Benzimidazole-Quinazoline Fused Derivativesvia In-Silico Study Approach Targeting GABA A Receptor for PET-based Brain Imaging

Benzimidazole-Quinazoline Fused Derivativesvia In-Silico Study Approach Targeting GABA A Receptor for PET-based Brain Imaging | 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 Benzimidazole-Quinazoline Fused Derivativesvia In-Silico Study Approach Targeting GABA A Receptor for PET-based Brain Imaging Vaibhav Pandey, Mohd. Faheem, Alok Kumar, Manish Dixit This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7160073/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Epilepsy is a neurological disorder that originates from an electrical imbalance in the brain. According to the data, approximately. ~50 millionpeople are affected by this disease. Exploring the epileptic region of the brain using positron emission tomography/computed tomography (PET/CT) is beneficial to medical research and can lead to accurate treatment. Many radiopharmaceuticals are used to explore the brain using PET scans, which are verycost-effective. This article aims to design potent scaffolds based on a benzimidazole-quinazoline fused skeleton and systematically screen them through a multi-parameter in silico approach involving DFT calculations, ADMET profiling, target prediction, molecular docking, and molecular dynamics simulation. The GABA A receptor is a crucial molecular target in epileptic conditions, and this study reveals that fluorine-substituted ligands have substantial binding affinities. In this study, it was observed that the fluorine (-F) atoms improved binding through hydrogen bonding and π-π interactions. The potent scaffolds can be radiolabeled with [ 18 F] fluorine atom to make suitable candidates for PET brain imaging. After screening the most promising scaffolds BEN01, BEN05, BEN07, BEN08, and BEN15, are shows the best physicochemical and pharmacokinetic characteristics. According to this study fluorinated benzimidazole–quinazoline fused ligands have the potential to be employed as radiopharmaceutical scaffolds for PET-based imaging of epileptic brain areas and other neurological disorders. In silico drug design Fluorinated ligands Radiopharmaceuticals PET imaging GABA receptor Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Epilepsy is a chronic neurological disorder characterised by recurrent, unprovoked seizures resulting from abnormal electrical activity in the brain [ 1 – 5 ]. The epilepsy causes structural brain abnormalities, central nervous system infections, traumatic brain injury, brain tumours, cerebrovascular disease and neurodegenerative diseases are most common[ 6 ]. Epilepsy disrupts this rhythmic electrical impulse pattern. There are bursts of electrical energy, like an unpredictable lightning storm, between cells in one or more areas of the brain. This electrical disruption causes changes in muscle movement, sensation, and loss of consciousness[ 7 ]. According to the data, globally, an estimated 5 million people are diagnosed with epilepsy each year. In high-income countries, there are estimated to be 49 per 100,000 people diagnosed with epilepsy, whereas in low and middle-income countries, this figure can be as high as 139 per 100,000 in a year[ 8 ]. The literature reported that the primary receptors implicated in the generation of epilepsy are the Gamma-Aminobutyric Acid type-A (GABA A ) receptors and glutamate-based receptors[ 9 ]. The GABA A receptor is a ligand-gated chloride ion channel and represents the primary inhibitory neurotransmitter receptor in the central nervous system[ 10 – 11 ]. Structurally, it is a pentameric complex composed of various subunits (α, β, γ, δ, etc.), whose composition determines receptor pharmacology and regional brain distribution (Fig. 1)[ 11 – 13 ]. The glutamate receptors, such as N -methyl-D-aspartate (NMDA) and α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptors, enhance excitatory neurotransmission, creating an imbalance between excitation and inhibition in the brain[ 9 ]. Many studies have targeted the GABA A receptor using different chemical ligands as inhibitors or for imaging through PET/CT using labelled radiopharmaceuticals with C-11, F-18, Ga-68, and other radionuclides[ 14 – 15 ]. Currently, the most commonly used radiotracers for epilepsy imaging are [ 18 F]FDG (fluorodeoxyglucose), which measures regional cerebral glucose metabolism, and [ 11 C]methionine, which lacks specificity[ 16 – 17 ]. Researcher focused on the target-specific imaging agents like GABA A targeting and benzodiazepine-based receptor-specific radiotracers, such as [ 11 C]flumazenil/ [ 18 F]flumazenil. These radiotracers can identify more defined regions of anomalies in the epileptogenic foci and have better sensitivity in the extratemporal areas [ 18 – 22 ]. The synthesis of these radiopharmaceuticals and their precursors is a tedious and multi-step process that requires sophisticated facility infrastructure. All enhance the cost and widen applicability. There is a need to develop a cost-effective PET tracer with a straightforward synthesis process that can be completed in a short time. Recent research has stated that benzimidazole and quinazoline-based scaffolds exhibit antiepileptic properties [ 23 – 24 ]. This property can be utilised to develop suitable radiopharmaceuticals for imaging ligands in the epileptic brain. In this study, we designed a benzimidazole-quinazoline fused moiety that targeted the GABA A receptor. A series of 15 structurally potent ligands designated as BEN01–BEN15 was selected based on their documented efficacy and relevance in recent literature reports. The selected ligands/scaffolds are screened using computational approaches, including density functional theory (DFT), ADMET profiling with various parameters, molecular docking, and molecular dynamics simulations, which provide valuable insights into the electronic structure and dynamic properties of these inhibitors. The role of fused benzimidazole-quinazoline offers structural versatility and potential improvements in binding affinity and bioavailability to inhibit the GABA A receptor. This study integrates in silico techniques to assess BEN01-15 scaffolds as GABA A -targeted. This study aims to design a potent scaffold that can be utilized as PET radiotracers to map neuronal loss or altered inhibitory signalling, which holds promising potential for improving diagnosis and treatment monitoring in epilepsy. Some of the potent scaffolds from this study are currently under development for chemistry and biological studies. 2. Computational Methodology 2.1 Chemical reactivity evaluation using DFT: GaussView software was used as a graphical interface to visualize molecular structures and analyze computed results efficiently. In contrast, Gaussian 09W was used to optimize the molecular geometry and evaluate electronic properties. The DFT calculations were performed using the B3LYP functional and 6-311G basis set. The Gaussian was chosen due to its robust computational capabilities in handling quantum chemical calculations with high accuracy. The electronic property like HOMO (eV), LUMO (eV), energy gap (ΔE LUMO-HOMO ), global reactivity descriptors such as ionization potential (IP), electron affinity (EA), electronegativity (χ), global hardness (ղ), chemical potential (µ), electrophilicity index (ω), softness (S) and molecular electrostatic potential (MESP) were computed [ 25 – 26 , 59 ]. The energy gap and MESP map were generated to analyse the stability of the molecule and charge distribution and identify potential electrophilic and nucleophilic regions relevant for GABA A interaction[ 27 – 31 ]. 2.2 Ligand preparation and optimisation: The Benzimidazole-quinazoline fused derivatives-based ligands (abbreviated as BEN01-15) were designed in ChemDraw Ultra (version 20.0)[ 32 ] and represent a set of 15 such molecules added to a virtual library, and each ligand substituted with a fluorine atom. The chemical structures and their SMILES representations, along with physicochemical properties, are presented in Table 1 . Table 1 General physicochemical properties of selected compounds [ADMETLab3.0]. Ligand SMILES MW TPSA pKa nHA nHD logP ligD7.4 logS BEN01 FC(C = C1) = CC(C = N2) = C1N3C2 = NC4 = C3C = C5C = CC = CC5 = C4 287.0 30.19 3.6 3 0 3.38 3.55 -4.3 BEN02 FC(C = C1) = CC2 = C1N3C(C = C4C = C5C = CC = CC5 = CC4 = C6) = C6N = C3N = C2 337.1 30.19 3.3 3 0 4.24 3.89 -6.0 BEN03 FC(C = C1) = CC(C = N2) = C1N3C2 = NC4 = C3C = CC5 = C4C = CC = C5 287.1 30.19 4.1 3 0 3.36 3.48 -4.2 BEN04 FC1 = CC(C = N2) = C(C = C1)N3C2 = NC4 = C3C5 = C(C6 = C4C = CC = C6)C = CC = C5 337.1 30.19 3.9 3 0 4.21 3.82 -5.4 BEN05 COC(C = C1) = CC2 = C1N = C3N = CC(C = C(F)C = C4) = C4N23 267.1 39.42 3.4 4 0 2.43 2.65 -3.3 BEN06 OC1 = CC = CC2 = C1N3C(N = CC4 = C3C = CC(F) = C4) = N2 253.1 50.42 2.3 4 1 2.53 2.54 -4.4 BEN07 COC1 = CC = C(OC)C2 = C1N3C(N = CC4 = C3C = CC(F) = C4) = N2 297.1 48.65 3.0 5 0 2.11 2.32 -3.1 BEN08 FC1 = CC = CC2 = C1N3C(N = CC4 = C3C = CC(F) = C4) = N2 255.1 30.19 3.3 3 0 2.56 2.80 -3.7 BEN09 IC(C = C1) = CC2 = C1N = C3N = CC(C = C(F)C = C4) = C4N23 362.9 30.19 3.8 3 0 3.23 3.24 -4.3 BEN10 CC(C)(C)C(C = C1) = CC2 = C1N = C3N = CC(C = C(F)C = C4) = C4N23 293.1 30.19 4.8 3 0 3.98 3.71 -5.0 BEN11 CC(C = C1) = CC2 = C1N = C3N = CC(C = C(F)C = C4) = C4N23 251.1 30.19 3.0 3 0 2.97 3.09 -3.7 BEN12 BrC1 = CC(N = C2N = CC(C = C(F)C = C3) = C3N42) = C4C = C1Br 392.8 30.19 4.3 3 0 3.69 3.57 -4.8 BEN13 BrC1 = CC = C(Br)C2 = C1N3C(N = CC4 = C3C = CC(F) = C4) = N2 392.8 30.19 3.2 3 0 3.42 3.39 -4.6 BEN14 ClC1 = CC = CC2 = C1N3C(N = CC4 = C3C = CC(F) = C4) = N2 271.0 30.19 2.8 3 0 2.76 2.90 -4.1 BEN15 FC1 = CC(C = NC2 = NC3 = C(C = CC = C3)N42) = C4C = C1 237.0 30.19 2.5 3 0 2.28 2.47 -3.1 BZP CN1C(= O)CN = C(C2 = C1C = CC(= C2)Cl)C3 = CC = CC = C3 284.0 32.60 1.1 9 0 2.90 2.80 -3.9 Energy minimisation of the ligands was performed using Chem3D (version 20.0)[ 33 ] with the MMFF94 force field, setting the maximum number of iterations to 1000 and the minimum RMS gradient to 0.01. After minimisation, the files were saved in .sdf format. The converted files were further optimized using Density Functional Theory (DFT) with the B3LYP/6-311G basis set and the optimized geometries. The files were extracted as .pdb files. 2.3Target Prediction & Bioactivity Score: Target prediction was performed using the SuperPred web tool ( https://prediction.charite.de/subpages/target_prediction.php )[ 34 ], with a specific focus on the GABA A receptor. The ligands were input as SMILES to identify potential target interactions. Both 2D and 3D similarity measures were applied to the known ligands virtual library database, generating ranked predictions for GABA A and associated off-target interactions. This computational approach provided insight into the potential efficacy and specificity of the ligands. The pie chart depicting target classes was generated by the SwissTargetPrediction web tool ( http://www.swisstargetprediction.ch/ )[ 35 ]. 2.4 Pharmacokinetics, drug-likeness and toxicity prediction: The Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties play a key role in developing an ideal radiopharmaceutical for brain imaging. The ADMET screening was evaluated by using three different web servers. The Pharmacokinetic and drug-likeness properties were evaluated using ADMETLab3.0[ 36 ], which included toxicity and distribution profiles. The metabolism and absorption were predicted using the SwissADME web tool[ 37 – 39 ]. The excretion and logBB (blood-brain ratio) were evaluated by the pkCSM web server[ 40 ]. 2.5 Receptor Preparation and Docking Study: The Docking studies were conducted using AutoDock Vina by using PyRx software[ 41 – 49 ]. The crystal structure of the GABA A receptor (PDB ID: 7QNE) was selected and downloaded via the RCSB database ( www.rcsb.org ) employing the GABA A active site [ 50 ]. For the active site identification, use P2RANK ( https://prankweb.cz/ )[ 51 – 54 ]. The rank2 active pocket was chosen, having several residues 19, Probability 0.719, and a score of 13.90 GABA A receptor. The binding affinity, key hydrogen bonding, and other interactions with GABA A residues were analysed via Discovery Studio. After the docking, the binding free energy (ΔG kcal/mol) was calculated. The other parameters calculated (Table 2 ), like Inhibition constant (Ki), Ligand Efficiency (LE), Lipophilic Efficiency (LipE), Ligand Efficiency Index (LEI), Enthalpy Contribution (ΔH), are as follows, along with their relevance regarding GABA A ligands and interpretation [ 55 – 56 ]: 2.6 Molecular Dynamics: The online dynamics server iMODS conducted the MD simulations of this study ( https://imods.iqfr.csic.es/ )[ 57 ] aimed to explore the collective motions of the receptor using Normal Mode Analysis (NMA) in internal coordinates (torsional space) and to determine the deformability and rigidity of our complexes, depending on NMA, which provides deformability, B-factors, eigenvalues, and a variance plot. The complex files were prepared by adding receptor and docked ligands (BEN01 and the Benzodiazepine complex) and saved in the .pdb file format[ 58 ]. 3. Results and Discussion 3.1 Molecular Electrostatic Potential Surface (MESP): The surface morphology in the development of new drugs plays a crucial role in identifying the ideal drug for the target. In this study, the surface morphology was evaluated using Molecular Electrostatic Potential Surfaces (MESP) by using Density Functional Theory (DFT). The MESP provides a visual representation of charge distribution, allowing identification of electrophilic and nucleophilic regions essential for the GABA A receptor. A positive electrostatic potential (+ ESP) is produced when a point charge is placed in an area with a greater positive charge (electron-deficient region). On the other hand, an attractive contact results in a negative electrostatic potential (ESP), if the point charge is situated in an area with an excess of negative charge (electron-rich region). A molecular electrostatic potential surface is used for the computational calculation of nucleophilicity and electrophilicity of BEN01-15 [ 25 – 26 , 59 ]. These surfaces indicate electronic density variation with a colour change. The red regions depict areas of highest electron repulsion, and the blue areas signify regions of strongest electron attraction as depicted in Fig. 2 . The pictorial representation shows that all the molecules' surfaces have balanced electron-deficient zones, as shown in mint yellowish colours. These regions are favourable for electrophilic attack and to make H-bond donors (nHD) region with the GABA A receptor. The nitrogen group of the chemical skeleton shows a red zone, indicating electron-rich regions, indicating nucleophilic attack and favourable to make H-bond acceptors (nHA) with the GABA A receptor. Overall, these compounds indicate that the surface morphology is neither very electron-rich nor electron-deficient. In that case, all the selected compounds are low molecular weight and exhibit an electron-deficient region, indicating a low polar surface area. These properties can be utilized to create a perfect radiopharmaceutical and to detect epileptic regions in the brain. 3.2 Frontier Molecular Orbital (FMO) Analysis: The frontier molecular orbital (FMO) analysis, Global reactivity descriptors like electrophilicity index (ω), chemical potential (µ), hardness (η), softness (S), and electronegativity (χ) can be used to evaluate physicochemical, pharmacological, and ecotoxicological characteristics. The frontier molecular orbital (FMO) theory states that critical insights into molecular reactivity can be gained from the spatial distribution of frontier orbitals. The FMO analysis indicates that if ∆E gap is high, it means the molecule has low reactivity, high kinetic and chemical stability, and is more stable, less reactive, and less likely to form spontaneous interactions with the GABA A receptor, potentially behaving as inert. On the other hand, ∆E gap is low, indicating high reactivity and lower stability. The molecule is more chemically reactive, more likely to interact with the target GABA A receptor, and is therefore suitable for biological activity. The HOMO-LUMO gap values of all scaffolds are listed in Fig. 3 and compared with the corresponding 2M4NA and urea energy (eV) values, which are used as references[25–26,]. The HOMO–LUMO energy gap (∆E gap ) analysis reveals that the BEN01-15 based scaffolds exhibit transition energies (∆E gap ) ranging from 2.55-4.18eV. These lower energy gaps suggest enhanced electronic reactivity and molecular flexibility, which are favourable for biological interactions. When compared to standard compounds such as urea (∆E gap : 7.36 eV) and 2M4NA (∆E gap : 6.36 eV), the BEN moiety demonstrates superior reactivity but is less stable. From the virtual library, BEN02 shows an energy gap of the first transition of 2.55 eV, whereas BEN15 shows 4.15 eV. Our exploration focusedonthe Global Reactive Descriptor, evaluatingthe promising candidates based on their optimal electronic and reactivity profiles (Table 3 ). The BEN02 displayed the lowest energy gap (2.55 eV), indicating the lowest value among the selected library, which suggests high chemical reactivity and potential for better interaction with biological targets. Its high electrophilicity index (ω = 6.65) and favourable chemical potential (µ = −4.12 eV) suggest enhanced binding affinity and possible BBB permeability. The BEN08 demonstrates a particularly favourable electronic profile, with a high electrophilicity index (5.51 eV), optimal energy gap (3.83 eV), and suitable softness (0.96 eV⁻¹), and BEN05 also exhibits moderate potential due to its stable profile (ω = 5.24 eV, ΔE gap = 3.40 eV), its comparatively lower reactivity may limit receptor interaction strength. The BEN12 and BEN13 showed strong electrophilic character (ω = 6.20 and 6.03, respectively) with moderate hardness values (η ≈ 1.86–1.82 eV) and softness in the acceptable range (S ≈ 0.91–0.93), indicating a favourable balance between stability and reactivity.BEN14 and BEN15 also exhibited excellent reactivity with narrow energy gaps (3.80 and 4.18 eV), strong electron-accepting capabilities (EA = 2.74 and 2.62 eV), and the highest softness values (0.95 and 1.04 eV, respectively), suggesting high polarizability and the potential to participate in non-covalent interactions with the target. Notably, BEN15 showed the highest ionisation potential (6.80 eV) and electrophilicity among all ligands, reflecting both stability and reactivity. From the virtual library, the BEN02, BEN05, BEN08, BEN12, BEN13, BEN14 and BEN15 demonstrates favourable characteristics. These seven ligands demonstrate electronic properties comparable to better ones (of whom) and are suitable for further development as potential anti-epileptic agents. Table 3 Global Reactive Descriptor HOMO–LUMO Analysis Benzimidazole–Quinazoline fused ligands. Ligands E H (eV) E L (eV) ΔE gap (eV) IP(eV) EA(eV) 𝛘(eV) η(eV) µ(eV) ω(eV) S(eV) BEN01 -5.82 -2.74 3.08 5.82 2.74 4.28 1.54 -4.28 5.96 0.77 BEN02 -5.40 -2.84 2.55 5.40 2.84 4.12 1.28 -4.12 6.65 0.64 BEN03 -5.99 -2.58 3.41 5.99 2.58 4.28 1.71 -4.28 5.37 0.85 BEN04 -6.00 -2.55 3.45 6.00 2.55 4.27 1.72 -4.27 5.29 0.86 BEN05 -5.93 -2.52 3.40 5.93 2.52 4.22 1.70 -4.22 5.24 0.85 BEN06 -6.12 -2.43 3.69 6.12 2.43 4.28 1.85 -4.28 4.96 0.92 BEN07 -5.48 -2.37 3.11 5.48 2.37 3.93 1.56 -3.93 4.95 0.78 BEN08 -6.50 -2.68 3.83 6.50 2.68 4.59 1.91 -4.59 5.51 0.96 BEN09 -6.14 -2.42 3.72 6.14 2.42 4.28 1.86 -4.28 4.93 0.93 BEN10 -6.21 -2.54 3.67 6.21 2.54 4.38 1.83 -4.38 5.22 0.92 BEN11 -6.22 -2.55 3.66 6.22 2.55 4.39 1.83 -4.39 5.25 0.92 BEN12 -6.66 -2.94 3.71 6.66 2.94 4.80 1.86 -4.80 6.20 0.93 BEN13 -6.51 -2.87 3.65 6.51 2.87 4.69 1.82 -4.69 6.03 0.91 BEN14 -6.54 -2.74 3.80 6.54 2.74 4.64 1.90 -4.64 5.67 0.95 BEN15 -6.80 -2.62 4.18 6.80 2.62 4.71 2.09 -4.71 5.31 1.04 3.3 Target prediction analysis: Target prediction analysis confirmed that the GABA A receptor subtype composed of alpha-1,beta-3, and gamma-2 subunits with high confidence, supporting its biological relevance. The targets identified through the SuperPRED web server also provide valuable insights into off-target effects and alternative pathways. To evaluate the binding specificity of the designed benzimidazole-quinazoline fused hybrid molecules toward the GABA A receptor, a comprehensive target prediction analysis was performed. All fifteen ligands demonstrated high prediction probabilities, ranging from 82–95%, as shown in Fig. 4 . Among the evaluated compounds, BEN01, BEN02, BEN03, BEN04, BEN05, BEN07, BEN08, BEN09, BEN10, BEN12, BEN13, BEN14 and BEN15 are the top leading ligands, each showing target probability scores of > 85% (Fig. 4 ) targeted towards the GABA A receptor, with BEN04 exhibiting the highest predicted binding probability of 95%, followed by BEN03 shows 94%, and BEN08, BEN13, and BEN15 show 93%. These findings suggest a potential interaction between these ligands and the GABA A receptor. The predictive model demonstrated a consistent accuracy of 95.5% for all ligands, indicating the reliability of the computational approach used. A target prediction pi-chart was generated by SwissTarget Prediction, as shown in Fig. 5 . 3.4 ADMET analysis of benzimidazole–quinazoline fused derivatives: The ADMET analysis of BEN01–BEN15 compounds was compared with the reference ligand diazepam to assess their potential as CNS-active drug candidates. All BEN-series ligands demonstrated to adopt different parameters of ADMET and evaluate the properties by using well-known web servers like pkCSM, ADMETlab3.0, and SwissADMET. In this study, we evaluate the properties via ADMETlab3.0 along with a standard approved drug, Diazepam (chemically known as “7-chloro-1-methyl-5-phenyl-1,3-dihydro-2H-benzo[e][ 1 , 4 ]diazepin-2-one”). The fluorinated benzimidazole PET radiotracer (name of PET tracer) targeting epileptic brain regions has key parameters that require focus, including brain penetration, pharmacokinetics, and safety, which are selected (as mentioned in Table 4 ). These parameters output and set target ranges for evaluating the potent ligands. Some of the ligands do not follow the target range and violate it. The values are compared with Diazepam (as reference), as the drug. After screening, Diazepam shows seven violations of the selected range. The BEN05-11 and BEN13-15 show ≤ 7 violations, which is better than Diazepam. The Blood-Brain Barrier (BBB) permeability and blood-brain ratio (logBB) confirm their potential CNS availability. All the compounds are BBB allowed and show in the yellow region of the boiled-egg graph, as shown in Fig. 6 . Among the ligands, only four ligands are Pgp-ve, named BEN05, BEN07, and BEN08; the rest of the ligands are Pgp + ve. Table 4 ADMET result of Benzimidazole–Quinazoline fused ligands. Absorption Distribution Metabolism Excretion Toxicity Ligands Pgp BBB PPB Vd(L/kg) logBB CYP3A4 Inhibitor CYP2D6 CYP2C9 Inhibitors CL hERG H-HT AMES NeurTox DILI BEN01 Yes Yes 97.9 0.13 0.36 No Yes No 0.8 0.4 0.8 0.8 1.0 0.8 BEN02 Yes Yes 97.9 0.22 0.58 No No No 0.9 0.6 0.8 0.8 1.0 0.9 BEN03 Yes Yes 97.8 0.12 0.39 No Yes No 0.8 0.4 0.8 0.8 1.0 0.8 BEN04 Yes Yes 97.7 0.26 0.63 No No No 0.7 0.5 0.9 0.9 1.0 0.9 BEN05 No Yes 94.5 0.06 0.14 Yes Yes No 0.8 0.4 0.7 0.8 0.9 0.7 BEN06 Yes Yes 94.7 -0.47 0.16 No Yes No 0.7 0.2 0.7 0.8 0.9 0.6 BEN07 No Yes 95.4 0.26 -0.30 Yes Yes Yes 0.6 0.4 0.7 0.7 0.9 0.7 BEN08 Yes Yes 95.6 0.20 0.23 No No No 0.6 0.3 0.8 0.8 1.0 0.7 BEN09 No Yes 97.4 0.26 0.21 No No No 0.7 0.7 0.5 0.2 0.6 0.4 BEN10 Yes Yes 97.6 0.15 0.39 No No No 0.8 0.4 0.6 0.4 1.0 0.5 BEN11 Yes Yes 96.1 0.11 0.27 No No No 0.8 0.3 0.7 0.7 0.9 0.5 BEN12 Yes Yes 96.3 0.20 0.09 No No Yes 0.9 0.3 0.7 0.5 0.9 0.9 BEN13 Yes Yes 96.0 0.22 0.15 No No Yes 0.6 0.1 0.5 0.6 1.0 1.0 BEN14 Yes Yes 98.3 -0.01 0.19 No No No 0.7 0.3 0.7 0.6 1.0 0.8 BEN15 Yes Yes 95.8 -0.03 0.22 No Yes No 0.8 0.3 0.7 0.7 0.9 0.5 Diazepam No Yes 98.0 -0.12 0.33 Yes Yes Yes 0.3 0.5 0.6 0.2 1.0 0.2 3.5 Molecular docking analysis: The GABA A receptor, a ligand-gated ion channel, plays a crucial role in inhibitory neurotransmission in the central nervous system. The benzodiazepines, such as diazepam, bind to a specific site on the GABA A receptor, enhancing its inhibitory effects by increasing the receptor's affinity for GABA. This binding site is located at the interface between the α and γ subunits of the receptor. The key amino acids involved in benzodiazepine binding include residues from the α1 and γ2 subunits and other hydrophobic interactions that stabilise the ligand-receptor complex. These interactions contribute to the modulation of the receptor and the subsequent neuropharmacological effects of benzodiazepines. The fluorinated benzo[ 4 , 5 ]imidazo[1,2-a] quinazoline derivatives (BEN01–BEN15) targeting the GABA A receptor, which provided key insights into their binding affinities and interaction patterns. The downloaded file 7QNE contains a co-crystal ligand benzodiazepine, which reveals that Ser206, Ala79, Thr142, Phe77, Tyr210, and Phe100 are affected residues. For the screening of the selected library, perform rigid docking and dock all ligands in the same place as the benzodiazepine. The benzodiazepine co-crystal ligands and interacting residue were used as standards, the interacting amino acids with the receptor are all listed in Table 5 . Table 5 Interaction of the Benzimidazole–Quinazoline fused ligands. Type of interaction SN Compounds Atom /group Bond Type Amino acid 1 BEN01 -F Ph heterocyclic hydrogen bond π-interaction π-interaction Ser206 Phe77, Tyr210 Thr207, His102, Val203 2 BEN02 -F Ph heterocyclic hydrogen bond π-interaction π-interaction Lys156 Val203, Tyr58, Phe77, Tyr160 Tyr58, Val203 3 BEN03 Ph heterocyclic pi-interaction pi-interaction Ala79, Phe77, Tyr210, His102 Tyr210, Phe77 4 BEN04 Ph heterocyclic π-interaction π-interaction Phe77, Tyr58, Ala79, Thr207, Tyr210, His102 Tyr160, Phe77, Tyr210 5 BEN05 Ph heterocyclic π-interaction pi-interaction Tyr58, Phe77, Tyr210 Tyr160, Phe77 6 BEN06 -F Ph heterocyclic hydrogen bond pi-interaction pi-interaction Ser205 Phe77, His102, Val203, Tyr210 Tyr210, Phe77 7 BEN07 -F Ph Heterocyclic -OCH3 hydrogen bond pi-interaction pi-interaction pi-interaction Phe77 His102, Tyr210, Phe77 Phe77, Tyr210 Tyr58, Ala79 8 BEN08 Ph heterocyclic pi-interaction pi-interaction Phe77, Tyr210, His102, Val203 Phe77, Tyr210 9 BEN09 -F Ph Heterocyclic -I hydrogen bond pi-interaction pi-interaction pi-alkyl Ser206 Phe77, Tyr210 Phe77, Tyr210 Val203, His102, Val212 10 BEN10 -F Ph Heterocyclic -C(CH3)3 hydrogen bond pi-interaction pi-interaction pi-alkyl Ser206 Phe77, Tyr210 His102 Phe77, Tyr210 Val203, His102, Tyr210 11 BEN11 -F Ph Heterocyclic -CH3 hydrogen bond pi-pi interaction pi-pi interaction pi-alkyl Ser206 Phe77, Tyr210 Phe77, His102, Tyr210 Val203, Val212 12 BEN12 Ph Heterocyclic -Br pi-interaction pi-interaction pi-alkyl His102, Phe77 Tyr160, Phe77, Tyr160 Tyr58 13 BEN13 Ph Heterocyclic -Br pi-interaction pi-interaction pi-alkyl Phe77, Tyr210, His102 Phe77, Tyr210 His102, Tyr58, Ala79 14 BEN14 Ph Heterocyclic pi-interaction pi-pi interaction His102, Phe77 Tyr210, Tyr160, Phe77 15 BEN15 Ph Heterocyclic pi-interaction pi-interaction Phe77, His102, Tyr210, Val203 Phe77, Tyr210 After docking, the binding free energies (ΔG) ranged from (–9.7 to − 12.8) kcal/mol and inhibition constants (Ki) from (0.38 to 72.51) nM are listed in Table 6 . The ligands BEN01, BEN03, BEN04, BEN06, BEN05, BEN06, BEN08, BEN11, BEN13, BEN14 and BEN15 showed the strongest binding affinities, forming multiple vital interactions such as (π–π stacking, hydrogen bonding and π-alkyl) contacts with critical receptor common residues including Phe77, His102, Tyr210, Thr207, and Ser206. Table 6 Thermodynamic & Efficiency Profiling of Benzimidazole-Quinazoline Fused Ligands. Ligands ΔG (kcal/mol) Ki (nM) LE (kcal/mol/HA) LipE LEI (kcal/mol/Da) ΔH (est. kcal/mol) BEN01 -12.8 00.38 4.27 6.11 0.0445 -12.8 BEN02 -09.7 72.51 3.23 3.96 0.0288 -09.7 BEN03 -12.5 00.63 4.17 5.92 0.0435 -12.5 BEN04 -11.5 03.43 3.83 4.27 0.0341 -11.5 BEN05 -11.0 08.01 2.75 5.72 0.0412 -11.0 BEN06 -11.9 01.74 2.98 6.43 0.0470 -11.9 BEN07 -10.6 15.77 2.12 5.80 0.0357 -10.6 BEN08 -11.8 02.06 3.93 6.33 0.0463 -11.8 BEN09 -10.2 31.07 3.40 4.51 0.0281 -10.2 BEN10 -09.8 61.20 3.27 4.24 0.0335 -09.8 BEN11 -11.4 04.07 3.80 5.45 0.0454 -11.4 BEN12 -10.9 09.49 3.63 4.41 0.0277 -10.9 BEN13 -11.6 02.90 3.87 5.12 0.0295 -11.6 BEN14 -11.6 02.90 3.87 5.78 0.0428 -11.6 BEN15 -11.7 02.45 3.90 6.32 0.0493 -11.7 Fluorine substitutions present in several ligands enhanced binding stability via hydrogen bonding. They contributed to favourable pharmacokinetic properties, such as blood-brain barrier (BBB) permeability and metabolic stability, which are critical for CNS-targeted agents. The other properties, such as ligand efficiency (LE), lipophilic efficiency (LipE), and ligand efficiency indices (LEI), support the drug-like nature of these compounds. The presence of fluorine in these scaffolds offers the advantage of fluorine-[ 18 F] labelling and makes these ligands promising candidates as PET imaging agents for the diagnosis of epilepsy. According to the docking data, the BEN01 emerged as the most promising scaffold with the highest binding affinity (ΔG = − 12.8 kcal/mol), lowest Ki (0.38 nM), and strong interactions with Phe77, Tyr210, and His102. Other promising ligands like BEN03, BEN06, BEN08, and BEN15 also showed excellent binding profiles. (Fig. 7 ) 3.6 Molecular Dynamics Simulation To analyse the physical movement and stability of our ligand–protein complexes(BEN01-7QNE) and Cocrystal Ligands-Protein complex (Benzodiazapine-7QNE), molecular dynamics (MD) simulations were carried out with the iMODS online web server. Particularly, Normal Mode Analysis (NMA) was carried out to explore the structural flexibility and the stability of the docked complexes. The outcome of NMA-mobility, as displayed in Figs. 8 – 9 and 10 , indicates high mobility in the docked complexes, emphasising the structural flexibility of the GABA A with BEN01 and Benzodiazepine (standard). The B-factor and deformability plots exhibit peaks at protein regions with increased flexibility, and taller peaks have increased deformability. The eigenvalues derived from the NMA also indicate the stiffness of motion in the protein–ligand complex. The lower eigenvalues indicate higher structural flexibility and improved complex stability. The BEN01 and Benzodiazepine complexes exhibit eigenvalues of 1.158721e-04 and 1.153432e-04, respectively, indicating favorable flexibility and stable molecular motions due to binding at the precise location. The covariance matrix further supports these findings by showing the correlation between residue motions in the complexes. In the matrix, red represents correlated motion and white represents uncorrelated motion. Together, our NMA and MD simulation results demonstrate that the docked complexes are highly deformable and flexible, especially in regions crucial for ligand binding. Based on the potential interactions of the selected proteins with GABA A BEN01/Benzodiazepine, we suggest that these compounds are promising candidates for drugs aimed at enabling highly bioavailable brain imaging. However, further in vitro and in vivo studies are required to evaluate the toxicity and therapeutic activity of GABA A -BEN compounds are potent scaffolds for target-specific epileptic brain imaging. 4. Conclusion The selected virtual library BEN01-15 is screened using various parameters, including DFT calculations, ADMET profiling, target prediction, molecular docking, and dynamics. The present study demonstrates that several fluorine-substituted BEN-series ligands exhibit strong binding affinity and favorable interaction profiles with the GABA A receptor, a key target in neurological conditions such as epilepsy. The presence of the fluorine atom (-F) in many of these ligands enhances binding through hydrogen bonding and pi-interactions, while also making them suitable candidates for PET/CT-based brain imaging, particularly using fluorine-18 as a radionuclide. The findings of this study reveal that BEN01, BEN05, BEN07, BEN08, and BEN15 are the most promising and potent scaffolds from the selected virtual library. BEN02 was excluded in the final stage due to its very low energy gap caused by a volatile compound and was rejected during ADMET profiling. Although BEN03 and BEN06 also exhibit high binding energy, they are not considered potent scaffolds based on their target prediction and global index parameter values, which are not suitable for creating an effective radiopharmaceutical for epileptic brain imaging. Both BEN01 and BEN15 may be potent and will undergo further screening in a real scenario, with results published soon. Overall, this work supports the development of fluorinated GABA-targeting benzimidazole–quinazoline fused derivatives, identified as potentially promising radiopharmaceuticals for PET-based imaging research in epileptic regions and other neurological disorders. Declarations Acknowledgment: The authors gratefully acknowledge institutional support for facilitating the computational and radiopharmaceutical research infrastructure for this study. CRediT authorship contribution statement Vaibhav Pandey: Methodology, Investigation, Formal analysis. Mohd. Faheem: Validation, Investigation. Alok Kumar: Validation, Conceptualization, and Manish Dixit : Supervision, Funding acquisition, Conceptualization. Funding We acknowledge the Indian Council of Medical Research, New Delhi, India (ICMR-DHR; Grant Number: 11013/24/2021-GIA/HR), for funding support of this work. V. P. acknowledges fellowship with this project. Data availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Declaration of competing interest The authors declare no competing interests. Ethical approval NA. Consent to participate NA. Consent to publish NA Clinical trial number: not applicable References Koutroumanidis M, Stavropoulou-Deli A, Giannakopoulou A, Kostopoulos GK. Introduction to Epilepsy and Related Brain Disorders. 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2","display":"","copyAsset":false,"role":"figure","size":227305,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular electrostatic profile of Benzimidazole–Quinazolinefused Scaffold.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7160073/v1/451d3ae5683134175cf03049.png"},{"id":88410729,"identity":"a322b3d9-222e-49f8-9586-8f2e2f747e6f","added_by":"auto","created_at":"2025-08-06 08:23:36","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":199150,"visible":true,"origin":"","legend":"\u003cp\u003eHOMO–LUMO profile of selected molecules.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7160073/v1/8b72b5cbdc6d26d3ee6a84d3.png"},{"id":88412062,"identity":"29289bda-efbb-4fa6-98c0-6452ca1f4a39","added_by":"auto","created_at":"2025-08-06 08:31:35","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":61914,"visible":true,"origin":"","legend":"\u003cp\u003eTarget prediction probability with \u0026gt;95% model accuracy.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7160073/v1/ccdc04a11e791b2945d91a1a.png"},{"id":88410727,"identity":"3a6afbcc-6f81-4283-8c6a-a59e527f44bc","added_by":"auto","created_at":"2025-08-06 08:23:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":108525,"visible":true,"origin":"","legend":"\u003cp\u003ePie chart of selected scaffolds for targeted GABA\u003csub\u003eA\u003c/sub\u003e receptor\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7160073/v1/1e0320ccd8e1bf2259d00448.png"},{"id":88408937,"identity":"f57e3611-6b78-4ff6-a1c6-1715da1adc0b","added_by":"auto","created_at":"2025-08-06 08:15:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":36149,"visible":true,"origin":"","legend":"\u003cp\u003eBBB is allowed and shown in the yellow region of the boiled-egg diagram\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7160073/v1/4c7cc15e44aaa94a5301e026.png"},{"id":88408928,"identity":"328d276e-6aca-4e70-b301-fcfcb979ff71","added_by":"auto","created_at":"2025-08-06 08:15:36","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":147893,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e2D molecular interaction of \u003c/strong\u003eBenzimidazole–quinazoline fused ligands with (7QNE) GABA\u003csub\u003eA\u003c/sub\u003e receptor.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7160073/v1/1211a19f85d400a9f87d7e91.png"},{"id":88410736,"identity":"59f84a63-2918-4c8e-9877-cfa42608ec81","added_by":"auto","created_at":"2025-08-06 08:23:36","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":329244,"visible":true,"origin":"","legend":"\u003cp\u003eIMODS evaluated the molecular mobility of the docked ligand–protein complexes. The direction of motion is displayed with the two-colored afne arrows. The motion is greater if the arrow is longer, (A) BEN01(B) Benzodiazepine.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7160073/v1/89b906430393925829b8d518.png"},{"id":88408932,"identity":"fcff0550-e359-4eb6-857f-fd432743776e","added_by":"auto","created_at":"2025-08-06 08:15:36","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":235441,"visible":true,"origin":"","legend":"\u003cp\u003eIMODS molecular dynamics simulations outputs of BEN01, Deformability graphs (A), B-factor plots (B), eigenvalue plots (C), variance map plots (D), correlation matrix plots (E).\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-7160073/v1/241470ce7fdac0bff5b3572c.png"},{"id":88410744,"identity":"c30bd782-d578-4db6-9733-9e2484a7783b","added_by":"auto","created_at":"2025-08-06 08:23:36","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":222960,"visible":true,"origin":"","legend":"\u003cp\u003eIMODS molecular dynamics simulations. Outputs of Benzodiazepine, Deformability graphs (A), B-factor plots (B), eigenvalue plots (C), variance map plots (D), correlation matrix plots (E).\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-7160073/v1/987cd790941cccea6ef1c121.png"},{"id":92600486,"identity":"7d672ba3-5e74-48ab-8def-ae0a4dd1c5a9","added_by":"auto","created_at":"2025-10-01 14:23:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3145223,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7160073/v1/18b23dce-6300-4d0d-b5e5-6d4c862487ad.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Benzimidazole-Quinazoline Fused Derivativesvia In-Silico Study Approach Targeting GABA A Receptor for PET-based Brain Imaging","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eEpilepsy is a chronic neurological disorder characterised by recurrent, unprovoked seizures resulting from abnormal electrical activity in the brain [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The epilepsy causes structural brain abnormalities, central nervous system infections, traumatic brain injury, brain tumours, cerebrovascular disease and neurodegenerative diseases are most common[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Epilepsy disrupts this rhythmic electrical impulse pattern. There are bursts of electrical energy, like an unpredictable lightning storm, between cells in one or more areas of the brain. This electrical disruption causes changes in muscle movement, sensation, and loss of consciousness[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. According to the data, globally, an estimated 5\u0026nbsp;million people are diagnosed with epilepsy each year. In high-income countries, there are estimated to be 49 per 100,000 people diagnosed with epilepsy, whereas in low and middle-income countries, this figure can be as high as 139 per 100,000 in a year[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The literature reported that the primary receptors implicated in the generation of epilepsy are the Gamma-Aminobutyric Acid type-A (GABA\u003csub\u003eA\u003c/sub\u003e) receptors and glutamate-based receptors[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The GABA\u003csub\u003eA\u003c/sub\u003e receptor is a ligand-gated chloride ion channel and represents the primary inhibitory neurotransmitter receptor in the central nervous system[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Structurally, it is a pentameric complex composed of various subunits (α, β, γ, δ, etc.), whose composition determines receptor pharmacology and regional brain distribution (Fig.\u0026nbsp;1)[\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The glutamate receptors, such as \u003cem\u003eN\u003c/em\u003e-methyl-D-aspartate (NMDA) and α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) receptors, enhance excitatory neurotransmission, creating an imbalance between excitation and inhibition in the brain[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMany studies have targeted the GABA\u003csub\u003eA\u003c/sub\u003e receptor using different chemical ligands as inhibitors or for imaging through PET/CT using labelled radiopharmaceuticals with C-11, F-18, Ga-68, and other radionuclides[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Currently, the most commonly used radiotracers for epilepsy imaging are [\u003csup\u003e18\u003c/sup\u003eF]FDG (fluorodeoxyglucose), which measures regional cerebral glucose metabolism, and [\u003csup\u003e11\u003c/sup\u003eC]methionine, which lacks specificity[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Researcher focused on the target-specific imaging agents like GABA\u003csub\u003eA\u003c/sub\u003e targeting and benzodiazepine-based receptor-specific radiotracers, such as [\u003csup\u003e11\u003c/sup\u003eC]flumazenil/ [\u003csup\u003e18\u003c/sup\u003eF]flumazenil. These radiotracers can identify more defined regions of anomalies in the epileptogenic foci and have better sensitivity in the extratemporal areas [\u003cspan additionalcitationids=\"CR19 CR20 CR21\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The synthesis of these radiopharmaceuticals and their precursors is a tedious and multi-step process that requires sophisticated facility infrastructure. All enhance the cost and widen applicability. There is a need to develop a cost-effective PET tracer with a straightforward synthesis process that can be completed in a short time. Recent research has stated that benzimidazole and quinazoline-based scaffolds exhibit antiepileptic properties [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This property can be utilised to develop suitable radiopharmaceuticals for imaging ligands in the epileptic brain.\u003c/p\u003e\u003cp\u003eIn this study, we designed a benzimidazole-quinazoline fused moiety that targeted the GABA\u003csub\u003eA\u003c/sub\u003e receptor. A series of 15 structurally potent ligands designated as BEN01\u0026ndash;BEN15 was selected based on their documented efficacy and relevance in recent literature reports. The selected ligands/scaffolds are screened using computational approaches, including density functional theory (DFT), ADMET profiling with various parameters, molecular docking, and molecular dynamics simulations, which provide valuable insights into the electronic structure and dynamic properties of these inhibitors. The role of fused benzimidazole-quinazoline offers structural versatility and potential improvements in binding affinity and bioavailability to inhibit the GABA\u003csub\u003eA\u003c/sub\u003e receptor. This study integrates \u003cem\u003ein silico\u003c/em\u003e techniques to assess BEN01-15 scaffolds as GABA\u003csub\u003eA\u003c/sub\u003e-targeted. This study aims to design a potent scaffold that can be utilized as PET radiotracers to map neuronal loss or altered inhibitory signalling, which holds promising potential for improving diagnosis and treatment monitoring in epilepsy. Some of the potent scaffolds from this study are currently under development for chemistry and biological studies.\u003c/p\u003e"},{"header":"2. Computational Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Chemical reactivity evaluation using DFT:\u003c/h2\u003e\u003cp\u003eGaussView software was used as a graphical interface to visualize molecular structures and analyze computed results efficiently. In contrast, Gaussian 09W was used to optimize the molecular geometry and evaluate electronic properties. The DFT calculations were performed using the B3LYP functional and 6-311G basis set. The Gaussian was chosen due to its robust computational capabilities in handling quantum chemical calculations with high accuracy. The electronic property like HOMO (eV), LUMO (eV), energy gap (ΔE\u003csub\u003eLUMO-HOMO\u003c/sub\u003e), global reactivity descriptors such as ionization potential (IP), electron affinity (EA), electronegativity (χ), global hardness (ղ), chemical potential (\u0026micro;), electrophilicity index (ω), softness (S) and molecular electrostatic potential (MESP) were computed [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The energy gap and MESP map were generated to analyse the stability of the molecule and charge distribution and identify potential electrophilic and nucleophilic regions relevant for GABA\u003csub\u003eA\u003c/sub\u003e interaction[\u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Ligand preparation and optimisation:\u003c/h2\u003e\u003cp\u003eThe Benzimidazole-quinazoline fused derivatives-based ligands (abbreviated as BEN01-15) were designed in ChemDraw Ultra (version 20.0)[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] and represent a set of 15 such molecules added to a virtual library, and each ligand substituted with a fluorine atom. The chemical structures and their SMILES representations, along with physicochemical properties, are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGeneral physicochemical properties of selected compounds [ADMETLab3.0].\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLigand\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSMILES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTPSA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003epKa\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003enHA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003enHD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003elogP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eligD7.4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003elogS\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-4.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFC(C\u0026thinsp;=\u0026thinsp;C1)\u0026thinsp;=\u0026thinsp;CC2\u0026thinsp;=\u0026thinsp;C1N3C(C\u0026thinsp;=\u0026thinsp;C4C\u0026thinsp;=\u0026thinsp;C5C\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;CC5\u0026thinsp;=\u0026thinsp;CC4\u0026thinsp;=\u0026thinsp;C6)\u0026thinsp;=\u0026thinsp;C6N\u0026thinsp;=\u0026thinsp;C3N\u0026thinsp;=\u0026thinsp;C2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e337.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-6.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFC(C\u0026thinsp;=\u0026thinsp;C1)\u0026thinsp;=\u0026thinsp;CC(C\u0026thinsp;=\u0026thinsp;N2)\u0026thinsp;=\u0026thinsp;C1N3C2\u0026thinsp;=\u0026thinsp;NC4\u0026thinsp;=\u0026thinsp;C3C\u0026thinsp;=\u0026thinsp;CC5\u0026thinsp;=\u0026thinsp;C4C\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;C5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e287.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-4.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFC1\u0026thinsp;=\u0026thinsp;CC(C\u0026thinsp;=\u0026thinsp;N2)\u0026thinsp;=\u0026thinsp;C(C\u0026thinsp;=\u0026thinsp;C1)N3C2\u0026thinsp;=\u0026thinsp;NC4\u0026thinsp;=\u0026thinsp;C3C5\u0026thinsp;=\u0026thinsp;C(C6\u0026thinsp;=\u0026thinsp;C4C\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;C6)C\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;C5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e337.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-5.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOC(C\u0026thinsp;=\u0026thinsp;C1)\u0026thinsp;=\u0026thinsp;CC2\u0026thinsp;=\u0026thinsp;C1N\u0026thinsp;=\u0026thinsp;C3N\u0026thinsp;=\u0026thinsp;CC(C\u0026thinsp;=\u0026thinsp;C(F)C\u0026thinsp;=\u0026thinsp;C4)\u0026thinsp;=\u0026thinsp;C4N23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e267.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e39.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-3.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN06\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOC1\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;CC2\u0026thinsp;=\u0026thinsp;C1N3C(N\u0026thinsp;=\u0026thinsp;CC4\u0026thinsp;=\u0026thinsp;C3C\u0026thinsp;=\u0026thinsp;CC(F)\u0026thinsp;=\u0026thinsp;C4)\u0026thinsp;=\u0026thinsp;N2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e253.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e50.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-4.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN07\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCOC1\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;C(OC)C2\u0026thinsp;=\u0026thinsp;C1N3C(N\u0026thinsp;=\u0026thinsp;CC4\u0026thinsp;=\u0026thinsp;C3C\u0026thinsp;=\u0026thinsp;CC(F)\u0026thinsp;=\u0026thinsp;C4)\u0026thinsp;=\u0026thinsp;N2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e297.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e48.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-3.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN08\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFC1\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;CC2\u0026thinsp;=\u0026thinsp;C1N3C(N\u0026thinsp;=\u0026thinsp;CC4\u0026thinsp;=\u0026thinsp;C3C\u0026thinsp;=\u0026thinsp;CC(F)\u0026thinsp;=\u0026thinsp;C4)\u0026thinsp;=\u0026thinsp;N2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e255.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-3.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN09\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eIC(C\u0026thinsp;=\u0026thinsp;C1)\u0026thinsp;=\u0026thinsp;CC2\u0026thinsp;=\u0026thinsp;C1N\u0026thinsp;=\u0026thinsp;C3N\u0026thinsp;=\u0026thinsp;CC(C\u0026thinsp;=\u0026thinsp;C(F)C\u0026thinsp;=\u0026thinsp;C4)\u0026thinsp;=\u0026thinsp;C4N23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e362.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-4.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCC(C)(C)C(C\u0026thinsp;=\u0026thinsp;C1)\u0026thinsp;=\u0026thinsp;CC2\u0026thinsp;=\u0026thinsp;C1N\u0026thinsp;=\u0026thinsp;C3N\u0026thinsp;=\u0026thinsp;CC(C\u0026thinsp;=\u0026thinsp;C(F)C\u0026thinsp;=\u0026thinsp;C4)\u0026thinsp;=\u0026thinsp;C4N23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e293.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-5.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN11\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCC(C\u0026thinsp;=\u0026thinsp;C1)\u0026thinsp;=\u0026thinsp;CC2\u0026thinsp;=\u0026thinsp;C1N\u0026thinsp;=\u0026thinsp;C3N\u0026thinsp;=\u0026thinsp;CC(C\u0026thinsp;=\u0026thinsp;C(F)C\u0026thinsp;=\u0026thinsp;C4)\u0026thinsp;=\u0026thinsp;C4N23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e251.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-3.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBrC1\u0026thinsp;=\u0026thinsp;CC(N\u0026thinsp;=\u0026thinsp;C2N\u0026thinsp;=\u0026thinsp;CC(C\u0026thinsp;=\u0026thinsp;C(F)C\u0026thinsp;=\u0026thinsp;C3)\u0026thinsp;=\u0026thinsp;C3N42)\u0026thinsp;=\u0026thinsp;C4C\u0026thinsp;=\u0026thinsp;C1Br\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e392.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-4.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN13\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBrC1\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;C(Br)C2\u0026thinsp;=\u0026thinsp;C1N3C(N\u0026thinsp;=\u0026thinsp;CC4\u0026thinsp;=\u0026thinsp;C3C\u0026thinsp;=\u0026thinsp;CC(F)\u0026thinsp;=\u0026thinsp;C4)\u0026thinsp;=\u0026thinsp;N2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e392.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-4.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClC1\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;CC2\u0026thinsp;=\u0026thinsp;C1N3C(N\u0026thinsp;=\u0026thinsp;CC4\u0026thinsp;=\u0026thinsp;C3C\u0026thinsp;=\u0026thinsp;CC(F)\u0026thinsp;=\u0026thinsp;C4)\u0026thinsp;=\u0026thinsp;N2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e271.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-4.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN15\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFC1\u0026thinsp;=\u0026thinsp;CC(C\u0026thinsp;=\u0026thinsp;NC2\u0026thinsp;=\u0026thinsp;NC3\u0026thinsp;=\u0026thinsp;C(C\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;C3)N42)\u0026thinsp;=\u0026thinsp;C4C\u0026thinsp;=\u0026thinsp;C1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e237.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e30.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-3.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBZP\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCN1C(=\u0026thinsp;O)CN\u0026thinsp;=\u0026thinsp;C(C2\u0026thinsp;=\u0026thinsp;C1C\u0026thinsp;=\u0026thinsp;CC(=\u0026thinsp;C2)Cl)C3\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;CC\u0026thinsp;=\u0026thinsp;C3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e284.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e32.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e-3.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eEnergy minimisation of the ligands was performed using Chem3D (version 20.0)[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] with the MMFF94 force field, setting the maximum number of iterations to 1000 and the minimum RMS gradient to 0.01. After minimisation, the files were saved in .sdf format. The converted files were further optimized using Density Functional Theory (DFT) with the B3LYP/6-311G basis set and the optimized geometries. The files were extracted as .pdb files.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3Target Prediction \u0026amp; Bioactivity Score:\u003c/h2\u003e\u003cp\u003eTarget prediction was performed using the SuperPred web tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://prediction.charite.de/subpages/target_prediction.php\u003c/span\u003e\u003cspan address=\"https://prediction.charite.de/subpages/target_prediction.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], with a specific focus on the GABA\u003csub\u003eA\u003c/sub\u003e receptor. The ligands were input as SMILES to identify potential target interactions. Both 2D and 3D similarity measures were applied to the known ligands virtual library database, generating ranked predictions for GABA\u003csub\u003eA\u003c/sub\u003e and associated off-target interactions. This computational approach provided insight into the potential efficacy and specificity of the ligands. The pie chart depicting target classes was generated by the SwissTargetPrediction web tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swisstargetprediction.ch/\u003c/span\u003e\u003cspan address=\"http://www.swisstargetprediction.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4 Pharmacokinetics, drug-likeness and toxicity prediction:\u003c/h2\u003e\u003cp\u003eThe Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) properties play a key role in developing an ideal radiopharmaceutical for brain imaging. The ADMET screening was evaluated by using three different web servers. The Pharmacokinetic and drug-likeness properties were evaluated using ADMETLab3.0[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], which included toxicity and distribution profiles. The metabolism and absorption were predicted using the SwissADME web tool[\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The excretion and logBB (blood-brain ratio) were evaluated by the pkCSM web server[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5 Receptor Preparation and Docking Study:\u003c/h2\u003e\u003cp\u003eThe Docking studies were conducted using AutoDock Vina by using PyRx software[\u003cspan additionalcitationids=\"CR42 CR43 CR44 CR45 CR46 CR47 CR48\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The crystal structure of the GABA\u003csub\u003eA\u003c/sub\u003e receptor (PDB ID: 7QNE) was selected and downloaded via the RCSB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"https://prediction.charite.de/subpages/target_prediction.php\" target=\"_blank\"\u003ewww.rcsb.org\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.rcsb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) employing the GABA\u003csub\u003eA\u003c/sub\u003e active site [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. For the active site identification, use P2RANK (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://prankweb.cz/\u003c/span\u003e\u003cspan address=\"https://prankweb.cz/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan additionalcitationids=\"CR52 CR53\" citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The rank2 active pocket was chosen, having several residues 19, Probability 0.719, and a score of 13.90 GABA\u003csub\u003eA\u003c/sub\u003e receptor. The binding affinity, key hydrogen bonding, and other interactions with GABA\u003csub\u003eA\u003c/sub\u003e residues were analysed via Discovery Studio. After the docking, the binding free energy (ΔG kcal/mol) was calculated. The other parameters calculated (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), like Inhibition constant (Ki), Ligand Efficiency (LE), Lipophilic Efficiency (LipE), Ligand Efficiency Index (LEI), Enthalpy Contribution (ΔH), are as follows, along with their relevance regarding GABA\u003csub\u003eA\u003c/sub\u003e ligands and interpretation [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]:\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"657\" height=\"347\"\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6 Molecular Dynamics:\u003c/h2\u003e\u003cp\u003eThe online dynamics server iMODS conducted the MD simulations of this study (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://imods.iqfr.csic.es/\u003c/span\u003e\u003cspan address=\"https://imods.iqfr.csic.es/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] aimed to explore the collective motions of the receptor using Normal Mode Analysis (NMA) in internal coordinates (torsional space) and to determine the deformability and rigidity of our complexes, depending on NMA, which provides deformability, B-factors, eigenvalues, and a variance plot. The complex files were prepared by adding receptor and docked ligands (BEN01 and the Benzodiazepine complex) and saved in the .pdb file format[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Molecular Electrostatic Potential Surface (MESP):\u003c/h2\u003e\u003cp\u003eThe surface morphology in the development of new drugs plays a crucial role in identifying the ideal drug for the target. In this study, the surface morphology was evaluated using Molecular Electrostatic Potential Surfaces (MESP) by using Density Functional Theory (DFT). The MESP provides a visual representation of charge distribution, allowing identification of electrophilic and nucleophilic regions essential for the GABA\u003csub\u003eA\u003c/sub\u003e receptor. A positive electrostatic potential (+\u0026thinsp;ESP) is produced when a point charge is placed in an area with a greater positive charge (electron-deficient region). On the other hand, an attractive contact results in a negative electrostatic potential (ESP), if the point charge is situated in an area with an excess of negative charge (electron-rich region). A molecular electrostatic potential surface is used for the computational calculation of nucleophilicity and electrophilicity of BEN01-15 [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. These surfaces indicate electronic density variation with a colour change. The red regions depict areas of highest electron repulsion, and the blue areas signify regions of strongest electron attraction as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The pictorial representation shows that all the molecules' surfaces have balanced electron-deficient zones, as shown in mint yellowish colours. These regions are favourable for electrophilic attack and to make H-bond donors (nHD) region with the GABA\u003csub\u003eA\u003c/sub\u003e receptor. The nitrogen group of the chemical skeleton shows a red zone, indicating electron-rich regions, indicating nucleophilic attack and favourable to make H-bond acceptors (nHA) with the GABA\u003csub\u003eA\u003c/sub\u003e receptor. Overall, these compounds indicate that the surface morphology is neither very electron-rich nor electron-deficient. In that case, all the selected compounds are low molecular weight and exhibit an electron-deficient region, indicating a low polar surface area. These properties can be utilized to create a perfect radiopharmaceutical and to detect epileptic regions in the brain.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Frontier Molecular Orbital (FMO) Analysis:\u003c/h2\u003e\u003cp\u003eThe frontier molecular orbital (FMO) analysis, Global reactivity descriptors like electrophilicity index (ω), chemical potential (\u0026micro;), hardness (η), softness (S), and electronegativity (χ) can be used to evaluate physicochemical, pharmacological, and ecotoxicological characteristics. The frontier molecular orbital (FMO) theory states that critical insights into molecular reactivity can be gained from the spatial distribution of frontier orbitals. The FMO analysis indicates that if ∆E\u003csub\u003egap\u003c/sub\u003e is high, it means the molecule has low reactivity, high kinetic and chemical stability, and is more stable, less reactive, and less likely to form spontaneous interactions with the GABA\u003csub\u003eA\u003c/sub\u003e receptor, potentially behaving as inert. On the other hand, ∆E\u003csub\u003egap\u003c/sub\u003e is low, indicating high reactivity and lower stability. The molecule is more chemically reactive, more likely to interact with the target GABA\u003csub\u003eA\u003c/sub\u003e receptor, and is therefore suitable for biological activity. The HOMO-LUMO gap values of all scaffolds are listed in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e and compared with the corresponding 2M4NA and urea energy (eV) values, which are used as references[25\u0026ndash;26,]. The HOMO\u0026ndash;LUMO energy gap (∆E\u003csub\u003egap\u003c/sub\u003e) analysis reveals that the BEN01-15 based scaffolds exhibit transition energies (∆E\u003csub\u003egap\u003c/sub\u003e) ranging from 2.55-4.18eV. These lower energy gaps suggest enhanced electronic reactivity and molecular flexibility, which are favourable for biological interactions. When compared to standard compounds such as urea (∆E\u003csub\u003egap\u003c/sub\u003e: 7.36 eV) and 2M4NA (∆E\u003csub\u003egap\u003c/sub\u003e: 6.36 eV), the BEN moiety demonstrates superior reactivity but is less stable. From the virtual library, BEN02 shows an energy gap of the first transition of 2.55 eV, whereas BEN15 shows 4.15 eV.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOur exploration focusedonthe Global Reactive Descriptor, evaluatingthe promising candidates based on their optimal electronic and reactivity profiles (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The BEN02 displayed the lowest energy gap (2.55 eV), indicating the lowest value among the selected library, which suggests high chemical reactivity and potential for better interaction with biological targets. Its high electrophilicity index (ω\u0026thinsp;=\u0026thinsp;6.65) and favourable chemical potential (\u0026micro; = \u0026minus;4.12 eV) suggest enhanced binding affinity and possible BBB permeability. The BEN08 demonstrates a particularly favourable electronic profile, with a high electrophilicity index (5.51 eV), optimal energy gap (3.83 eV), and suitable softness (0.96 eV⁻\u0026sup1;), and BEN05 also exhibits moderate potential due to its stable profile (ω\u0026thinsp;=\u0026thinsp;5.24 eV, ΔE\u003csub\u003egap\u003c/sub\u003e = 3.40 eV), its comparatively lower reactivity may limit receptor interaction strength. The BEN12 and BEN13 showed strong electrophilic character (ω\u0026thinsp;=\u0026thinsp;6.20 and 6.03, respectively) with moderate hardness values (η\u0026thinsp;\u0026asymp;\u0026thinsp;1.86\u0026ndash;1.82 eV) and softness in the acceptable range (S\u0026thinsp;\u0026asymp;\u0026thinsp;0.91\u0026ndash;0.93), indicating a favourable balance between stability and reactivity.BEN14 and BEN15 also exhibited excellent reactivity with narrow energy gaps (3.80 and 4.18 eV), strong electron-accepting capabilities (EA\u0026thinsp;=\u0026thinsp;2.74 and 2.62 eV), and the highest softness values (0.95 and 1.04 eV, respectively), suggesting high polarizability and the potential to participate in non-covalent interactions with the target. Notably, BEN15 showed the highest ionisation potential (6.80 eV) and electrophilicity among all ligands, reflecting both stability and reactivity. From the virtual library, the BEN02, BEN05, BEN08, BEN12, BEN13, BEN14 and BEN15 demonstrates favourable characteristics. These seven ligands demonstrate electronic properties comparable to better ones (of whom) and are suitable for further development as potential anti-epileptic agents.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGlobal Reactive Descriptor HOMO\u0026ndash;LUMO Analysis Benzimidazole\u0026ndash;Quinazoline fused ligands.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLigands\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE\u003csub\u003eH\u003c/sub\u003e(eV)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eE\u003csub\u003eL\u003c/sub\u003e(eV)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eΔE\u003csub\u003egap\u003c/sub\u003e(eV)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIP(eV)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eEA(eV)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026#120536;(eV)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eη(eV)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026micro;(eV)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eω(eV)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eS(eV)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-5.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-4.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e5.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-5.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-4.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e6.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-5.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-4.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e5.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-6.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-4.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e5.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-5.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-4.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e5.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN06\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-6.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-4.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e4.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN07\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-5.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-3.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e4.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN08\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-6.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-4.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e5.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN09\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-6.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-4.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e4.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-6.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-4.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e5.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN11\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-6.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-4.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e5.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-6.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-4.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e6.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.93\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN13\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-6.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-4.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e6.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-6.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-4.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e5.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN15\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-6.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-2.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e4.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e-4.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e5.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e1.04\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Target prediction analysis:\u003c/h2\u003e\u003cp\u003eTarget prediction analysis confirmed that the GABA\u003csub\u003eA\u003c/sub\u003e receptor subtype composed of alpha-1,beta-3, and gamma-2 subunits with high confidence, supporting its biological relevance. The targets identified through the SuperPRED web server also provide valuable insights into off-target effects and alternative pathways. To evaluate the binding specificity of the designed benzimidazole-quinazoline fused hybrid molecules toward the GABA\u003csub\u003eA\u003c/sub\u003e receptor, a comprehensive target prediction analysis was performed. All fifteen ligands demonstrated high prediction probabilities, ranging from 82\u0026ndash;95%, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAmong the evaluated compounds, BEN01, BEN02, BEN03, BEN04, BEN05, BEN07, BEN08, BEN09, BEN10, BEN12, BEN13, BEN14 and BEN15 are the top leading ligands, each showing target probability scores of \u0026gt;\u0026thinsp;85% (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e4\u003c/span\u003e) targeted towards the GABA\u003csub\u003eA\u003c/sub\u003e receptor, with BEN04 exhibiting the highest predicted binding probability of 95%, followed by BEN03 shows 94%, and BEN08, BEN13, and BEN15 show 93%. These findings suggest a potential interaction between these ligands and the GABA\u003csub\u003eA\u003c/sub\u003e receptor. The predictive model demonstrated a consistent accuracy of 95.5% for all ligands, indicating the reliability of the computational approach used. A target prediction pi-chart was generated by SwissTarget Prediction, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4 ADMET analysis of benzimidazole\u0026ndash;quinazoline fused derivatives:\u003c/h2\u003e\u003cp\u003eThe ADMET analysis of BEN01\u0026ndash;BEN15 compounds was compared with the reference ligand diazepam to assess their potential as CNS-active drug candidates. All BEN-series ligands demonstrated to adopt different parameters of ADMET and evaluate the properties by using well-known web servers like pkCSM, ADMETlab3.0, and SwissADMET. In this study, we evaluate the properties via ADMETlab3.0 along with a standard approved drug, Diazepam (chemically known as \u0026ldquo;7-chloro-1-methyl-5-phenyl-1,3-dihydro-2H-benzo[e][\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]diazepin-2-one\u0026rdquo;). The fluorinated benzimidazole PET radiotracer (name of PET tracer) targeting epileptic brain regions has key parameters that require focus, including brain penetration, pharmacokinetics, and safety, which are selected (as mentioned in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These parameters output and set target ranges for evaluating the potent ligands. Some of the ligands do not follow the target range and violate it. The values are compared with Diazepam (as reference), as the drug. After screening, Diazepam shows seven violations of the selected range. The BEN05-11 and BEN13-15 show\u0026thinsp;\u0026le;\u0026thinsp;7 violations, which is better than Diazepam. The Blood-Brain Barrier (BBB) permeability and blood-brain ratio (logBB) confirm their potential CNS availability. All the compounds are BBB allowed and show in the yellow region of the boiled-egg graph, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e6\u003c/span\u003e. Among the ligands, only four ligands are Pgp-ve, named BEN05, BEN07, and BEN08; the rest of the ligands are Pgp\u0026thinsp;+\u0026thinsp;ve.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eADMET result of Benzimidazole\u0026ndash;Quinazoline fused ligands.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"15\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAbsorption\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e\u003cp\u003eDistribution\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u003cp\u003eMetabolism\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eExcretion\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c15\" namest=\"c11\"\u003e\u003cp\u003eToxicity\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLigands\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePgp\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBBB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePPB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVd(L/kg)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003elogBB\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCYP3A4 Inhibitor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eCYP2D6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eCYP2C9 Inhibitors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eCL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003ehERG\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eH-HT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eAMES\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003eNeurTox\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003eDILI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBEN01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBEN02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBEN03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBEN04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBEN05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBEN06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e94.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBEN07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e95.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBEN08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e95.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBEN09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBEN10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e97.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBEN11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBEN12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBEN13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBEN14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e98.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBEN15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e95.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDiazepam\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e98.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-0.12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.33\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e0.5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e\u003cp\u003e\u003cb\u003e0.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c13\"\u003e\u003cp\u003e\u003cb\u003e0.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c14\"\u003e\u003cp\u003e\u003cb\u003e1.0\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c15\"\u003e\u003cp\u003e\u003cb\u003e0.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Molecular docking analysis:\u003c/h2\u003e\u003cp\u003eThe GABA\u003csub\u003eA\u003c/sub\u003e receptor, a ligand-gated ion channel, plays a crucial role in inhibitory neurotransmission in the central nervous system. The benzodiazepines, such as diazepam, bind to a specific site on the GABA\u003csub\u003eA\u003c/sub\u003e receptor, enhancing its inhibitory effects by increasing the receptor's affinity for GABA. This binding site is located at the interface between the α and γ subunits of the receptor. The key amino acids involved in benzodiazepine binding include residues from the α1 and γ2 subunits and other hydrophobic interactions that stabilise the ligand-receptor complex.\u003c/p\u003e\u003cp\u003eThese interactions contribute to the modulation of the receptor and the subsequent neuropharmacological effects of benzodiazepines. The fluorinated benzo[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]imidazo[1,2-a] quinazoline derivatives (BEN01\u0026ndash;BEN15) targeting the GABA\u003csub\u003eA\u003c/sub\u003e receptor, which provided key insights into their binding affinities and interaction patterns.\u003c/p\u003e\u003cp\u003eThe downloaded file 7QNE contains a co-crystal ligand benzodiazepine, which reveals that Ser206, Ala79, Thr142, Phe77, Tyr210, and Phe100 are affected residues. For the screening of the selected library, perform rigid docking and dock all ligands in the same place as the benzodiazepine. The benzodiazepine co-crystal ligands and interacting residue were used as standards, the interacting amino acids with the receptor are all listed in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eInteraction of the Benzimidazole\u0026ndash;Quinazoline fused ligands.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003eType of interaction\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCompounds\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAtom /group\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBond Type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAmino acid\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBEN01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-F\u003c/p\u003e\u003cp\u003ePh\u003c/p\u003e\u003cp\u003eheterocyclic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehydrogen bond\u003c/p\u003e\u003cp\u003eπ-interaction\u003c/p\u003e\u003cp\u003eπ-interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSer206\u003c/p\u003e\u003cp\u003ePhe77, Tyr210\u003c/p\u003e\u003cp\u003eThr207, His102, Val203\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBEN02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-F\u003c/p\u003e\u003cp\u003ePh\u003c/p\u003e\u003cp\u003eheterocyclic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehydrogen bond\u003c/p\u003e\u003cp\u003eπ-interaction\u003c/p\u003e\u003cp\u003eπ-interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLys156\u003c/p\u003e\u003cp\u003eVal203, Tyr58, Phe77, Tyr160\u003c/p\u003e\u003cp\u003eTyr58, Val203\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBEN03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePh\u003c/p\u003e\u003cp\u003eheterocyclic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003epi-interaction\u003c/p\u003e\u003cp\u003epi-interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAla79, Phe77, Tyr210, His102\u003c/p\u003e\u003cp\u003eTyr210, Phe77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e4\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBEN04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePh\u003c/p\u003e\u003cp\u003eheterocyclic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eπ-interaction\u003c/p\u003e\u003cp\u003eπ-interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePhe77, Tyr58, Ala79, Thr207, Tyr210, His102\u003c/p\u003e\u003cp\u003eTyr160, Phe77, Tyr210\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e5\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBEN05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePh\u003c/p\u003e\u003cp\u003eheterocyclic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eπ-interaction\u003c/p\u003e\u003cp\u003epi-interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTyr58, Phe77, Tyr210\u003c/p\u003e\u003cp\u003eTyr160, Phe77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBEN06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-F\u003c/p\u003e\u003cp\u003ePh\u003c/p\u003e\u003cp\u003eheterocyclic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehydrogen bond\u003c/p\u003e\u003cp\u003epi-interaction\u003c/p\u003e\u003cp\u003epi-interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSer205\u003c/p\u003e\u003cp\u003ePhe77, His102, Val203, Tyr210\u003c/p\u003e\u003cp\u003eTyr210, Phe77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBEN07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-F\u003c/p\u003e\u003cp\u003ePh\u003c/p\u003e\u003cp\u003eHeterocyclic\u003c/p\u003e\u003cp\u003e-OCH3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehydrogen bond\u003c/p\u003e\u003cp\u003epi-interaction\u003c/p\u003e\u003cp\u003epi-interaction\u003c/p\u003e\u003cp\u003epi-interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePhe77\u003c/p\u003e\u003cp\u003eHis102, Tyr210, Phe77\u003c/p\u003e\u003cp\u003ePhe77, Tyr210\u003c/p\u003e\u003cp\u003eTyr58, Ala79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e8\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBEN08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePh\u003c/p\u003e\u003cp\u003eheterocyclic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003epi-interaction\u003c/p\u003e\u003cp\u003epi-interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePhe77, Tyr210, His102, Val203\u003c/p\u003e\u003cp\u003ePhe77, Tyr210\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e9\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBEN09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-F\u003c/p\u003e\u003cp\u003ePh\u003c/p\u003e\u003cp\u003eHeterocyclic\u003c/p\u003e\u003cp\u003e-I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehydrogen bond\u003c/p\u003e\u003cp\u003epi-interaction\u003c/p\u003e\u003cp\u003epi-interaction\u003c/p\u003e\u003cp\u003epi-alkyl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSer206\u003c/p\u003e\u003cp\u003ePhe77, Tyr210\u003c/p\u003e\u003cp\u003ePhe77, Tyr210\u003c/p\u003e\u003cp\u003eVal203, His102, Val212\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBEN10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-F\u003c/p\u003e\u003cp\u003ePh\u003c/p\u003e\u003cp\u003eHeterocyclic\u003c/p\u003e\u003cp\u003e-C(CH3)3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehydrogen bond\u003c/p\u003e\u003cp\u003epi-interaction\u003c/p\u003e\u003cp\u003epi-interaction\u003c/p\u003e\u003cp\u003epi-alkyl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSer206\u003c/p\u003e\u003cp\u003ePhe77, Tyr210 His102\u003c/p\u003e\u003cp\u003ePhe77, Tyr210\u003c/p\u003e\u003cp\u003eVal203, His102, Tyr210\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBEN11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-F\u003c/p\u003e\u003cp\u003ePh\u003c/p\u003e\u003cp\u003eHeterocyclic\u003c/p\u003e\u003cp\u003e-CH3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ehydrogen bond\u003c/p\u003e\u003cp\u003epi-pi interaction\u003c/p\u003e\u003cp\u003epi-pi interaction\u003c/p\u003e\u003cp\u003epi-alkyl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSer206\u003c/p\u003e\u003cp\u003ePhe77, Tyr210\u003c/p\u003e\u003cp\u003ePhe77, His102, Tyr210\u003c/p\u003e\u003cp\u003eVal203, Val212\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBEN12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePh\u003c/p\u003e\u003cp\u003eHeterocyclic\u003c/p\u003e\u003cp\u003e-Br\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003epi-interaction\u003c/p\u003e\u003cp\u003epi-interaction\u003c/p\u003e\u003cp\u003epi-alkyl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHis102, Phe77\u003c/p\u003e\u003cp\u003eTyr160, Phe77, Tyr160\u003c/p\u003e\u003cp\u003eTyr58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e13\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBEN13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePh\u003c/p\u003e\u003cp\u003eHeterocyclic\u003c/p\u003e\u003cp\u003e-Br\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003epi-interaction\u003c/p\u003e\u003cp\u003epi-interaction\u003c/p\u003e\u003cp\u003epi-alkyl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePhe77, Tyr210, His102\u003c/p\u003e\u003cp\u003ePhe77, Tyr210\u003c/p\u003e\u003cp\u003eHis102, Tyr58, Ala79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBEN14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePh\u003c/p\u003e\u003cp\u003eHeterocyclic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003epi-interaction\u003c/p\u003e\u003cp\u003epi-pi interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHis102, Phe77\u003c/p\u003e\u003cp\u003eTyr210, Tyr160, Phe77\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBEN15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePh\u003c/p\u003e\u003cp\u003eHeterocyclic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003epi-interaction\u003c/p\u003e\u003cp\u003epi-interaction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePhe77, His102, Tyr210, Val203\u003c/p\u003e\u003cp\u003ePhe77, Tyr210\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eAfter docking, the binding free energies (ΔG) ranged from (\u0026ndash;9.7 to \u0026minus;\u0026thinsp;12.8) kcal/mol and inhibition constants (Ki) from (0.38 to 72.51) nM are listed in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The ligands BEN01, BEN03, BEN04, BEN06, BEN05, BEN06, BEN08, BEN11, BEN13, BEN14 and BEN15 showed the strongest binding affinities, forming multiple vital interactions such as (π\u0026ndash;π stacking, hydrogen bonding and π-alkyl) contacts with critical receptor common residues including Phe77, His102, Tyr210, Thr207, and Ser206.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThermodynamic \u0026amp; Efficiency Profiling of Benzimidazole-Quinazoline Fused Ligands.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLigands\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eΔG\u003c/p\u003e\u003cp\u003e(kcal/mol)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKi\u003c/p\u003e\u003cp\u003e(nM)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLE\u003c/p\u003e\u003cp\u003e(kcal/mol/HA)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLipE\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLEI\u003c/p\u003e\u003cp\u003e(kcal/mol/Da)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eΔH\u003c/p\u003e\u003cp\u003e(est. 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\u003e\u003cb\u003eBEN01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-12.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e00.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0445\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-12.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-09.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e72.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-09.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-12.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e00.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0435\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-12.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-11.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e03.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0341\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-11.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-11.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e08.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-11.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN06\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-11.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e01.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-11.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN07\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-10.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-10.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN08\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-11.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e02.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-11.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN09\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-10.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e31.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0281\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-10.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-09.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-09.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN11\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-11.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e04.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0454\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-11.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-10.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e09.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0277\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-10.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN13\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-11.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e02.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-11.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-11.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e02.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-11.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBEN15\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-11.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e02.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0493\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-11.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFluorine substitutions present in several ligands enhanced binding stability via hydrogen bonding. They contributed to favourable pharmacokinetic properties, such as blood-brain barrier (BBB) permeability and metabolic stability, which are critical for CNS-targeted agents. The other properties, such as ligand efficiency (LE), lipophilic efficiency (LipE), and ligand efficiency indices (LEI), support the drug-like nature of these compounds. The presence of fluorine in these scaffolds offers the advantage of fluorine-[\u003csup\u003e18\u003c/sup\u003eF] labelling and makes these ligands promising candidates as PET imaging agents for the diagnosis of epilepsy. According to the docking data, the BEN01 emerged as the most promising scaffold with the highest binding affinity (ΔG = \u0026minus;\u0026thinsp;12.8 kcal/mol), lowest Ki (0.38 nM), and strong interactions with Phe77, Tyr210, and His102. Other promising ligands like BEN03, BEN06, BEN08, and BEN15 also showed excellent binding profiles. (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Molecular Dynamics Simulation\u003c/h2\u003e\u003cp\u003eTo analyse the physical movement and stability of our ligand\u0026ndash;protein complexes(BEN01-7QNE) and Cocrystal Ligands-Protein complex (Benzodiazapine-7QNE), molecular dynamics (MD) simulations were carried out with the iMODS online web server. Particularly, Normal Mode Analysis (NMA) was carried out to explore the structural flexibility and the stability of the docked complexes. The outcome of NMA-mobility, as displayed in Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e9\u003c/span\u003e and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e10\u003c/span\u003e, indicates high mobility in the docked complexes, emphasising the structural flexibility of the GABA\u003csub\u003eA\u003c/sub\u003e with BEN01 and Benzodiazepine (standard). The B-factor and deformability plots exhibit peaks at protein regions with increased flexibility, and taller peaks have increased deformability. The eigenvalues derived from the NMA also indicate the stiffness of motion in the protein\u0026ndash;ligand complex. The lower eigenvalues indicate higher structural flexibility and improved complex stability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe BEN01 and Benzodiazepine complexes exhibit eigenvalues of 1.158721e-04 and 1.153432e-04, respectively, indicating favorable flexibility and stable molecular motions due to binding at the precise location. The covariance matrix further supports these findings by showing the correlation between residue motions in the complexes. In the matrix, red represents correlated motion and white represents uncorrelated motion. Together, our NMA and MD simulation results demonstrate that the docked complexes are highly deformable and flexible, especially in regions crucial for ligand binding. Based on the potential interactions of the selected proteins with GABA\u003csub\u003eA\u003c/sub\u003e BEN01/Benzodiazepine, we suggest that these compounds are promising candidates for drugs aimed at enabling highly bioavailable brain imaging. However, further in vitro and in vivo studies are required to evaluate the toxicity and therapeutic activity of GABA\u003csub\u003eA\u003c/sub\u003e-BEN compounds are potent scaffolds for target-specific epileptic brain imaging.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThe selected virtual library BEN01-15 is screened using various parameters, including DFT calculations, ADMET profiling, target prediction, molecular docking, and dynamics. The present study demonstrates that several fluorine-substituted BEN-series ligands exhibit strong binding affinity and favorable interaction profiles with the GABA\u003csub\u003eA\u003c/sub\u003e receptor, a key target in neurological conditions such as epilepsy. The presence of the fluorine atom (-F) in many of these ligands enhances binding through hydrogen bonding and pi-interactions, while also making them suitable candidates for PET/CT-based brain imaging, particularly using fluorine-18 as a radionuclide. The findings of this study reveal that BEN01, BEN05, BEN07, BEN08, and BEN15 are the most promising and potent scaffolds from the selected virtual library. BEN02 was excluded in the final stage due to its very low energy gap caused by a volatile compound and was rejected during ADMET profiling. Although BEN03 and BEN06 also exhibit high binding energy, they are not considered potent scaffolds based on their target prediction and global index parameter values, which are not suitable for creating an effective radiopharmaceutical for epileptic brain imaging. Both BEN01 and BEN15 may be potent and will undergo further screening in a real scenario, with results published soon. Overall, this work supports the development of fluorinated GABA-targeting benzimidazole\u0026ndash;quinazoline fused derivatives, identified as potentially promising radiopharmaceuticals for PET-based imaging research in epileptic regions and other neurological disorders.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge institutional support for facilitating the computational and radiopharmaceutical research infrastructure for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVaibhav Pandey:\u003c/strong\u003e Methodology, Investigation, Formal analysis. \u003cstrong\u003eMohd. Faheem:\u003c/strong\u003e Validation, Investigation. \u003cstrong\u003eAlok Kumar:\u003c/strong\u003e Validation, Conceptualization, and \u003cstrong\u003eManish Dixit\u003cstrong\u003e:\u003c/strong\u003e\u003c/strong\u003e Supervision, Funding acquisition, Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe acknowledge the Indian Council of Medical Research, New Delhi, India (ICMR-DHR; Grant Number: 11013/24/2021-GIA/HR), for funding support of this work. V. P. acknowledges fellowship with this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNA\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKoutroumanidis M, Stavropoulou-Deli A, Giannakopoulou A, Kostopoulos GK. 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Chem Phys Impact. 2024;8:100509.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"In silico drug design, Fluorinated ligands, Radiopharmaceuticals, PET imaging, GABA receptor","lastPublishedDoi":"10.21203/rs.3.rs-7160073/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7160073/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEpilepsy is a neurological disorder that originates from an electrical imbalance in the brain. According to the data, approximately. ~50 millionpeople are affected by this disease. Exploring the epileptic region of the brain using positron emission tomography/computed tomography (PET/CT) is beneficial to medical research and can lead to accurate treatment. Many radiopharmaceuticals are used to explore the brain using PET scans, which are verycost-effective. This article aims to design potent scaffolds based on a benzimidazole-quinazoline fused skeleton and systematically screen them through a multi-parameter \u003cem\u003ein silico\u003c/em\u003e approach involving DFT calculations, ADMET profiling, target prediction, molecular docking, and molecular dynamics simulation. The GABA\u003csub\u003eA\u003c/sub\u003e receptor is a crucial molecular target in epileptic conditions, and this study reveals that fluorine-substituted ligands have substantial binding affinities. In this study, it was observed that the fluorine (-F) atoms improved binding through hydrogen bonding and π-π interactions. The potent scaffolds can be radiolabeled with [\u003csup\u003e18\u003c/sup\u003eF] fluorine atom to make suitable candidates for PET brain imaging. After screening the most promising scaffolds BEN01, BEN05, BEN07, BEN08, and BEN15, are shows the best physicochemical and pharmacokinetic characteristics. According to this study fluorinated benzimidazole\u0026ndash;quinazoline fused ligands have the potential to be employed as radiopharmaceutical scaffolds for PET-based imaging of epileptic brain areas and other neurological disorders.\u003c/p\u003e","manuscriptTitle":"Benzimidazole-Quinazoline Fused Derivativesvia In-Silico Study Approach Targeting GABA A Receptor for PET-based Brain Imaging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-06 08:15:30","doi":"10.21203/rs.3.rs-7160073/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a1ec5d59-1305-4484-be52-c7a8e59bd531","owner":[],"postedDate":"August 6th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-01T05:08:38+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-06 08:15:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7160073","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7160073","identity":"rs-7160073","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}