of the parent molecule. The selection of compound 14 as a lead template exemplifies how a systematic structure-based approach can drive the discovery and refinement of novel therapeutic candidates, paving the way for further preclinical validation and optimization.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7 Docking Results of design compounds\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e presents the molecular docking outcomes for two newly designed compounds evaluated using the MolDock scoring function and the Re-Rank scoring system. The MolDock scores, which represent the estimated ligand-target binding affinity, were \u0026minus;\u0026thinsp;132.579 kcal/mol and \u0026minus;\u0026thinsp;125.894 kcal/mol for compounds 1 and 2, respectively. Correspondingly, the Re-Rank scores, which provide a refined estimate by considering additional energy terms such as steric clashes and hydrogen bonding contributions, were \u0026minus;\u0026thinsp;95.309 kcal/mol and \u0026minus;\u0026thinsp;92.140 kcal/mol.\u003c/p\u003e\n \u003cp\u003eA more negative MolDock or Re-Rank score suggests a stronger and more favourable interaction between the ligand-protein [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]. In this context, compound 1 exhibited superior docking performance, indicating a more stable and potentially more efficacious interaction with the receptor than compound 2. This stronger interaction could be attributed to better geometric complementarity or a higher number of favourable interactions (such as hydrogen bonds, hydrophobic contacts) between compound 1 and the residues of the target protein. Furthermore, the docking performance of these novel compounds can be contextualized by comparing them to reference inhibitors or co-crystallized ligands from previous studies. Compounds with MolDock scores more negative than \u0026minus;\u0026thinsp;120 kcal/mol are empirically linked to high-affinity binding in prior SBDD campaigns [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]. Both compounds in this study surpass this threshold, indicating their strong potential as inhibitors or modulators of the target protein.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003eTable 9: Docking results of the newly designed compounds\u003c/div\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"746\" height=\"316\"\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\n \u003ch2\u003e3.8 Drug-Likeness and ADMET Evaluation of design compounds\u003c/h2\u003e\n \u003cp\u003eEarly-phase drug discovery relies heavily on \u003cem\u003ein silico\u003c/em\u003e evaluation of pharmacokinetic and safety profiles to prioritize candidates with optimal drug-like properties. This approach reduces the cost and time associated with experimental testing and helps to eliminate candidates with suboptimal properties [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e presents the drug-likeness characteristics of the newly designed compounds. Both compounds demonstrated compliance with Lipinski\u0026rsquo;s Rule of Five, a benchmark for predicting oral bioavailability [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e]. Compound 1 had one violation, with a WLOGP (octanol-water partition coefficient) of 6.51, slightly exceeding the recommended threshold of 5, indicating a higher degree of lipophilicity. In contrast, compound 2 fully complied with all Lipinski criteria, including molecular weight, hydrogen bond donors/acceptors, and logP. Both molecules demonstrated moderate oral bioavailability potential (bioavailability score: 0.55) and favourable synthetic feasibility (SA scores: 2.59 and 2.39), with lower scores reflecting simpler synthesis pathways [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e summarizes the predicted ADMET properties of the two designed compounds. Both compounds exhibited high human intestinal absorption, with values of 88.45% for compound 1 and 93.51% for compound 2. These values suggest that the compounds are likely to be efficiently absorbed through the gastrointestinal tract when administered orally.\u003c/p\u003e\n \u003cp\u003eIn terms of distribution, both compounds demonstrated moderate to low blood-brain barrier (BBB) permeability, with compound 1 showing a value of 0.35 and compound 2 a significantly lower value of 0.05. Correspondingly, both compounds showed negative CNS permeability values, with \u0026minus;\u0026thinsp;2.84 for compound 1 and \u0026minus;\u0026thinsp;2.32 for compound 2, placing them outside the CNS-permeable range, which is consistent with limited CNS exposure [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]. In the metabolism domain, both compounds were non-substrates and non-inhibitors of CYP2D6, minimizing potential for drug\u0026ndash;drug interactions involving this enzyme. However, both were predicted to be substrates of CYP3A4, which is responsible for the metabolism of a large proportion of clinically used drugs. Fortunately, neither compound inhibited CYP3A4, reducing concerns about CYP3A4-related metabolic interference.\u003c/p\u003e\n \u003cp\u003eFor excretion, the compounds displayed comparable total clearance values (0.82 and 0.84 mL/min/kg), and neither was identified as a renal OCT2 substrate, suggesting elimination through non-OCT2-dependent pathways. Importantly, both compounds were non-toxic based on AMES mutagenicity and skin sensitization predictions. The absence of genotoxicity and dermal reactivity enhances the safety profile of these molecules, supporting their advancement to further stages of development [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e\n \u003ctable id=\"Tab10\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDrug-likeness characteristics results of the designed compounds\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS/N\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMW\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eH-bond acceptors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eH-bond donors\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eWLOGP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLipinski violations\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBioavailability Score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSynthetic Accessibility\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e419.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e359.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003ctable id=\"Tab11\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of ADMET results of designed compounds\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eADMET Parameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntestinal absorption (human)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBBB permeability (distribution)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCNS permeability (distribution)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCYP2D6 substrate (metabolism)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCYP3A4 substrate (metabolism)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCYP2D6 inhibitor (metabolism)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCYP3A4 inhibitor (metabolism)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Clearance (excretion)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRenal OCT2 substrate (excretion)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAMES toxicity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkin Sensitisation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"4 Conclusion","content":"\u003cp\u003eThis study employed an integrated computational strategy to identify promising inhibitors of the \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e InhA enzyme, a validated drug target involved in mycolic acid synthesis. Through molecular docking, quantum chemical calculations, pharmacokinetic profiling, and 250 ns molecular dynamics simulations, compounds 10, 12, and 14 emerged as potent candidates with strong binding affinity, structural stability, and favourable ADMET properties. Among them, compound 14 stood out for its superior binding energy and interaction profile, making it a suitable lead for structural optimization. Two novel analogues derived from this scaffold demonstrated even greater docking performance and maintained acceptable drug-likeness and safety characteristics. These findings support the utility of structure-based design in anti-tubercular drug discovery and pave the way for experimental validation and further optimization of the proposed compounds as potential therapeutic agents.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work received no external funding\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship contribution:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTAN: Writing, review \u0026amp; editing, Writing original draft, Conceptualization; GAS: Supervision, Methodology, review \u0026amp; editing; AU: Supervision, Review \u0026amp; editing, Methodology; ABU: Supervision, Review \u0026amp; editing, Methodology; MTI: Supervision, Software, Methodology; MA: Software, Methodology; HAM: Review \u0026amp; editing; AH: Review \u0026amp; editing; JB: Review \u0026amp; editing. All the authors review and accept the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no known competing financial interests\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll relevant data from this study are included in the content of this published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eB. H. Gulumbe, A. Abdulrahim, S. K. Ahmad, K. A. Lawan, and M. B. Danlami, \u0026ldquo;WHO report signals tuberculosis resurgence: Addressing systemic failures and revamping control strategies,\u0026rdquo; \u003cem\u003eDecoding Infection and Transmission\u003c/em\u003e, vol. 3, p. 100044, 2025, doi: 10.1016/j.dcit.2025.100044.\u003c/li\u003e\n\u003cli\u003eA. Diab, H. Dickerson, and O. Al Musaimi, \u0026ldquo;Targeting the Heart of Mycobacterium: Advances in Anti-Tubercular Agents Disrupting Cell Wall Biosynthesis,\u0026rdquo; \u003cem\u003ePharmaceuticals\u003c/em\u003e, vol. 18, no. 1, p. 70, Jan. 2025, doi: 10.3390/ph18010070.\u003c/li\u003e\n\u003cli\u003eS. Maddipatla, P. K. Agnivesh, B. Bakchi, S. Nanduri, N. P. Kalia, and V. M. Yaddanapudi, \u0026ldquo;New pyrazole-based derivatives targeting MmpL3 transporter in Mycobacterium tuberculosis: design, synthesis, biological evaluation and molecular docking studies,\u0026rdquo; \u003cem\u003eMol Divers\u003c/em\u003e, Mar. 2025, doi: 10.1007/s11030-025-11152-3.\u003c/li\u003e\n\u003cli\u003eM. T. Stevens, P. M. E. Hawkins, T. Wang, R. J. Payne, and W. J. Britton, \u0026ldquo;Analogue of the natural product ecumicin causes sustained growth inhibition of Mycobacterium tuberculosis under multiple growth conditions,\u0026rdquo; \u003cem\u003eTuberculosis\u003c/em\u003e, vol. 151, p. 102594, Mar. 2025, doi: 10.1016/j.tube.2024.102594.\u003c/li\u003e\n\u003cli\u003eV. N. Nono \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Prevalence of katG and inhA mutations associated with isoniazid resistance in Mycobacterium tuberculosis clinical isolates in Cameroon,\u0026rdquo; \u003cem\u003eBMC Microbiol\u003c/em\u003e, vol. 25, no. 1, p. 127, Mar. 2025, doi: 10.1186/s12866-025-03816-9.\u003c/li\u003e\n\u003cli\u003eV. S. Sukhachev \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Assessment of the Efficiency of Selecting Promising Compounds During Virtual Screening Based on Various Estimations of Drug-Likeness,\u0026rdquo; \u003cem\u003ePharm Chem J\u003c/em\u003e, vol. 58, no. 9, pp. 1388\u0026ndash;1396, Dec. 2024, doi: 10.1007/s11094-025-03286-3.\u003c/li\u003e\n\u003cli\u003eP. Xie, X. Wang, Y. Deng, C. Zhang, Y. Zhang, and L. Huang, \u0026ldquo;Deciphering non-covalent binding mechanism of olive biophenolic secoiridoids interaction with \u0026beta;-lactoglobulin: Multi-spectroscopies, thermodynamics, molecular docking, and molecular dynamics simulation,\u0026rdquo; \u003cem\u003eFood Hydrocoll\u003c/em\u003e, vol. 162, p. 110897, May 2025, doi: 10.1016/j.foodhyd.2024.110897.\u003c/li\u003e\n\u003cli\u003eK. Tabti, A. Sbai, H. Maghat, T. Lakhlifi, and M. Bouachrine, \u0026ldquo;Computational assessment of the reactivity and pharmaceutical potential of novel triazole derivatives: An approach combining DFT calculations, molecular dynamics simulations, and molecular docking,\u0026rdquo; \u003cem\u003eArabian Journal of Chemistry\u003c/em\u003e, vol. 17, no. 1, Jan. 2024, doi: 10.1016/j.arabjc.2023.105376.\u003c/li\u003e\n\u003cli\u003eM. A. Alamri \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Development of newer generation Vascular endothelial growth factor Receptor-2 Inhibitors: Pharmacophore based design, virtual Screening, molecular Docking, molecular dynamic Simulation, and DFT analyses,\u0026rdquo; \u003cem\u003eJ King Saud Univ Sci\u003c/em\u003e, vol. 36, no. 8, p. 103285, Sep. 2024, doi: 10.1016/j.jksus.2024.103285.\u003c/li\u003e\n\u003cli\u003eA. Moulishankar and T. Sundarrajan, \u0026ldquo;Pharmacophore, QSAR, molecular docking, molecular dynamics and ADMET study of trisubstituted benzimidazole derivatives as potent anti-tubercular agents,\u0026rdquo; \u003cem\u003eChemical Physics Impact\u003c/em\u003e, vol. 8, p. 100512, Jun. 2024, doi: 10.1016/j.chphi.2024.100512.\u003c/li\u003e\n\u003cli\u003eF. A. Ugbe \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Computational evaluation of the inhibitory potential of some urea, thiourea, and selenourea derivatives of diselenides against leishmaniasis: 2D-QSAR, pharmacokinetics, molecular docking, and molecular dynamics simulation,\u0026rdquo; \u003cem\u003eJ Mol Struct\u003c/em\u003e, vol. 1302, Apr. 2024, doi: 10.1016/j.molstruc.2023.137473.\u003c/li\u003e\n\u003cli\u003eS. H. Abdullahi \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Molecular docking studies of some benzoxazole and benzothiazole derivatives as VEGFR-2 target inhibitors: In silico design, MD simulation, pharmacokinetics and DFT studies,\u0026rdquo; \u003cem\u003eIntelligent Pharmacy\u003c/em\u003e, vol. 2, no. 2, pp. 232\u0026ndash;250, Apr. 2024, doi: 10.1016/j.ipha.2023.11.010.\u003c/li\u003e\n\u003cli\u003eM. Abdalla \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;In silico studies on phytochemicals to combat the emerging COVID-19 infection,\u0026rdquo; \u003cem\u003eJournal of Saudi Chemical Society\u003c/em\u003e, vol. 25, no. 12, p. 101367, Dec. 2021, doi: 10.1016/j.jscs.2021.101367.\u003c/li\u003e\n\u003cli\u003eM. Abdalla, A. Khalid, J. Hedayati, and M. N. Ghayur, \u0026ldquo;Cholinesterase Inhibitory Activity of Paeoniflorin: Molecular Dynamics Simulation, and In Vitro Mechanistic Investigation,\u0026rdquo; \u003cem\u003eBiochem Res Int\u003c/em\u003e, vol. 2024, no. 1, Jan. 2024, doi: 10.1155/bri/9192496.\u003c/li\u003e\n\u003cli\u003eD. Wu \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;The stability of the SARS-COV-2 structure in the presence of variable external heat flux in the vicinity of the water/silver nanofluid: A molecular dynamics simulation,\u0026rdquo; \u003cem\u003eCase Studies in Thermal Engineering\u003c/em\u003e, vol. 69, p. 105994, May 2025, doi: 10.1016/j.csite.2025.105994.\u003c/li\u003e\n\u003cli\u003eM. El fadili \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;In silico insights into the design of novel NR2B-selective NMDA receptor antagonists: QSAR modeling, ADME-toxicity predictions, molecular docking, and molecular dynamics investigations,\u0026rdquo; \u003cem\u003eBMC Chem\u003c/em\u003e, vol. 18, no. 1, Dec. 2024, doi: 10.1186/s13065-024-01248-6.\u003c/li\u003e\n\u003cli\u003eK. Bhattacharya, N. R. Chanu, P. Khanal, A. Bhattacharjee, M. Chakraborty, and D. Das, \u0026ldquo;Etonogestrel and Alzheimer\u0026rsquo;s: A multi-scale computational approach to acetylcholinesterase inhibition,\u0026rdquo; \u003cem\u003eNext Materials\u003c/em\u003e, vol. 8, p. 100711, Jul. 2025, doi: 10.1016/j.nxmate.2025.100711.\u003c/li\u003e\n\u003cli\u003eA. Daina, O. Michielin, and V. Zoete, \u0026ldquo;SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules,\u0026rdquo; \u003cem\u003eSci Rep\u003c/em\u003e, vol. 7, no. 1, p. 42717, Mar. 2017, doi: 10.1038/srep42717.\u003c/li\u003e\n\u003cli\u003eS. R. Mahapatra \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;RETRACTED: The potential of plant-derived secondary metabolites as novel drug candidates against Klebsiella pneumoniae: Molecular docking and simulation investigation,\u0026rdquo; \u003cem\u003eSouth African Journal of Botany\u003c/em\u003e, vol. 149, pp. 789\u0026ndash;797, Sep. 2022, doi: 10.1016/j.sajb.2022.04.043.\u003c/li\u003e\n\u003cli\u003eC. A. Lipinski, F. Lombardo, B. W. Dominy, and P. J. Feeney, \u0026ldquo;Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings,\u0026rdquo; \u003cem\u003eAdv Drug Deliv Rev\u003c/em\u003e, vol. 64, pp. 4\u0026ndash;17, Dec. 2012, doi: 10.1016/j.addr.2012.09.019.\u003c/li\u003e\n\u003cli\u003eD. Vengadesan, N. Arumugam, E. Manikandan, and A. Mandal, \u0026ldquo;Evaluation of Nanomaterials as Effective Carriers Targeted to Traverse Blood\u0026ndash;Brain Barrier for Theragnostics of Neurodegenerative Diseases: An Overview,\u0026rdquo; \u003cem\u003eBionanoscience\u003c/em\u003e, vol. 15, no. 1, p. 143, Mar. 2025, doi: 10.1007/s12668-024-01773-7.\u003c/li\u003e\n\u003cli\u003eD. E. V. Pires, T. L. Blundell, and D. B. Ascher, \u0026ldquo;pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures,\u0026rdquo; \u003cem\u003eJ Med Chem\u003c/em\u003e, vol. 58, no. 9, pp. 4066\u0026ndash;4072, May 2015, doi: 10.1021/acs.jmedchem.5b00104.\u003c/li\u003e\n\u003cli\u003eS. Choudhari, S. K. Patil, and S. Rathod, \u0026ldquo;Identification of hits as anti-obesity agents against human pancreatic lipase \u003cem\u003evia\u003c/em\u003e docking, drug-likeness, \u003cem\u003ein-silico\u003c/em\u003e ADME(T), pharmacophore, DFT, molecular dynamics, and MM/PB(GB)SA analysis,\u0026rdquo; \u003cem\u003eJ Biomol Struct Dyn\u003c/em\u003e, vol. 42, no. 20, pp. 10688\u0026ndash;10710, Dec. 2024, doi: 10.1080/07391102.2023.2258407.\u003c/li\u003e\n\u003cli\u003eM. K. Usha \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Synthesis, crystal structure, Hirshfeld surface analysis, energy frameworks, and DFT calculations of (2 \u003cem\u003ee\u003c/em\u003e )-3-(dimethylamino)-1-[5-methyl-1-(4-nitrophenyl)-1 \u003cem\u003eh\u003c/em\u003e -1,2,3-triazol-4-yl]prop-2-en-1-one,\u0026rdquo; \u003cem\u003eMolecular Crystals and Liquid Crystals\u003c/em\u003e, vol. 768, no. 6, pp. 11\u0026ndash;26, Apr. 2024, doi: 10.1080/15421406.2024.2326337.\u003c/li\u003e\n\u003cli\u003eM. K. Usha \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Synthesis, crystal structure, Hirshfeld surface analysis, energy frameworks, and DFT calculations of (2 \u003cem\u003ee\u003c/em\u003e )-3-(dimethylamino)-1-[5-methyl-1-(4-nitrophenyl)-1 \u003cem\u003eh\u003c/em\u003e -1,2,3-triazol-4-yl]prop-2-en-1-one,\u0026rdquo; \u003cem\u003eMolecular Crystals and Liquid Crystals\u003c/em\u003e, vol. 768, no. 6, pp. 11\u0026ndash;26, Apr. 2024, doi: 10.1080/15421406.2024.2326337.\u003c/li\u003e\n\u003cli\u003eJ. Shaikh \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Pharmacophore mapping, 3D QSAR, molecular docking, and ADME prediction studies of novel Benzothiazinone derivatives,\u0026rdquo; \u003cem\u003eIn Silico Pharmacol\u003c/em\u003e, vol. 12, no. 2, p. 79, Aug. 2024, doi: 10.1007/s40203-024-00255-8.\u003c/li\u003e\n\u003cli\u003eD. Smith \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Passive Lipoidal Diffusion and Carrier-Mediated Cell Uptake Are Both Important Mechanisms of Membrane Permeation in Drug Disposition,\u0026rdquo; \u003cem\u003eMol Pharm\u003c/em\u003e, vol. 11, no. 6, pp. 1727\u0026ndash;1738, Jun. 2014, doi: 10.1021/mp400713v.\u003c/li\u003e\n\u003cli\u003eC. A. Lipinski, \u0026ldquo;Rule of five in 2015 and beyond: Target and ligand structural limitations, ligand chemistry structure and drug discovery project decisions,\u0026rdquo; \u003cem\u003eAdv Drug Deliv Rev\u003c/em\u003e, vol. 101, pp. 34\u0026ndash;41, Jun. 2016, doi: 10.1016/j.addr.2016.04.029.\u003c/li\u003e\n\u003cli\u003eP. Mehnati, H. Khoshsima, M. S. Zakerhamidi, R. Kian, and M. Sahrai, \u0026ldquo;Solution environment effects on the photo-physical behavior of Daunorubicin and Mitoxantrone: Anthraquinone-based drugs,\u0026rdquo; \u003cem\u003eJ Mol Struct\u003c/em\u003e, vol. 1310, p. 138317, Aug. 2024, doi: 10.1016/j.molstruc.2024.138317.\u003c/li\u003e\n\u003cli\u003eT. A. Nyijime, A. M. Ayuba, and H. F. Chahul, \u0026ldquo;Experimental and computational studies on activated Bambara groundnut (Vigna subterranean) hulls for the adsorptive removal of herbicides from aqueous solution,\u0026rdquo; \u003cem\u003eBull Natl Res Cent\u003c/em\u003e, vol. 45, no. 1, p. 189, Dec. 2021, doi: 10.1186/s42269-021-00648-1.\u003c/li\u003e\n\u003cli\u003eL. H. S. Santos, R. S. Ferreira, and E. R. Caffarena, \u0026ldquo;Integrating Molecular Docking and Molecular Dynamics Simulations,\u0026rdquo; 2019, pp. 13\u0026ndash;34. doi: 10.1007/978-1-4939-9752-7_2.\u003c/li\u003e\n\u003cli\u003eJ. Caballero, \u0026ldquo;The latest automated docking technologies for novel drug discovery,\u0026rdquo; \u003cem\u003eExpert Opin Drug Discov\u003c/em\u003e, vol. 16, no. 6, pp. 625\u0026ndash;645, Jun. 2021, doi: 10.1080/17460441.2021.1858793.\u003c/li\u003e\n\u003cli\u003eT. W. Johnson, R. A. Gallego, and M. P. Edwards, \u0026ldquo;Lipophilic Efficiency as an Important Metric in Drug Design,\u0026rdquo; \u003cem\u003eJ Med Chem\u003c/em\u003e, vol. 61, no. 15, pp. 6401\u0026ndash;6420, Aug. 2018, doi: 10.1021/acs.jmedchem.8b00077.\u003c/li\u003e\n\u003cli\u003eJ. Shaikh \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Pharmacophore mapping, 3D QSAR, molecular docking, and ADME prediction studies of novel Benzothiazinone derivatives,\u0026rdquo; \u003cem\u003eIn Silico Pharmacol\u003c/em\u003e, vol. 12, no. 2, p. 79, Aug. 2024, doi: 10.1007/s40203-024-00255-8.\u003c/li\u003e\n\u003cli\u003eY. H. Momin \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Computational investigation of 2, 4-Di Tert Butyl Phenol as alpha amylase inhibitor isolated from Coccinia grandis (L.) Voigt using molecular docking, and ADMET parameters,\u0026rdquo; \u003cem\u003eComput Biol Chem\u003c/em\u003e, vol. 110, p. 108087, Jun. 2024, doi: 10.1016/j.compbiolchem.2024.108087.\u003c/li\u003e\n\u003cli\u003eT. Ongtanasup, S. Wanmasae, S. Srisang, C. Manaspon, S. Net-anong, and K. Eawsakul, \u0026ldquo;In silico investigation of ACE2 and the main protease of SARS-CoV-2 with phytochemicals from Myristica fragrans (Houtt.) for the discovery of a novel COVID-19 drug,\u0026rdquo; \u003cem\u003eSaudi J Biol Sci\u003c/em\u003e, vol. 29, no. 9, p. 103389, Sep. 2022, doi: 10.1016/j.sjbs.2022.103389.\u003c/li\u003e\n\u003cli\u003eG. Klebe, \u0026ldquo;Protein\u0026ndash;Ligand Interactions as the Basis for Drug Action,\u0026rdquo; in \u003cem\u003eDrug Design\u003c/em\u003e, Berlin, Heidelberg: Springer Berlin Heidelberg, 2024, pp. 39\u0026ndash;65. doi: 10.1007/978-3-662-68998-1_4.\u003c/li\u003e\n\u003cli\u003eE. I. Edache, H. A. Dawi, and F. A. Ugbe, \u0026ldquo;3D-QSAR, Molecular Docking, Molecular Dynamics Simulations and Structural Studies of Some Selected Inhibitors of the Glycoprotein (GPC) of Lassa Virus,\u0026rdquo; \u003cem\u003eJournal of Applied Organometallic Chemistry\u003c/em\u003e, vol. 3, no. 3, pp. 224\u0026ndash;244, Jul. 2023, doi: 10.48309/jaoc.2023.410946.1103.\u003c/li\u003e\n\u003cli\u003eR. Shukla and T. Tripathi, \u0026ldquo;Molecular Dynamics Simulation of Protein and Protein\u0026ndash;Ligand Complexes,\u0026rdquo; in \u003cem\u003eComputer-Aided Drug Design\u003c/em\u003e, Singapore: Springer Singapore, 2020, pp. 133\u0026ndash;161. doi: 10.1007/978-981-15-6815-2_7.\u003c/li\u003e\n\u003cli\u003eK. Liu, E. Watanabe, and H. Kokubo, \u0026ldquo;Exploring the stability of ligand binding modes to proteins by molecular dynamics simulations,\u0026rdquo; \u003cem\u003eJ Comput Aided Mol Des\u003c/em\u003e, vol. 31, no. 2, pp. 201\u0026ndash;211, Feb. 2017, doi: 10.1007/s10822-016-0005-2.\u003c/li\u003e\n\u003cli\u003eC. S. C. Canales, A. R. Pavan, J. L. dos Santos, and F. R. Pavan, \u0026ldquo;In silico drug design strategies for discovering novel tuberculosis therapeutics,\u0026rdquo; \u003cem\u003eExpert Opin Drug Discov\u003c/em\u003e, vol. 19, no. 4, pp. 471\u0026ndash;491, Apr. 2024, doi: 10.1080/17460441.2024.2319042.\u003c/li\u003e\n\u003cli\u003eY. Isyaku, A. Uzairu, S. Uba, M. T. Ibrahim, and A. B. Umar, \u0026ldquo;QSAR, molecular docking, and design of novel 4-(N,N-diarylmethyl amines) Furan-2(5H)-one derivatives as insecticides against Aphis craccivora,\u0026rdquo; \u003cem\u003eBull Natl Res Cent\u003c/em\u003e, vol. 44, no. 1, p. 44, Dec. 2020, doi: 10.1186/s42269-020-00297-w.\u003c/li\u003e\n\u003cli\u003eB. Chandrasekaran, S. N. Abed, O. Al-Attraqchi, K. Kuche, and R. K. Tekade, \u0026ldquo;Computer-Aided Prediction of Pharmacokinetic (ADMET) Properties,\u0026rdquo; in \u003cem\u003eDosage Form Design Parameters\u003c/em\u003e, vol. 2, Elsevier, 2018, pp. 731\u0026ndash;755. doi: 10.1016/B978-0-12-814421-3.00021-X.\u003c/li\u003e\n\u003cli\u003eN. R. Khedkar, M. Sindkhedkar, and A. Joseph, \u0026ldquo;Fragment-Based Drug Discovery: Small Fragments, Big Impact \u0026minus; Success Stories of Approved Oncology Therapeutics,\u0026rdquo; \u003cem\u003eBioorg Chem\u003c/em\u003e, vol. 156, p. 108197, Mar. 2025, doi: 10.1016/j.bioorg.2025.108197.\u003c/li\u003e\n\u003cli\u003eM. S. Raghu \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;Design, synthesis and docking studies of new molecular hybrids bearing benzimidazole and thiazolidine-2,4-dione as potential antitubercular agents,\u0026rdquo; \u003cem\u003eJournal of the Indian Chemical Society\u003c/em\u003e, vol. 101, no. 10, p. 101346, 2024, doi: https://doi.org/10.1016/j.jics.2024.101346.\u003c/li\u003e\n\u003cli\u003eA. Ajala \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;QSAR application of natural therapeutics inhibitors against Alzheimer\u0026rsquo;s disease through in-silico virtual-screening, docking-simulation, molecular dynamics, and pharmacokinetic prediction analysis,\u0026rdquo; \u003cem\u003eIntelligent Pharmacy\u003c/em\u003e, vol. 2, no. 4, pp. 505\u0026ndash;515, 2024, doi: https://doi.org/10.1016/j.ipha.2023.12.004.\u003c/li\u003e\n\u003cli\u003eA. Ajala \u003cem\u003eet al.\u003c/em\u003e, \u0026ldquo;In-silico screening and ADMET evaluation of therapeutic MAO-B inhibitors against Parkinson disease,\u0026rdquo; \u003cem\u003eIntelligent Pharmacy\u003c/em\u003e, vol. 2, no. 4, pp. 554\u0026ndash;564, 2024, doi: https://doi.org/10.1016/j.ipha.2023.12.008.\u003c/li\u003e\n\u003cli\u003eA.-K. Sohlenius-Sternbeck and Y. Terelius, \u0026ldquo;Evaluation of ADMET Predictor in Early Discovery Drug Metabolism and Pharmacokinetics Project Work,\u0026rdquo; \u003cem\u003eDrug Metabolism and Disposition\u003c/em\u003e, vol. 50, no. 2, pp. 95\u0026ndash;104, Feb. 2022, doi: 10.1124/dmd.121.000552.\u003c/li\u003e\n\u003cli\u003eS. N. Adawara, G. A. Shallangwa, P. A. Mamza, and A. Ibrahim, \u0026ldquo;Molecular docking and QSAR theoretical model for prediction of phthalazinone derivatives as new class of potent dengue virus inhibitors,\u0026rdquo; \u003cem\u003eBeni Suef Univ J Basic Appl Sci\u003c/em\u003e, vol. 9, no. 1, Dec. 2020, doi: 10.1186/s43088-020-00073-9.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-chemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Chemistry](https://link.springer.com/journal/44371)","snPcode":"44371","submissionUrl":"https://submission.nature.com/new-submission/44371/3","title":"Discover Chemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Enoyl-Acyl Carrier Protein Reductase, MolDock score, MM/GBSA binding energy","lastPublishedDoi":"10.21203/rs.3.rs-6726135/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6726135/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe ongoing global challenge posed by drug-resistant strains of \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e underscores the urgent need for novel therapeutic strategies. In this study, a comprehensive \u003cem\u003ein silico\u003c/em\u003e approach was utilized to design, analyse, and evaluate a series of small-molecule inhibitors targeting the InhA enzyme, a critical component in mycolic acid biosynthesis. A total of 47 ligands were analysed using molecular docking, quantum chemical calculations, Molecular dynamics simulation, and ADMET profiling. Compounds 10, 12, and 14 exhibited superior binding affinities compared to reference drugs, with compound 14 emerging as the most promising based on MolDock scores, MM/GBSA binding energy (-70.08 kcal/mol), and dynamic stability from a 250 ns molecular dynamics (MD) simulation. Principal component analysis confirmed enhanced conformational stability for compound 14. Based on its favourable binding and non-toxic ADMET profile, compound 14 was chosen as a template compound for the design of two new derivatives. These analogues demonstrated improved docking scores (-132.579 and \u0026minus;\u0026thinsp;125.894 kcal/mol), high intestinal absorption (\u0026gt;\u0026thinsp;88%), and no predicted toxicity. The findings support compound 14 and its derivatives as viable InhA inhibitors for further preclinical development in TB therapy.\u003c/p\u003e","manuscriptTitle":"Molecular docking studies for investigating and evaluating some active compounds as potent anti-tubercular agents against InhA Inhibitors: In-Silico design, MD Simulation, DFT and Pharmacokinetics studies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-16 12:50:00","doi":"10.21203/rs.3.rs-6726135/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-24T12:30:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-23T08:02:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-18T08:23:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"283857830536256276976818189895467658240","date":"2025-06-13T07:08:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230417997350791071898213327204702585904","date":"2025-06-13T04:34:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"118807664303016948900318776265105883711","date":"2025-06-13T04:07:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-12T15:28:29+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-11T14:35:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-26T01:33:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-26T01:31:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Chemistry","date":"2025-05-22T14:46:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-chemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Chemistry](https://link.springer.com/journal/44371)","snPcode":"44371","submissionUrl":"https://submission.nature.com/new-submission/44371/3","title":"Discover Chemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8981fc78-ac39-4f2c-8492-53d03c29fa5f","owner":[],"postedDate":"June 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-13T12:38:39+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-16 12:50:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6726135","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6726135","identity":"rs-6726135","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}