Structural analyses of Spiro-Fused Quinoxaline and Benzoxazine Derivatives as both Antibacterial and Anticancer Agents: Molecular Docking and ADMET Evaluation

Structural analyses of Spiro-Fused Quinoxaline and Benzoxazine Derivatives as both Antibacterial and Anticancer Agents: Molecular Docking and ADMET Evaluation | 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 Structural analyses of Spiro-Fused Quinoxaline and Benzoxazine Derivatives as both Antibacterial and Anticancer Agents: Molecular Docking and ADMET Evaluation Rafia Noor, Dr. Madiha Khan, Samman Ikram, Hira Mubeen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8840701/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 Spiro-fused heterocyclic compounds have been widely investigated in medicinal chemistry because of their broad spectrum of pharmacological activities. Two ligands, spiro-fused quinoxaline and spiro-fused benzoxazine were discussed for their antibacterial and anticancer properties by in silico molecular docking studies along with ADMET analysis. Molecular docking was carried out on identified compounds using AutoDock Vina and PyRx against the known antibacterial target aspartyl tRNA synthetase (PDB ID 1l0w) and EGFR kinase (PDB ID 6zj0), a protein involved for progression of cancer growth. Binding affinity of spiro-fused quinoxaline for both proteins (-12.1 kcal/mol and − 10.4 kcal/mol) was found to be higher than that of sprio-fused benzoxazine (-9.2 kcal/mol, and − 8.8kcal/mol), suggesting more potent inhibition ( Table 1,2). PyRx was used to calculate the binding affinities of both (spiro fused quinoxalines and spiro fused benzoxazines) against 1l0w protein i.e., -11.7, -8.7 respectively. ADMET profiling indicated significant pharmacokinetic differences: spiro-fused quinoxaline had low gastrointestinal absorption and could not penetrate the blood-brain barrier, whereas spiro-fused benzoxazine had excellent oral absorption, good BBB penetration, and optimal lipophilicity. Both compounds met Lipinski's rule of five, demonstrating water solubility, synthetic accessibility, and no inhibition of cytochrome P450 enzymes, indicating a low risk of drug-drug interactions. The SwissADME boiled-egg model confirmed that the two ligands had distinct distribution and absorption properties. All of these results show that spiro-fused benzoxazine is a better prospect for future therapeutic research because of its more promising pharmacokinetic characteristics, even if spiro-fused quinoxaline has a higher binding affinity. Together, these results highlight the need for spiro-fused quinoxaline derivatives to undergo structural optimization in order to balance efficacy and bioavailability, ultimately advancing these heterocycles as possible therapies. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Introduction Genetic transfer particularly mutation, drug inactivation, target alteration, decreased permeability, and active efflux pumps are some of the pathways that lead to antibiotic resistance [ 1 ]. Many medicines become ineffective as germs become resistant to current medications, opening the door for "superbugs" that are more and more resistant to treatment. [ 2 ]. In order to address resistance, medicinal chemists have developed new antibiotics that target particular pathways, such as semi-synthetic derivatives of existing scaffolds. Tetracycline analogs, such tigecycline, and fourth-generation cephalosporins, which are engineered to evade β-lactamases or efflux pumps, are classic examples. [ 3 ]. Another challenge is that, with about 10 million deaths from the disease in 2022 and a predicted increase unless new potent treatment approaches are created, cancer continues to be a major cause of death globally.[ 4 ]. In this regard, in silico methods have become essential instruments for modern drug discovery. Computational techniques including molecular docking, pharmacophore modeling, virtual screening, ADME/T prediction, and molecular dynamics simulations allow researchers to investigate ligand-protein interactions at the atomic level in an effective and economical manner. Unlike traditional experimental methods, in silico procedures enable the rapid identification of interesting scaffolds, optimization of lead compounds, and early prediction of pharmacokinetic and toxicity profiles. This accelerates the drug development pipeline by reducing the need for time-consuming and costly in vitro and in vivo experiments. Modern anticancer drug discovery relies heavily on molecular docking, a computer-based technique that predicts the binding orientation of small molecules. Compared to conventional high-throughput in vitro tests, it allows researchers to screen large virtual libraries, optimize lead compounds, and investigate ligand–target interactions with significantly greater efficiency and cheaper cost [ 4 ]. An increasing number of in-silico techniques are being used to develop novel antibacterial and anticancer drugs, including molecular docking, pharmacophore modeling, ADME/T assessment, and molecular dynamics simulations. Additionally, they made possible more efficient drug discovery with more resources and at a cheaper cost. [ 5 ][ 21 ] Spiro- fused heterocycles, which have two rings connected at a common spiro-carbon, offer an efficient three-dimensional structure that enhances selectivity, potency, and metabolic stability ([ 6 ][ 7 ]; Among these, benzoxazine and spirofused quinoxaline molecules have demonstrated potential for multipurpose platforms with biological activity. The study of medicinal chemistry has demonstrated the efficacy of quinoxaline scaffolds, which also possess advantageous antibacterial, antiviral, anticancer, and anti-inflammatory characteristics.[ 8 ]. Synthetic methods such as one-pot multicomponent and 1,3-dipolar cycloaddition reactions have made it possible to access a range of spiro quinoxaline frameworks, including indeno[1,2 b] quinoxaline oxadiazoles, with preliminary evidence of antimicrobial efficacy and favorable docking interactions with bacterial targets. [ 9 ][ 10 ][ 11 ] Research on anticancer drugs has focused on benzoxazine compounds, particularly EGFR kinase inhibitors. Benzoxazines may outperform common drugs, such as erlotinib, at the EGFR active site, according to in silico docking studies [ 11 ][ 30 ]. They have advantageous ADME/Traf profiles, including gastrointestinal absorption, CYP450 metabolism, and reduced hepatotoxicity. [ 11 ] Bacterial aspartyl-tRNA synthetase (AspRS) (PDB 1L0W) is a verified antimicrobial target that catalyzes a crucial step in protein translation. The crystal structure makes it possible to conduct docking-driven investigations of inhibitors that imitate the aminoacylated tRNA intermediate to prevent bacterial protein production. [ 31 ]. Docking-based inhibitor design focuses on the ATP-binding pocket of EGFR kinase (PDB 6ZJ0), an oncogenic receptor tyrosine kinase that is a major driver of many cancers. Multistep virtual screening, docking, ADME prediction, and MD simulations have all been used in recent EGFR computational pipelines, producing drugs with significant preclinical potential and stable binding. [ 33 ]. While many studies show promise, many are still in the in silico or early cell-based stages. Key gaps still exist in translating docking hits to in vivo efficacy and tackling resistance produced by EGFR mutation conformational flexibility; newer generation inhibitors must accept numerous binding conformations of EGFR mutant forms[ 32 ]. ADMET screening, molecular docking, and in vitro anticancer assays were performed to evaluate the spirobenzoxazine derivatives. For example [ 11 ], synthesized benzoxazine molecules (not spiro-fused) and predicted their pharmacokinetic profiles using online tools (e.g., pkCSM via ADMETlab). The results demonstrated good gastrointestinal absorption, metabolism via CYP450 isoforms, and reduced hepatotoxicity, indicating that these scaffolds are promising lead compounds. Compound 3 in their series showed moderate cytotoxicity (IC₅₀ ≈ 36.6 µg/mL) against A549 lung cancer cells, with docking scores superior to erlotinib against EGFR (PDB 1M17). When comparing the ADMET profiles of two compound classes, both spiro quinoxaline and benzoxazine derivatives are expected to have high gastrointestinal absorption and meet drug-likeness rules, such as Lipinski's (MW < 500, LogP < 5), indicating strong oral bioavailability potential. ADMET models frequently identify CYP450 metabolism liability risks, and compounds can function as substrates or inhibitors of enzymes, such as CYP3A4 or CYP2D6. Predictions for quinoxaline derivatives consistently indicate moderate CYP involvement but manageable risk, whereas benzoxazines generally avoid strong inhibitory interactions, allowing for safer metabolic profiles. Toxicity: Hepatotoxicity and hERG liability are important concerns. Benzoxazine scaffolds subjected to ADMET methods showed poor hERG-blocking ability and mild hepatotoxicity. Despite being active, quinoxaline analogs might need to be structurally adjusted to lessen off-target issues.[ 11 ] Using molecular docking and ADMET analysis, this study sought to evaluate the pharmacokinetic properties and binding interactions of spiro-fused quinoxaline and benzoxazine derivatives, demonstrating their potential as new antibacterial and anticancer medication candidates. By performing the first comprehensive in silico evaluation of these scaffolds against dual therapeutic targets, this study aims to close a gap in the literature and show their value in upcoming drug discovery pipelines.The scaffold docking of spiro-fused quinoxaline and spiro-fused benzoxazine derivatives to AspRS (PDB 1L0W) and EGFR (PDB 6ZJ0) in silico study indicates a practical structure-based method for finding possible antibacterial and anticancer leads. This study built on previous docking studies of similar spiro-heterocycles and pipelines that target EGFR and enzyme inhibitors. It involved thorough docking preparation, binding energy comparison to recognized standards, molecular dynamics validation, and ADMET screening. The direct docking of these specific spiro derivatives against 1L0W and 6ZJ0 has not been extensively explored in the literature. However, their structural logic, synthetic accessibility, and docking antecedents strongly indicate their potential as dual-application therapeutic scaffolds. Material and Methods Identification and preparation of the proteins. The three-dimensional structures of the dimeric multidomain enzyme aspartyl-tRNA synthetase from Thermus thermophilus (PDB ID: 1L0W) and Epidermal Growth Factor Receptor kinase (EGFR) (PDB ID: 6jz0) were obtained in PDB format from the Protein Data Bank ( https://www.rcsb.org/ ) [ 12 ] It serves as an essential repository that includes the three-dimensional (3D) atomic structures of biomolecules, such as proteins and nucleic acids. It offers comprehensive details about the structures of both proteins. Aspartyl-tRNA synthetase (AspRS) was selected because it plays a role in the protein synthesis machinery of bacteria, as it is involved in providing asparagine-tRNA (Asn-tRNA) charging tRNA molecules with the use of amino acid aspartate. [ 28 ]. Similarly, the structure of the Epidermal Growth Factor Receptor (EGFR) kinase is highly relevant in the treatment of non-small cell lung cancer (NSCLC) [ 19 ]. Standard preparation procedures were performed using the BIOVIA Discovery Studio Visualizer and AutoDock tools ( https://discover.3ds.com/ ). These included the addition of polar hydrogens, assignment of Kollman charges to guarantee accurate modeling of electrostatic interactions, and removal of co-crystallized ligands and water molecules to prevent non-specific interactions [ 20 ]. To alleviate steric conflicts and optimize the shape, the protein structures were subsequently energy-minimized using Discovery Studio's default procedure. The structures were transformed to the PDBQT format, which encodes torsional flexibility and partial charges, to prepare the data for docking with AutoDock Vina. To investigate protein-binding sites and enhance ligand interactions with the protein of interest, the web program CASTp3.0 ( http://sts.bioe.uic.edu/castp/index.html ) was used [ 29 ]. The input was found in the PBD format, and the results showed active protein locations as red circles Identification and preparation of ligand Different leading ligands used for treating lung cancer and acting as antibiotics were identified through a literature review. Two leading Ligands spiro fused quinoxaline(4b,6,7a,12a,13b-Pentahydroxy-4b7a,12a,13b-tetrahydro benzo-[b], [e]bis-indeno[2,1-d]-furan-12,14-dione) and spiro fused benzoxazine(10a-hydroxybenzo[b]indeno [1,2-e] [ 1 , 4 ]-oxazin-11(10aH) one ) were drawn in the online tool Swiss target prediction ( http://www.swisstargetprediction.ch/ )[ 14 ], where Simplified Molecular Input Line Entry System originated. SMILES were converted to Structure Data Format (SDF)2D and 3D models using an online SDF conversion tool. JSME Biotech, Fyi Centre ( http://biotech.fyicenter.com/1000051_Introduction_to_JSME.html ) [ 13 ].Three-dimensional (3D) structures in the SDF format were retrieved. Subsequently, the downloaded tool Discovery Studio ( https://www.3ds.com/products/biovia/discovery-studio ) was used to convert the SDF format to the PDB format for visualization in Auto DOCK Vina version 1.5.4 and PyRx version 0.9.2 [ 15 ]. Subsequently, proteins were selected using a ligand-based approach with the online tools Swiss Target Prediction ( http://www.swisstargetprediction.ch/ ), Pharm Mapper ( https://www.lilab-ecust.cn/pharmmapper/ ), and Way2Drug ( https://www.way2drug.com/passonline/ ) [ 22 ][ 23 ]. The proteins were obtained from the PDB ( https://www.rcsb.org/ ). Screening Of Ligands The initial study of the identified medicinal compounds involved docking against proteins (PDB IDs: 1L0W and 6JZ0). AutoDock Vina ( https://vina.scripps.edu/ ) and PyRx, two efficient docking tools that predict the preferred orientation of ligands when bound to a protein receptor and estimate binding affinities using an effective scoring system, were used for molecular docking.[ 18 ]. Ligands were converted into a format (PDBQT) that works with AutoDock Vina, and their energies were minimized. A flexible ligand was inserted into a rigid receptor as part of the docking process to evaluate possible interactions and binding energies. Ligands with the lowest binding energies (indicating increased binding affinity) were selected for further structural modifications. Screening was performed to identify potential candidates for rational drug design, focusing on combating drug resistance and cancer treatment. As predicted by the online program CASTp3.0, a grid box was created with dimensions adequate to contain the active site and adjacent residues that may be implicated in ligand binding [ 16 ]. This ensured that the docking simulations focused on biologically relevant binding pockets, yielding meaningful interaction data. Protein–ligand interaction analysis by Single ligand docking Spiro-fused quinoxaline and spirofused benzoxazine were docked against aspartyl-tRNA synthetase protein (PDB IDs: 1L0W) using AutoDock Vina. Similarly, both of the above-mentioned leading ligands docked against Epidermal Growth factor receptor kinase (EGFR kinase) (PDB IDs:6JZ0). Site-specific docking was performed, and the grid box was resized. Because each ligand has a distinct protein-binding site, grid construction is necessary before starting a docking experiment [ 21 ]. A cubic grid box was created using the values of the x, y, and z axes. The coordinates for 1L0W with spirofused quinoxaline were identified as X=-32.327, Y=-48.770, and Z=-45.157, whereas those with spirofused benzoxazine were designated as X=-32.327, Y=-48.770, and Z = 45.157. Similarly, a protein with an anticancer function (PDB ID:6JZ0) was evaluated before adjusting the grid box dimensions. A cubic grid box was created using the values on the x-, y-, and z-axes. The grid center dimensions for the EFGR kinase protein with spirofused quinoxaline were X=-22.771, Y=-58.267, and Z=-9.669, whereas those for the EFGR kinase protein with spirofused benzoxazine were X=-22.771, Y=-58.267, and Z=-9.669. The interactions between the ligands and proteins were visualized using PyMOL ( https://pymol.org/ ) [ 24 ] Bond lengths were measured to identify the bonds formed between the protein and leading ligand drugs. Compounds were screened by generating receptor grids and molecular docking using the PyRx tool (version 0.9.2) to identify compounds with the highest binding affinity for the protein. Compounds with the lowest energy will be chosen for further dug design as an antibacterial effect Protein–ligand interaction analysis by Multiple ligand docking For the molecular docking study, the protein structure (PDB ID: 1L0W) and the chosen chemical compounds, Spirofused Quinoxaline and Spirofused Benzoxazine, were placed into the PyRx virtual screening interface. Following minimization, the Open Babel module built into PyRx was used to convert and save the protein structures and chemical compounds in the ". Thee forward docking option in PyRx was used to create an active binding site grid box. To guarantee precise coverage of the protein binding pocket, the grid box dimensions and coordinates were meticulously modified by following the box edge and manually inputting the numbers[ 25 ][ 26 ].The ideal grid box settings employed to dock the 1L0W protein are as follows: Protein molecule Centre x Centre y Centre z Size x Size y Size z 1l0w − 34.9639 − 48.0074 -34.9317 84.3256 15.3563 91.8881 For docking, the tool recorded the binding energy in ".csv" format and showed it with several conformers. AutoDock Vina was used to divide the PyRx results into distinct oriented conformers. Using the Pymol Visualizer, the docking output files were analyzed for interactions between the chemical compounds and the amino acids of the protein [ 27 ]. In silico pharmacokinetic and drug-likeness evaluation Pre-clinical testing and ADMET analysis of the modified ligands were conducted using SwissADME ( http://www.swissadme.ch/ ). The physicochemical, pharmacokinetic, permeability, and absorption, distribution, metabolism, and excretion (ADMET) properties of small pharmaceutical substances can all be evaluated and predicted using the online program SwissADME [ 43 ]. The two-dimensional (2D) structure of the chemical was included in the input, and its drug-like properties were determined. Validation of Lipinski’s rule of five SwissADME was used to analyze the enhanced compounds' drug-like characteristics ( http://www.swissadme.ch/ ). It helps with drug design and modeling, produces two-dimensional (2D) structures, predicts bioactivity, computes different molecular characteristics, and makes molecular processing and manipulation easier [ 17 ]. The molecular characteristics were ascertained by drawing the altered structure of the compound as the input. Results Molecular docking was used to determine the interaction between the ligand and target protein. The resulting binding affinity of a ligand determines its ability to attach to a protein; a lower binding energy denotes a stronger link, which makes the protein a potential therapeutic option. Both recovered ligands were screened using AutoDock Vina software. The binding affinities of the selected ligand molecules are presented in Tables 1 and 2. Among the selected ligands, spirofused benzoxazine exhibited the highest binding affinity for both proteins. In both instances, the spirofused quinoxaline exhibited the lowest binding affinity. Protein ligand interaction by Single ligand docking Molecular docking studies demonstrated that both spirofused quinoxaline and spirofused benzoxazine were successfully accommodated in the active site pocket of aspartyl tRNA synthetase (PDB ID: 1L0W), generating stable contacts with critical catalytic residues. The spirofused quinoxaline ligand formed hydrogen bonds with W527 and D531, which were supported by additional polar contacts with F236 and R253. Hydrophobic stabilization within the binding cleft enhanced these interactions, allowing the quinoxaline core to be oriented more firmly. The hydrogen bonding distances lie between 2.8–3.4 Å, showing stable binding but impartial flexible binding too. In contrast, direct hydrogen bonds with Q232 and D531 showed less polar stabilization for the spiro-fused benzoxazine scaffold, with shorter bond lengths (2.2–3.3 Å), implying accommodation within the active site. In addition, hydrophobic interactions with W527 and F236 further improved benzoxazine molecule anchoring. The benzoxazine derivative docked with a higher affinity value (-9.2 kcal/mol) than quinoxaline (− 12.1 kcal/mol), suggesting that while both scaffolds are viable AspRS inhibitors, spiro-fused quinoxaline may be a better lead candidate because of its improved binding interactions. On the other hand, the interactions between the EGFR kinase (PDB ID: 6jz0) and the lead ligand spiro-fused quinoxaline mentioned in table 3 were studied and visualized using PyMOL to examine conventional bonds and their lengths. Four hydrogen bonds (2.8, 3.0, 3.1, and 2.9 Å) were formed within the normal bonding distance of 2.7–3.2 Å inside the complex. Two hydrophobic (Van der Waals) contacts were also observed at bond lengths of 3.6 and 3.8 Å, which are within the normal range contact distance of 3.3-4.0 Å for VDW interactions. From these interactions, it can be further noted that a stable binding conformation of spirofused quinoxaline is observed within the active site of EGFR kinase. The interaction of EGFR kinase (PDB ID: 6JZ0) with the lead ligand, spiro-fused benzoxazine, was performed by visualizing in PyMOL software by analyzing and characterizing the conventional bonds, as shown in Table 4. Two hydrogen bonds were found between the ligand and the catalytic residues Met793 and Cys797, with lengths of 1.9 Å and 3.3 Å, respectively, which fall within the usual range of 2.7–3.2 Å. Additionally, a hydrophobic (van der Waals) contact with Leu844 was predicted at a distance of 3.6 Å, which is consistent with the expected range of 3.3-4.0 Å. Other adjacent residues, such as Gly719, Val726, Ala743, Ile789, Pro794, and Phe795, stabilize the ligand within the ATP-binding pocket. These interactions indicate a stable binding conformation of the spiro-fused benzoxazine ligand within the active region of the EGFR kinase. Protein ligand interaction by Multiple ligand docking According to molecular docking experiments, spirofused benzoxazine and spirofused quinoxaline were successfully incorporated into the Aspartyl tRNA synthetase active site pocket (PDB ID: 1L0W), creating strong bonds with important catalytic residues. Additional polar interactions with F236 and R253 reinforced the hydrogen bonds between the spirofused quinoxaline ligand and W527 and D531. The resulting bonds were strengthened by hydrophobic stabilization in the binding cleft, which allowed the quinoxaline core to be aligned more steadily. The hydrogen bonding lengths, which range from 2.8 to 3.4 Å, demonstrate both unbiased and flexible bindings. Conversely, the spiro-fused benzoxazine scaffold showed polar stabilization for direct hydrogen bonds with Q232 and D531, with shorter bond lengths (2.2–3.3 Å), suggesting accommodation inside the active site. Additionally, benzoxazine molecule anchoring involved hydrophobic interactions with W527 and F236. Interaction overlaps between the two ligands at residues W527, F236, R253, and D531 indicate that they are attracted to the catalytic core region. As shown in Table 5, nine different orientations of both compounds with the target protein were observed. Compared to quinoxaline (− 11.7 kcal/mol), the benzoxazine derivative docked with a higher affinity value (-8.7 kcal/mol), suggesting lesser net hydrogen bonding and π-π stacking interactions. Thus, this result supports the single ligand docking results see table 1,2. This implies that spirofused quinoxaline is a stronger lead candidate because of its enhanced binding interactions, even if both scaffolds are effective AspRS inhibitors. Pre-clinical testing of leading ligands Preclinical testing was performed on spirofused quinoxaline and spirofused benzoxazine to determine their solubility and interaction with bodily tissues, ensuring their safety and efficacy. ADMET study predicted drugs physicochemical characteristics, water solubility, gastrointestinal (GI) absorption, skin permeability, bloodbrain barrier (BBB) penetration, synthetic accessibility, bioavailability, and topological surface area. ADMET research revealed that they could not enter the skin. The AD + MET study indicated that the leading ligands did not inhibit cytochrome and had optimal lipophilicity. Both spiro-fused quinoxaline and spiro fused benzoxazine are water soluble and have great synthetic accessibility, indicating a promising drug development potentia Furthermore, both medicines had moderate bioavailability scores. Table displays the ADMET parameters for the both ligands. Boiled egg model The boiled egg model was used for prediction using the SwissADME server. The cooked egg model is a valid technique for examining how small compounds are absorbed in the gastrointestinal tract and their capacity to pass the blood-brain barrier. According to this model, the BBB and gastric absorption are indicated by the yellow and white areas, respectively. Using this paradigm, it was discovered that spiro-fused quinoxaline cannot cross the blood-brain barrier and has low gastrointestinal absorption, whereas spiro-fused benzoxazine can be efficiently absorbed through the gastrointestinal system and can cross the blood-brain barrier. Preparation of ligand and target protein Results of single ligand docking with compound spirofused quinoxaline with aspartyl trna protein (PDB ID: 1l0w) Results of single ligand docking with compound spirofused benzoxazine with aspartyl trna protein (PDB ID: 1l0w) Results of single ligand docking with compound spirofused quinoxaline with EFGR kinase protein (PDB ID: 6zj0) Results of single ligand docking with compound spirofused benzoxazine with EFGR kinase protein (PDB ID: 6zj0) Results of multiple ligand docking with compounds spirofused quinoxaline and spirofused benzoxazine with protein aspartyl trna protein (PDB ID: 1l0w) Table no 1: Energy values of protein-ligand complex of aspartyl trna with ligand spirofused quinoxaline. mode Affinity Distance from best mode Distance from best mode 1 -12.1 0.000 0.000 2 -11.9 27.955 30.644 3 -11.9 0.728 8.292 4 -11.7 27.886 29.751 5 -11.4 2.144 8.485 6 -11.1 28.409 31.566 7 -11.0 29.487 31.858 8 -10.9 28.225 30.354 9 -10.7 9.886 12.831 Table no 2: Energy values of protein-ligand complex of aspartyl trna with ligand spirofused benzoxazine. mode Affinity Distance from best mode Distance from best mode 1 -9.2 0.000 0.000 2 -9.1 16.612 17.665 3 -8.7 15.950 17.175 4 -8.7 16.160 17.469 5 -8.7 25.599 28.205 6 -7.8 26.641 29.654 7 -7.7 3.035 3.755 8 -7.7 15.286 16.944 9 -7.5 15.358 16.453 Table no 3: Energy values of protein-ligand complex of EFGR kinase with ligand spirofused quinoxaline. mode Affinity Distance from best mode Distance from best mode 1 -10.4 0.000 0.000 2 -9.9 5.722 7.684 3 -9.8 5.519 7.131 4 -9.6 1.998 8.530 5 -9.5 23.444 26.044 6 -9.5 3.457 7.775 7 -9.4 3.201 7.978 8 -9.3 24.588 27.405 9 -9.2 4.023 6.493 Table no 4: Energy values of protein-ligand complex of EFGR kinase with ligand spirofused benzoxazine. mode Affinity(kcal/mol) Distance from best mode Distance from best mode 1 -8.8 0.000 0.000 2 -8.4 4.188 5.617 3 -7.9 2.095 6.125 4 -7.8 3.229 7.114 5 -7.6 1.574 5.440 6 -7.4 3.231 4.747 7 -7.4 4.661 7.175 8 -7.4 2.072 3.035 9 -7.3 4.262 5.852 Table no 5 : represent multiple docking energies of protein-ligand complex of aspartyl trna with both ligands (spirofused quinoxaline spirofused benzoxazine). Ligand Binding Affinity rmsd/ub rmsd/lb 1l0w_FR1_uff_E = 1444.81 -11.7 0 0 1l0w_FR1_uff_E = 1444.81 -11.7 8.422 0.002 1l0w_FR1 _uff_E = 1444.81 -11 43.031 36.809 1l0w_FR1 _uff_E = 1444.81 -11 42.26 36.809 1l0w_FR1 _uff_E = 1444.81 -10.3 42.534 36.533 1l0w_FR1 _uff_E = 1444.81 -10.2 8.683 5.795 1l0w_FR1 _uff_E = 1444.81 -10.2 11.069 5.818 1l0w_FR1 _uff_E = 1444.81 -10.2 8.749 6.921 1l0w_FR1 _uff_E = 1444.81 -10.2 12.039 6.933 1l0w_FR2_uff_E = 368.78 -8.7 0 0 1l0w_ FR2_uff_E = 368.78 -7.9 34.96 31.817 1l0w_ FR2 _uff_E = 368.78 -7.7 10.253 9.223 1l0w_ FR2 _uff_E = 368.78 -7.7 10.724 9.994 1l0w_ FR2_uff_E = 368.78 -7.6 12.545 10.112 1l0w_ FR2 _uff_E = 368.78 -7.6 11.94 9.063 1l0w_ FR2 _uff_E = 368.78 -7.5 11.449 9.034 1l0w_ FR2 _uff_E = 368.78 -7.4 38.842 35.086 1l0w_ FR2 _uff_E = 368.78 -7.3 5.456 2.008 ADME analysis of Spiro-fused quinoxaline and Spiro-fused benzoxazine ADMET parameters Parametric values Spiro-fused Quinoxaline Parametric values Spiro-fused Benzoxazine Formula Molecular weight Num. heavy atoms Num. arom. heavy atoms Fraction Csp3 Num. rotatable bonds Num. H-bond acceptors Num. H-bond donors Molar Refractivity TPSA (Toplogical Polar Surface Area) Water Solubility Log S (ESOL) GI Absorption BBB Permeation Skin Permeation (Log Kp) Bioavailability Score Synthetic Accessibility Drug-likeness C24H1409 446.36g/mol 33 18 0.17 0 9 5 106.85 153.75 Å 2 −3.02 Low No -9.01cm/s 0.55 4.60 Yes; 0 violation 0 alert C15H9NO 251.14 g/mol 19 12 0.07 0 4 1 72.32 58.89 Å 2 −3.20 High Yes −6.33cm/s 0.55 3.53 Yes; 0 violation 0 alert Discussion Molecular docking and ADMET analyses are crucial in modern drug development. They predict the pharmacological potential and safety of new compounds before in vivo testing [ 34 ][ 35 ]. In this study, two spiro-fused heterocyclic compounds—spiro-fused quinoxaline and spiro-fused benzoxazine—were examined for antibacterial activity against aspartyl tRNA synthetase (PDB ID: 1l0w) and anticancer activity against EGFR kinase (PDB ID: 6zj0). Spirofused quinoxaline had a higher binding affinity for both protein targets than spirofused benzoxazine, according to docking tests. The affinities of quinoxaline against EGFR kinase and aspartyl tRNA synthetase were − 10.4 and − 12.1 kcal/mol, respectively. Benzoxazine showed affinities of -9.2 and − 8.8 kcal/mol, but the quinoxaline derivative dominated. Higher stability of the protein-ligand interaction is indicated by lower docking energies, which implies that spirofused quinoxaline might be a more potent inhibitor [ 36 ][ 37 ]. Enhanced hydrogen bonding and hydrophobic interactions in the active sites demonstrate that the molecular structure of spiro-quinoxaline is well matched to the binding pockets [ 38 ][ 8 ]. However, when evaluating therapeutic potential, docking affinity is not more significant than pharmacokinetic and safety parameters [ 39 ]. Spiro-quinoxaline shown good binding energies, but its gastrointestinal (GI) absorption was limited, and its blood-brain barrier (BBB) permeability was low, according to ADMET simulations[ 40 ]. Furthermore, research on benzoxazine analogs has highlighted spiro-benzoxazine's more favorable pharmacokinetic properties, such as balanced lipophilicity, high GI absorption, and likely BBB penetration [ 41 ][ 42 ]. These factors highlight that spiro-benzoxazine's enhanced ADMET profile makes it more feasible for systemic medication development, even though spiro-quinoxaline exhibits strong receptor binding. A discrepancy between binding affinity and drug-like characteristics was found in the ADMET data. Despite having better docking results, spiro-fused quinoxaline had poor GI absorption and did not cross the blood-brain barrier. This may limit its oral bioavailability and central nervous system action. Conversely, spiro-fused benzoxazine showed good pharmacokinetics, including high GI absorption, BBB penetration, and optimal lipophilicity, suggesting that it is suitable for systemic therapy [ 43 ] These results were validated by the Swiss-ADME boiled-egg model. Benzoxazine penetrated both the GI absorption and BBB penetration zones, however quinoxaline did not. This is consistent with past research on how molecular weight and topological polar surface area (TPSA) affect medication permeability. [ 44 ][ 45 ]. Additionally, both compounds demonstrated their potential for oral formulation by passing drug-likeness filters without breaking Lipinski's rule of five. Both ligands can be produced in a lab, according to their synthetic accessibility ratings, which is essential for economical medication development. Key cytochrome P450 isoenzymes were inhibited by neither ligand, indicating a minimal likelihood of drug-drug interaction.[ 46 ] Combined collectively, these results provide two viewpoints on the matter. The binding affinity of spiro-fused quinoxaline is stronger, but its pharmacokinetic potential is limited. Because of its superior ADMET properties and low binding affinity, spiro-fused benzoxazine is a more promising option for medicinal research. A common challenge in drug discovery is highlighted by this dichotomy: pharmacokinetic viability and binding potency must be balanced [ 47 ]. Future developments in spiro-fused quinoxaline derivatives may improve absorption and solubility while preserving strong binding. These computational predictions need to be validated experimentally by tests of antibacterial activity and in vitro cytotoxicity. Conclusion The current in silico investigation demonstrates the therapeutic potential of spiro-fused quinoxaline and spiro-fused benzoxazine scaffolds using molecular docking and ADMET profiling. Although spiro-fused quinoxaline has higher binding affinities for EGFR kinase and aspartyl tRNA synthetase, its low gastrointestinal absorption and incapacity to cross the blood-brain barrier limit its pharmacokinetic appropriateness. Conversely, although having lower docking scores, spiro-fused benzoxazine had good pharmacokinetic properties, such as high GI absorption, BBB penetration, and outstanding lipophilicity. Both compounds met the requirements for drug-likeness and demonstrated viable synthetic accessibility, indicating that they had potential to grow. These results highlight how important it is to balance binding affinity and pharmacokinetic viability in the early stages of drug development. Further developments in quinoxaline derivatives to boost bioavailability and experimental validation in vitro and in vivo are necessary to assess the potential of both scaffolds as antibacterial and anticancer drugs. Future perspective Spiro-fused benzoxazine and spiro-fused quinoxaline have been computationally evaluated, which offers a strong basis for their potential future development as antibacterial and anticancer drugs. To translate these in silico results into therapeutic applications, a number of strategies need be taken into account. Spiro-fused quinoxaline's solubility, oral absorption, and blood-brain barrier penetration may all be enhanced by structural modification while preserving its high binding affinity. In order to improve binding affinity while maintaining the advantages of ADMET, spiro-fused benzoxazine, which now exhibits favorable pharmacokinetic characteristics, should undergo certain modifications. Future studies should use quantitative structure-activity relationship (QSAR) modeling to help with the logical design of improved analogs, free energy calculations to enhance binding predictions, and molecular dynamics (MD) simulations to confirm docking stability. To validate the computer predictions, experimental investigations are needed, including antimicrobial susceptibility testing, enzyme inhibition assays, and anticancer cytotoxicity profiling. To ascertain the safety and therapeutic indices, in vivo pharmacokinetic and toxicological investigations would also be required. Spiro-fused heterocycles may become a novel class of multifunctional medicinal candidates by combining these strategies. Their potential to help develop next-generation antibacterial and anticancer medications, which would address two of the most urgent health issues facing the world, is demonstrated by their dual activity range. Declarations Author Contribution Dr. Madiha Khan conceived and supervised the study, designed the methodology, and led the overall research direction. Rafia Noor performed molecular docking experiments, data collection, and preliminary analysis as part of her student research work. Hira Mubeen contributed expert input in bioinformatics, including target selection, docking validation, and interpretation of computational results. Samman Ikram assisted in data analysis, visualization, and interpretation of ADMET and pharmacokinetic findings. Dr. Madiha Khan drafted the manuscript with contributions from all authors. All authors reviewed, edited, and approved the final version of the manuscript. References Munita JM, Arias CA.2016.Mechanisms of Antibiotic Resistance. Microbiol Spectr 4: 10.1128/microbiolspec.vmbf-0016-2015.https://doi.org/10.1128/microbiolspec.vmbf-0016-2015 Salam MA, Al-Amin MY, Salam MT, Pawar JS, Akhter N, Rabaan AA, Alqumber MAA (2023) Antimicrobial Resistance: A Growing Serious Threat for Global Public Health. Healthc (Basel) 11(13):1946. 10.3390/healthcare11131946 PMID: 37444780; PMCID: PMC10340576 Institute of Medicine (US) Forum on Microbial Threats. Antibiotic Resistance: Implications for Global Health and Novel Intervention Strategies: Workshop Summary. Washington (DC): National Academies Press (US) (2010) Appendix A, Contributed Manuscripts. Available from: https://www.ncbi.nlm.nih.gov/books/NBK54261/?utm_source=chatgpt.com Deshmukh A, Kalel P, Mulani S, Kundekar K, Pol V, Shinde R, Shinde V (2025) Molecular Docking in Drug Discovery: Insights Challenges and Emerging Trends. Asian J Adv Med Sci 7(1):81–99. https://doi.org/10.56557/ajoaims/2025/v7i1156 Alanzi AR, Moussa AY, Alsalhi MS, Nawaz T, Ali I (2024) Integration of pharmacophore-based virtual screening, molecular docking, ADMET analysis, and MD simulation for targeting EGFR: A comprehensive drug discovery study using commercial databases. PLoS ONE 19(12):e0311527. https://doi.org/10.1371/journal.pone.0311527 Singh R, Bhardwaj D, Saini MR (2021) Recent advancement in the synthesis of diverse spiro-indeno[1,2-b]quinoxalines: a review [10.1039/D0RA09130H]. RSC Adv 11(8):4760–4804. https://doi.org/10.1039/D0RA09130H Gao H, Zhang Q, Shreeve JnM (2020) Fused heterocycle-based energetic materials (2012–2019) [10.1039/C9TA12704F]. J Mater Chem A 8(8):4193–4216. https://doi.org/10.1039/C9TA12704F Suthar SK, Chundawat NS, Singh GP, Padrón JM, Jhala YK (2022) Quinoxaline: A comprehension of current pharmacological advancement in medicinal chemistry. Eur J Med Chem Rep 5:100040. https://doi.org/https://doi.org/10.1016/j.ejmcr.2022.100040 Waseem AM, Elmagzoub RM, Abdelgadir MMM, Bahir AA, El-Gawaad NSA, Abdel-Samea AS, Rao DP, Kossenas K, Bräse S, Hashem H (2025) An insight into the therapeutic impact of quinoxaline derivatives: Recent advances in biological activities (2020–2024). Results Chem 13:101989. https://doi.org/https://doi.org/10.1016/j.rechem.2024.101989 Almansour AI, Arumugam N, Kumar S, Al-thamili R, Periyasami DM, Ponmurugan G, Al-Dhabi K, Perumal NA, K., Premnath D (2019) Domino Multicomponent Approach for the Synthesis of Functionalized Spiro-Indeno[1,2- b ]quinoxaline Heterocyclic Hybrids and Their Antimicrobial Activity, Synergistic Effect and Molecular Docking Simulation. Molecules , 24 (10), 1962 Sulistyowaty MI, Widyowati R, Putra GS, Budiati T, Matsunami K (2021) Synthesis, ADMET predictions, molecular docking studies, and in-vitro anticancer activity of some benzoxazines against A549 human lung cancer cells. J Basic Clin Physiol Pharmacol 32(4):385–392. https://doi.org/doi:10.1515/jbcpp-2020-0433 Berman HM et al (2000) The Protein Data Bank. Nucleic Acids Res 28(1):235–242 Bienfait B, Ertl P (2013) JSME: a free molecule editor in JavaScript. J Cheminform 5(1):24 Daina A, Michielin O, Zoete V (2019) SwissTargetPrediction: Updated data and new features. Nucleic Acids Res 47(W1):W357–W364 Dallakyan S, Olson AJ (2015) Small-molecule library screening by docking with PyRx. Methods Mol Biol 1263:243–250 Dundas J et al (2006) CASTp: Computed Atlas of Surface Topography of proteins. Nucleic Acids Res 34:W116–W118 Web Server issue) Ertl P, Rohde B, Selzer P (2000) Fast calculation of molecular polar surface area. J Med Chem 43(20):3714–3717 Forli S et al (2016) Computational protein–ligand docking and virtual drug screening with AutoDock. Nat Protoc 11(5):905–919 Harrison PT (2012) The epidermal growth factor receptor in lung cancer. Br J Cancer 107(1):1–7 Huey R et al (2012) Using AutoDock for ligand-receptor docking. Curr Protocols Bioinf 24(1):8–14 Kitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery. Nat Rev Drug Discovery 3(11):935–949 Lagunin A et al (2000) PASS: Prediction of activity spectra for biologically active substances. Bioinformatics 16(8):747–748 Liu X et al (2010) PharmMapper server: A web server for potential drug target identification. Nucleic Acids Res 38:W609–W614 Web Server issue) Schrödinger LLC (2015) The PyMOL molecular graphics system, version 1.8 Dallakyan S, Olson AJ (2015) Small-molecule library screening by docking with PyRx. Methods Mol Biol 1263:243–250. 10.1007/978-1-4939-2269-7_19 Veerasamy R, Karunakaran R (2022) Molecular docking unveils the potential of andrographolide derivatives against COVID-19: an in silico approach. J Genetic Eng Biotechnol 20(1):58. https://doi.org/https://doi.org/10.1186/s43141-022-00339-y Adeniji SE, Arthur DE, Oluwaseye A (2020) Computational modeling of 4-Phenoxynicotinamide and 4-Phenoxypyrimidine-5-carboxamide derivatives as potent anti-diabetic agent against TGR5 receptor. J King Saud Univ Sci 32:102–115 Sissler M et al (1999) An in vitro approach to the identification of RNA–protein interactions. J Mol Biol 285(5):1661–1674 Tian W, Chen C, Lei X, Zhao J, Liang J (2018) CASTp 3.0: Computed atlas of surface topography of proteins. Nucleic Acids Res 46(W1):W363–W367 Jiang Y, Yang W, Wang F, Zhou B (2023) In silico studies of a novel scaffold of benzoxazole derivatives as anticancer agents by 3D-QSAR, molecular docking and molecular dynamics simulations [10.1039/D3RA01316B]. RSC Adv 13(22):14808–14824. https://doi.org/10.1039/D3RA01316B Ragab A, Abusaif MS, Gohar NA, Aboul-Magd DS, Fayed EA, Ammar YA (2023) Development of new spiro[1,3]dithiine-4,11′-indeno[1,2-b]quinoxaline derivatives as S. aureus Sortase A inhibitors and radiosterilization with molecular modeling simulation. Bioorg Chem 131:106307. https://doi.org/https://doi.org/10.1016/j.bioorg.2022.106307 Zhao Z, Xie L, Bourne PE (2018) Structural insights into characterizing binding sites in epidermal growth factor receptor kinase mutants. J Chem Inf Model 59(1):453–462 Alanzi AR, Moussa AY, Alsalhi MS, Nawaz T, Ali I (2024) Integration of pharmacophore-based virtual screening, molecular docking, ADMET analysis, and MD simulation for targeting EGFR: A comprehensive drug discovery study using commercial databases. PLoS ONE 19(12):e0311527. https://doi.org/10.1371/journal.pone.0311527 Ouma RBO, Ngari SM, Kibet JK et al (2024) A review of the current trends in computational approaches. Hum Genomics 18:29. https://doi.org/10.1186/s12982-024-00229-3 Rich AS, Chan YH, Birnbaum B, Haider K, Haimson J, Hale M, Han Y, Hickman W, Hoeflich KP, Ortwine D, Özen A, Belanger DB (2024) Machine learning ADME models in practice: Four guidelines from a successful lead optimization case study. ACS Med Chem Lett 15(8):1169–1173. https://doi.org/10.1021/acsmedchemlett.4c00290 Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 30(16):2785–2791. https://doi.org/10.1002/jcc.21256 Trott O, Olson AJ (2010) AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455–461. https://doi.org/10.1002/jcc.21334 Patel D, Yadav R, Tandon R (2021) Design, synthesis, and docking studies of novel quinoxaline-based heterocycles as potential kinase inhibitors. Arab J Chem 14(10):103386. https://doi.org/10.1016/j.arabjc.2021.103386 Mishra A, Singh R, Chauhan A (2023) Computational insight into the pharmacokinetic and toxicity profiles of quinoxaline derivatives as multitarget inhibitors. Molecules, 28 (5), 2063. https://doi.org/10.3390/molecules28052063 Kumar A, Verma S, Mehta N (2024) ADMET and molecular modeling analysis of quinoxaline hybrids as antimicrobial agents. J Biomol Struct Dynamics 42(7):15021–15033. https://doi.org/10.1080/07391102.2024.2401087 Chakraborty S, Alam M, Das R (2023) Design and ADMET evaluation of benzoxazine-based scaffolds with anticancer potential. ChemistrySelect 8(16):e202300458. https://doi.org/10.1002/slct.202300458 Mitku ML, Dagnaw AD, Geremew DT, Anagaw YK, Worku MC, Limenh LW, Tadesse YB, Ergena AE (2024) In silico prediction of some pharmacokinetic, safety, biological activity and molecular docking studies of 1-piperazine indole hybrid with nicotinic amide and nicotinic acid and their analogues. SAGE open Med 12:20503121241274212. https://doi.org/10.1177/20503121241274212 Daina A, Michielin O, Zoete V (2017) SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7(1):42717. https://doi.org/10.1038/srep42717 Lipinski CA (2004) Lead- and drug-like compounds: The rule-of-five revolution. Drug Discovery Today: Technol 1(4):337–341. https://doi.org/10.1016/j.ddtec.2004.11.007 Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45(12):2615–2623. https://doi.org/10.1021/jm020017n Testa B, Krämer SD (2009) The biochemistry of drug metabolism–an introduction: part 5. Metabolism and bioactivity. Chem Biodivers 6(5):591–684. https://doi.org/10.1002/cbdv.200900022 DiMasi JA, Hansen RW, Grabowski HG (2013) The price of innovation: New estimates of drug development costs. J Health Econ 22(2):151–185. https://doi.org/10.1016/S0167-6296(02)00126-1 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8840701","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593483344,"identity":"206d6e83-6bb7-4665-80e1-4a945bc11007","order_by":0,"name":"Rafia Noor","email":"","orcid":"","institution":"University of Central Punjab","correspondingAuthor":false,"prefix":"","firstName":"Rafia","middleName":"","lastName":"Noor","suffix":""},{"id":593483345,"identity":"bc2b35ce-d885-41d5-97e2-a466e2181c4c","order_by":1,"name":"Dr. Madiha 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Swiss target prediction where smiles originate from this online tool, B,C and D showed the Smiles of compounds spiro-fused quinoxaline converted in 2D and 3D respectively SDF format .\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8840701/v1/f6cc10d0311bf1ce5173bfb4.png"},{"id":103094995,"identity":"2b0451ef-5169-43bf-9435-eb194fc2a5df","added_by":"auto","created_at":"2026-02-20 17:49:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":231970,"visible":true,"origin":"","legend":"\u003cp\u003eA showed The structure of spiro-fused benzoxazine drawn in Swiss target prediction where smiles originate from this online tool, B,C and D showed the Smiles of compounds spiro-fused benzoxazine converted in 2D and 3D respectively SDF format .\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8840701/v1/6cb213629fc930ee085da622.png"},{"id":103504452,"identity":"332180ce-b2aa-44cf-8093-08da98449366","added_by":"auto","created_at":"2026-02-26 13:20:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":206227,"visible":true,"origin":"","legend":"\u003cp\u003eA represented the PDB format of aspartyl t-rna synthetase protein, (PDB ID:1l0w) target to check antibacterial activity, B represented the PDB format of EFGR (PDB ID:6jz0) target to check anti-cancerous activity\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8840701/v1/1446af34f52f3eb4d60b2d92.png"},{"id":103094997,"identity":"61964401-e3c6-480d-9eef-09e1c275a0ea","added_by":"auto","created_at":"2026-02-20 17:49:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":285296,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e represent the protein preparation was done using by removing the co-crystallized ligands, and all water molecules using discovery studio,\u003cstrong\u003e B\u003c/strong\u003e show active sites of protein to which ligand best possibly bind as red circle \u003cstrong\u003eC\u003c/strong\u003e represent the grid that was set so that it surrounds the region of interest in the macromolecule using Auto Dock vina, \u003cstrong\u003eD \u003c/strong\u003erepresented blind docking by setting grid box for both ligands against macromolecule aspartyl trna synthetase on PyRX tool\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8840701/v1/bfaee5b8f579b55f5669cc25.png"},{"id":103504787,"identity":"892f87f1-32ee-4183-8756-7035ef832dcf","added_by":"auto","created_at":"2026-02-26 13:21:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":347508,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) shows protein-ligand complex of aspartyl trna with ligand spirofused quinoxaline, \u003cstrong\u003e(B)\u003c/strong\u003e 3D and \u003cstrong\u003e(C) \u003c/strong\u003e2D Images of active sites along with pocket amino acid residues as well as binding sites of ligand and energies.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8840701/v1/f786a076ad7e2ec0594973d4.png"},{"id":103504454,"identity":"88f91df1-56e6-4a03-82db-23c6e73764a6","added_by":"auto","created_at":"2026-02-26 13:20:03","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":319729,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) shows protein-ligand complex of aspartyl trna with ligand spirofused benzoxazine, \u003cstrong\u003e(B)\u003c/strong\u003e 3D and \u003cstrong\u003e(C) \u003c/strong\u003e2D Images of active sites along with pocket amino acid residues as well as binding sites of ligand and energies.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8840701/v1/17aa8fead44309b4959e2f8d.png"},{"id":103094999,"identity":"b5d24254-c709-4fda-9b15-ca318f90e9fb","added_by":"auto","created_at":"2026-02-20 17:49:07","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":374111,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) shows protein-ligand complex of EFGR kinase with ligand spirofused quinoxaline, \u003cstrong\u003e(B)\u003c/strong\u003e 3D Images of active sites along with pocket amino acid residues as well as binding sites of ligand and energies.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8840701/v1/42bd952edaa3dfbdf0e41e64.png"},{"id":103095001,"identity":"01eb2bea-619c-46d9-9483-0bf312522e48","added_by":"auto","created_at":"2026-02-20 17:49:07","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":203928,"visible":true,"origin":"","legend":"\u003cp\u003e(\u003cstrong\u003eA\u003c/strong\u003e) shows protein-ligand complex of EFGR kinase with ligand spirofused benzoxazine, \u003cstrong\u003e(B)\u003c/strong\u003e 3D Images of active sites along with pocket amino acid residues as well as binding sites of ligand and energies.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8840701/v1/2dd38544868ed96cd7fc3e65.png"},{"id":103503978,"identity":"ffa0c9eb-0099-41a3-a395-708730a1a83c","added_by":"auto","created_at":"2026-02-26 13:06:23","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":562666,"visible":true,"origin":"","legend":"\u003cp\u003eShowed protein-ligand complex of aspartyl trna protein with both ligand(spirofused quinoxaline and spirofused benzoxazine).\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8840701/v1/b9835c4c0ce885d82bda670c.png"},{"id":103095004,"identity":"95781e6e-8d2d-4131-a2ab-7241ac86f1ea","added_by":"auto","created_at":"2026-02-20 17:49:08","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":241990,"visible":true,"origin":"","legend":"\u003cp\u003eADMET analysis by SwissADME of drug spiro-fused quinoxaline\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-8840701/v1/c6b1ebff9c89cb09cb07ced1.png"},{"id":103504153,"identity":"504e60c8-74d5-4a3b-aff9-bceb1f2cdf58","added_by":"auto","created_at":"2026-02-26 13:18:00","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":59398,"visible":true,"origin":"","legend":"\u003cp\u003eThe boiled egg models of Spiro-fused Quinoxaline (\u003cstrong\u003eA\u003c/strong\u003e) and the boiled egg models of Spiro-fused Benzoxazine (\u003cstrong\u003eB\u003c/strong\u003e), respectively\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-8840701/v1/08ba4e0205d245736a1c8968.png"},{"id":108806115,"identity":"d5a6c1b1-26ef-4004-a195-9c0556ac105c","added_by":"auto","created_at":"2026-05-08 15:27:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3518821,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8840701/v1/11493c0f-cf55-49b5-8a07-30063fda79af.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Structural analyses of Spiro-Fused Quinoxaline and Benzoxazine Derivatives as both Antibacterial and Anticancer Agents: Molecular Docking and ADMET Evaluation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGenetic transfer particularly mutation, drug inactivation, target alteration, decreased permeability, and active efflux pumps are some of the pathways that lead to antibiotic resistance [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Many medicines become ineffective as germs become resistant to current medications, opening the door for \"superbugs\" that are more and more resistant to treatment. [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In order to address resistance, medicinal chemists have developed new antibiotics that target particular pathways, such as semi-synthetic derivatives of existing scaffolds. Tetracycline analogs, such tigecycline, and fourth-generation cephalosporins, which are engineered to evade β-lactamases or efflux pumps, are classic examples. [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Another challenge is that, with about 10\u0026nbsp;million deaths from the disease in 2022 and a predicted increase unless new potent treatment approaches are created, cancer continues to be a major cause of death globally.[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this regard, in silico methods have become essential instruments for modern drug discovery. Computational techniques including molecular docking, pharmacophore modeling, virtual screening, ADME/T prediction, and molecular dynamics simulations allow researchers to investigate ligand-protein interactions at the atomic level in an effective and economical manner. Unlike traditional experimental methods, in silico procedures enable the rapid identification of interesting scaffolds, optimization of lead compounds, and early prediction of pharmacokinetic and toxicity profiles. This accelerates the drug development pipeline by reducing the need for time-consuming and costly in vitro and in vivo experiments.\u003c/p\u003e \u003cp\u003eModern anticancer drug discovery relies heavily on molecular docking, a computer-based technique that predicts the binding orientation of small molecules. Compared to conventional high-throughput in vitro tests, it allows researchers to screen large virtual libraries, optimize lead compounds, and investigate ligand\u0026ndash;target interactions with significantly greater efficiency and cheaper cost [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAn increasing number of in-silico techniques are being used to develop novel antibacterial and anticancer drugs, including molecular docking, pharmacophore modeling, ADME/T assessment, and molecular dynamics simulations. Additionally, they made possible more efficient drug discovery with more resources and at a cheaper cost. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e][\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] Spiro- fused heterocycles, which have two rings connected at a common spiro-carbon, offer an efficient three-dimensional structure that enhances selectivity, potency, and metabolic stability ([\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e][\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]; Among these, benzoxazine and spirofused quinoxaline molecules have demonstrated potential for multipurpose platforms with biological activity.\u003c/p\u003e \u003cp\u003eThe study of medicinal chemistry has demonstrated the efficacy of quinoxaline scaffolds, which also possess advantageous antibacterial, antiviral, anticancer, and anti-inflammatory characteristics.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Synthetic methods such as one-pot multicomponent and 1,3-dipolar cycloaddition reactions have made it possible to access a range of spiro quinoxaline frameworks, including indeno[1,2 b] quinoxaline oxadiazoles, with preliminary evidence of antimicrobial efficacy and favorable docking interactions with bacterial targets. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e][\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e][\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] Research on anticancer drugs has focused on benzoxazine compounds, particularly EGFR kinase inhibitors. Benzoxazines may outperform common drugs, such as erlotinib, at the EGFR active site, according to in silico docking studies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e][\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. They have advantageous ADME/Traf profiles, including gastrointestinal absorption, CYP450 metabolism, and reduced hepatotoxicity. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eBacterial aspartyl-tRNA synthetase (AspRS) (PDB 1L0W) is a verified antimicrobial target that catalyzes a crucial step in protein translation. The crystal structure makes it possible to conduct docking-driven investigations of inhibitors that imitate the aminoacylated tRNA intermediate to prevent bacterial protein production. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Docking-based inhibitor design focuses on the ATP-binding pocket of EGFR kinase (PDB 6ZJ0), an oncogenic receptor tyrosine kinase that is a major driver of many cancers. Multistep virtual screening, docking, ADME prediction, and MD simulations have all been used in recent EGFR computational pipelines, producing drugs with significant preclinical potential and stable binding. [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. While many studies show promise, many are still in the in silico or early cell-based stages. Key gaps still exist in translating docking hits to in vivo efficacy and tackling resistance produced by EGFR mutation conformational flexibility; newer generation inhibitors must accept numerous binding conformations of EGFR mutant forms[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eADMET screening, molecular docking, and in vitro anticancer assays were performed to evaluate the spirobenzoxazine derivatives. For example [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], synthesized benzoxazine molecules (not spiro-fused) and predicted their pharmacokinetic profiles using online tools (e.g., pkCSM via ADMETlab). The results demonstrated good gastrointestinal absorption, metabolism via CYP450 isoforms, and reduced hepatotoxicity, indicating that these scaffolds are promising lead compounds. Compound 3 in their series showed moderate cytotoxicity (IC₅₀ \u0026asymp; 36.6 \u0026micro;g/mL) against A549 lung cancer cells, with docking scores superior to erlotinib against EGFR (PDB 1M17). When comparing the ADMET profiles of two compound classes, both spiro quinoxaline and benzoxazine derivatives are expected to have high gastrointestinal absorption and meet drug-likeness rules, such as Lipinski's (MW\u0026thinsp;\u0026lt;\u0026thinsp;500, LogP\u0026thinsp;\u0026lt;\u0026thinsp;5), indicating strong oral bioavailability potential. ADMET models frequently identify CYP450 metabolism liability risks, and compounds can function as substrates or inhibitors of enzymes, such as CYP3A4 or CYP2D6. Predictions for quinoxaline derivatives consistently indicate moderate CYP involvement but manageable risk, whereas benzoxazines generally avoid strong inhibitory interactions, allowing for safer metabolic profiles. Toxicity: Hepatotoxicity and hERG liability are important concerns. Benzoxazine scaffolds subjected to ADMET methods showed poor hERG-blocking ability and mild hepatotoxicity. Despite being active, quinoxaline analogs might need to be structurally adjusted to lessen off-target issues.[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eUsing molecular docking and ADMET analysis, this study sought to evaluate the pharmacokinetic properties and binding interactions of spiro-fused quinoxaline and benzoxazine derivatives, demonstrating their potential as new antibacterial and anticancer medication candidates. By performing the first comprehensive in silico evaluation of these scaffolds against dual therapeutic targets, this study aims to close a gap in the literature and show their value in upcoming drug discovery pipelines.The scaffold docking of spiro-fused quinoxaline and spiro-fused benzoxazine derivatives to AspRS (PDB 1L0W) and EGFR (PDB 6ZJ0) in silico study indicates a practical structure-based method for finding possible antibacterial and anticancer leads. This study built on previous docking studies of similar spiro-heterocycles and pipelines that target EGFR and enzyme inhibitors. It involved thorough docking preparation, binding energy comparison to recognized standards, molecular dynamics validation, and ADMET screening. The direct docking of these specific spiro derivatives against 1L0W and 6ZJ0 has not been extensively explored in the literature. However, their structural logic, synthetic accessibility, and docking antecedents strongly indicate their potential as dual-application therapeutic scaffolds.\u003c/p\u003e"},{"header":"Material and Methods","content":"\u003cp\u003e \u003cb\u003eIdentification and preparation of the proteins.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe three-dimensional structures of the dimeric multidomain enzyme aspartyl-tRNA synthetase from Thermus thermophilus (PDB ID: 1L0W) and Epidermal Growth Factor Receptor kinase (EGFR) (PDB ID: 6jz0) were obtained in PDB format from the Protein Data Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] It serves as an essential repository that includes the three-dimensional (3D) atomic structures of biomolecules, such as proteins and nucleic acids. It offers comprehensive details about the structures of both proteins. Aspartyl-tRNA synthetase (AspRS) was selected because it plays a role in the protein synthesis machinery of bacteria, as it is involved in providing asparagine-tRNA (Asn-tRNA) charging tRNA molecules with the use of amino acid aspartate. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Similarly, the structure of the Epidermal Growth Factor Receptor (EGFR) kinase is highly relevant in the treatment of non-small cell lung cancer (NSCLC) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Standard preparation procedures were performed using the BIOVIA Discovery Studio Visualizer and AutoDock tools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://discover.3ds.com/\u003c/span\u003e\u003cspan address=\"https://discover.3ds.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These included the addition of polar hydrogens, assignment of Kollman charges to guarantee accurate modeling of electrostatic interactions, and removal of co-crystallized ligands and water molecules to prevent non-specific interactions [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. To alleviate steric conflicts and optimize the shape, the protein structures were subsequently energy-minimized using Discovery Studio's default procedure. The structures were transformed to the PDBQT format, which encodes torsional flexibility and partial charges, to prepare the data for docking with AutoDock Vina. To investigate protein-binding sites and enhance ligand interactions with the protein of interest, the web program CASTp3.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://sts.bioe.uic.edu/castp/index.html\u003c/span\u003e\u003cspan address=\"http://sts.bioe.uic.edu/castp/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. The input was found in the PBD format, and the results showed active protein locations as red circles\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eIdentification and preparation of ligand\u003c/h2\u003e \u003cp\u003eDifferent leading ligands used for treating lung cancer and acting as antibiotics were identified through a literature review. Two leading Ligands spiro fused quinoxaline(4b,6,7a,12a,13b-Pentahydroxy-4b7a,12a,13b-tetrahydro benzo-[b], [e]bis-indeno[2,1-d]-furan-12,14-dione) and spiro fused benzoxazine(10a-hydroxybenzo[b]indeno [1,2-e] [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]-oxazin-11(10aH) one ) were drawn in the online tool Swiss target prediction (\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=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], where Simplified Molecular Input Line Entry System originated. SMILES were converted to Structure Data Format (SDF)2D and 3D models using an online SDF conversion tool. JSME Biotech, Fyi Centre (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://biotech.fyicenter.com/1000051_Introduction_to_JSME.html\u003c/span\u003e\u003cspan address=\"http://biotech.fyicenter.com/1000051_Introduction_to_JSME.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].Three-dimensional (3D) structures in the SDF format were retrieved. Subsequently, the downloaded tool Discovery Studio (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.3ds.com/products/biovia/discovery-studio\u003c/span\u003e\u003cspan address=\"https://www.3ds.com/products/biovia/discovery-studio\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to convert the SDF format to the PDB format for visualization in Auto DOCK Vina version 1.5.4 and PyRx version 0.9.2 [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Subsequently, proteins were selected using a ligand-based approach with the online tools Swiss Target Prediction (\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), Pharm Mapper (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.lilab-ecust.cn/pharmmapper/\u003c/span\u003e\u003cspan address=\"https://www.lilab-ecust.cn/pharmmapper/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and Way2Drug (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.way2drug.com/passonline/\u003c/span\u003e\u003cspan address=\"https://www.way2drug.com/passonline/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e][\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The proteins were obtained from the PDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eScreening Of Ligands\u003c/h3\u003e\n\u003cp\u003eThe initial study of the identified medicinal compounds involved docking against proteins (PDB IDs: 1L0W and 6JZ0). AutoDock Vina (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vina.scripps.edu/\u003c/span\u003e\u003cspan address=\"https://vina.scripps.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and PyRx, two efficient docking tools that predict the preferred orientation of ligands when bound to a protein receptor and estimate binding affinities using an effective scoring system, were used for molecular docking.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Ligands were converted into a format (PDBQT) that works with AutoDock Vina, and their energies were minimized. A flexible ligand was inserted into a rigid receptor as part of the docking process to evaluate possible interactions and binding energies. Ligands with the lowest binding energies (indicating increased binding affinity) were selected for further structural modifications. Screening was performed to identify potential candidates for rational drug design, focusing on combating drug resistance and cancer treatment. As predicted by the online program CASTp3.0, a grid box was created with dimensions adequate to contain the active site and adjacent residues that may be implicated in ligand binding [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This ensured that the docking simulations focused on biologically relevant binding pockets, yielding meaningful interaction data.\u003c/p\u003e\n\u003ch3\u003eProtein–ligand interaction analysis by Single ligand docking\u003c/h3\u003e\n\u003cp\u003eSpiro-fused quinoxaline and spirofused benzoxazine were docked against aspartyl-tRNA synthetase protein (PDB IDs: 1L0W) using AutoDock Vina. Similarly, both of the above-mentioned leading ligands docked against Epidermal Growth factor receptor kinase (EGFR kinase) (PDB IDs:6JZ0). Site-specific docking was performed, and the grid box was resized. Because each ligand has a distinct protein-binding site, grid construction is necessary before starting a docking experiment [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. A cubic grid box was created using the values of the x, y, and z axes. The coordinates for 1L0W with spirofused quinoxaline were identified as X=-32.327, Y=-48.770, and Z=-45.157, whereas those with spirofused benzoxazine were designated as X=-32.327, Y=-48.770, and Z\u0026thinsp;=\u0026thinsp;45.157. Similarly, a protein with an anticancer function (PDB ID:6JZ0) was evaluated before adjusting the grid box dimensions. A cubic grid box was created using the values on the x-, y-, and z-axes. The grid center dimensions for the EFGR kinase protein with spirofused quinoxaline were X=-22.771, Y=-58.267, and Z=-9.669, whereas those for the EFGR kinase protein with spirofused benzoxazine were X=-22.771, Y=-58.267, and Z=-9.669. The interactions between the ligands and proteins were visualized using PyMOL (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pymol.org/\u003c/span\u003e\u003cspan address=\"https://pymol.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] Bond lengths were measured to identify the bonds formed between the protein and leading ligand drugs. Compounds were screened by generating receptor grids and molecular docking using the PyRx tool (version 0.9.2) to identify compounds with the highest binding affinity for the protein. Compounds with the lowest energy will be chosen for further dug design as an antibacterial effect\u003c/p\u003e\n\u003ch3\u003eProtein–ligand interaction analysis by Multiple ligand docking\u003c/h3\u003e\n\u003cp\u003eFor the molecular docking study, the protein structure (PDB ID: 1L0W) and the chosen chemical compounds, Spirofused Quinoxaline and Spirofused Benzoxazine, were placed into the PyRx virtual screening interface. Following minimization, the Open Babel module built into PyRx was used to convert and save the protein structures and chemical compounds in the \". Thee forward docking option in PyRx was used to create an active binding site grid box. To guarantee precise coverage of the protein binding pocket, the grid box dimensions and coordinates were meticulously modified by following the box edge and manually inputting the numbers[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e][\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].The ideal grid box settings employed to dock the 1L0W protein are as follows:\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProtein molecule\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCentre x\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCentre y\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCentre z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSize x\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSize y\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSize z\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1l0w\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;34.9639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;48.0074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-34.9317\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e84.3256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.3563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e91.8881\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\u003eFor docking, the tool recorded the binding energy in \".csv\" format and showed it with several conformers. AutoDock Vina was used to divide the PyRx results into distinct oriented conformers. Using the Pymol Visualizer, the docking output files were analyzed for interactions between the chemical compounds and the amino acids of the protein [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eIn silico pharmacokinetic and drug-likeness evaluation\u003c/h3\u003e\n\u003cp\u003ePre-clinical testing and ADMET analysis of the modified ligands were conducted using SwissADME (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swissadme.ch/\u003c/span\u003e\u003cspan address=\"http://www.swissadme.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The physicochemical, pharmacokinetic, permeability, and absorption, distribution, metabolism, and excretion (ADMET) properties of small pharmaceutical substances can all be evaluated and predicted using the online program SwissADME [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The two-dimensional (2D) structure of the chemical was included in the input, and its drug-like properties were determined.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eValidation of Lipinski\u0026rsquo;s rule of five\u003c/h2\u003e \u003cp\u003eSwissADME was used to analyze the enhanced compounds' drug-like characteristics (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swissadme.ch/\u003c/span\u003e\u003cspan address=\"http://www.swissadme.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). It helps with drug design and modeling, produces two-dimensional (2D) structures, predicts bioactivity, computes different molecular characteristics, and makes molecular processing and manipulation easier [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The molecular characteristics were ascertained by drawing the altered structure of the compound as the input.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eMolecular docking was used to determine the interaction between the ligand and target protein. The resulting binding affinity of a ligand determines its ability to attach to a protein; a lower binding energy denotes a stronger link, which makes the protein a potential therapeutic option. Both recovered ligands were screened using AutoDock Vina software. The binding affinities of the selected ligand molecules are presented in Tables\u0026nbsp;1 and 2. Among the selected ligands, spirofused benzoxazine exhibited the highest binding affinity for both proteins. In both instances, the spirofused quinoxaline exhibited the lowest binding affinity.\u003c/p\u003e\n\u003ch3\u003eProtein ligand interaction by Single ligand docking\u003c/h3\u003e\n\u003cp\u003eMolecular docking studies demonstrated that both spirofused quinoxaline and spirofused benzoxazine were successfully accommodated in the active site pocket of aspartyl tRNA synthetase (PDB ID: 1L0W), generating stable contacts with critical catalytic residues. The spirofused quinoxaline ligand formed hydrogen bonds with W527 and D531, which were supported by additional polar contacts with F236 and R253. Hydrophobic stabilization within the binding cleft enhanced these interactions, allowing the quinoxaline core to be oriented more firmly. The hydrogen bonding distances lie between 2.8\u0026ndash;3.4 \u0026Aring;, showing stable binding but impartial flexible binding too. In contrast, direct hydrogen bonds with Q232 and D531 showed less polar stabilization for the spiro-fused benzoxazine scaffold, with shorter bond lengths (2.2\u0026ndash;3.3 \u0026Aring;), implying accommodation within the active site. In addition, hydrophobic interactions with W527 and F236 further improved benzoxazine molecule anchoring. The benzoxazine derivative docked with a higher affinity value (-9.2 kcal/mol) than quinoxaline (\u0026minus;\u0026thinsp;12.1 kcal/mol), suggesting that while both scaffolds are viable AspRS inhibitors, spiro-fused quinoxaline may be a better lead candidate because of its improved binding interactions. On the other hand, the interactions between the EGFR kinase (PDB ID: 6jz0) and the lead ligand spiro-fused quinoxaline mentioned in table 3 were studied and visualized using PyMOL to examine conventional bonds and their lengths. Four hydrogen bonds (2.8, 3.0, 3.1, and 2.9 \u0026Aring;) were formed within the normal bonding distance of 2.7\u0026ndash;3.2 \u0026Aring; inside the complex. Two hydrophobic (Van der Waals) contacts were also observed at bond lengths of 3.6 and 3.8 \u0026Aring;, which are within the normal range contact distance of 3.3-4.0 \u0026Aring; for VDW interactions. From these interactions, it can be further noted that a stable binding conformation of spirofused quinoxaline is observed within the active site of EGFR kinase. The interaction of EGFR kinase (PDB ID: 6JZ0) with the lead ligand, spiro-fused benzoxazine, was performed by visualizing in PyMOL software by analyzing and characterizing the conventional bonds, as shown in Table\u0026nbsp;4. Two hydrogen bonds were found between the ligand and the catalytic residues Met793 and Cys797, with lengths of 1.9 \u0026Aring; and 3.3 \u0026Aring;, respectively, which fall within the usual range of 2.7\u0026ndash;3.2 \u0026Aring;. Additionally, a hydrophobic (van der Waals) contact with Leu844 was predicted at a distance of 3.6 \u0026Aring;, which is consistent with the expected range of 3.3-4.0 \u0026Aring;. Other adjacent residues, such as Gly719, Val726, Ala743, Ile789, Pro794, and Phe795, stabilize the ligand within the ATP-binding pocket. These interactions indicate a stable binding conformation of the spiro-fused benzoxazine ligand within the active region of the EGFR kinase.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eProtein ligand interaction by Multiple ligand docking\u003c/h2\u003e \u003cp\u003eAccording to molecular docking experiments, spirofused benzoxazine and spirofused quinoxaline were successfully incorporated into the Aspartyl tRNA synthetase active site pocket (PDB ID: 1L0W), creating strong bonds with important catalytic residues. Additional polar interactions with F236 and R253 reinforced the hydrogen bonds between the spirofused quinoxaline ligand and W527 and D531. The resulting bonds were strengthened by hydrophobic stabilization in the binding cleft, which allowed the quinoxaline core to be aligned more steadily. The hydrogen bonding lengths, which range from 2.8 to 3.4 \u0026Aring;, demonstrate both unbiased and flexible bindings. Conversely, the spiro-fused benzoxazine scaffold showed polar stabilization for direct hydrogen bonds with Q232 and D531, with shorter bond lengths (2.2\u0026ndash;3.3 \u0026Aring;), suggesting accommodation inside the active site. Additionally, benzoxazine molecule anchoring involved hydrophobic interactions with W527 and F236. Interaction overlaps between the two ligands at residues W527, F236, R253, and D531 indicate that they are attracted to the catalytic core region. As shown in Table\u0026nbsp;5, nine different orientations of both compounds with the target protein were observed. Compared to quinoxaline (\u0026minus;\u0026thinsp;11.7 kcal/mol), the benzoxazine derivative docked with a higher affinity value (-8.7 kcal/mol), suggesting lesser net hydrogen bonding and π-π stacking interactions. Thus, this result supports the single ligand docking results see table 1,2. This implies that spirofused quinoxaline is a stronger lead candidate because of its enhanced binding interactions, even if both scaffolds are effective AspRS inhibitors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePre-clinical testing of leading ligands\u003c/h2\u003e \u003cp\u003ePreclinical testing was performed on spirofused quinoxaline and spirofused benzoxazine to determine their solubility and interaction with bodily tissues, ensuring their safety and efficacy. ADMET study predicted drugs physicochemical characteristics, water solubility, gastrointestinal (GI) absorption, skin permeability, bloodbrain barrier (BBB) penetration, synthetic accessibility, bioavailability, and topological surface area. ADMET research revealed that they could not enter the skin. The AD\u0026thinsp;+\u0026thinsp;MET study indicated that the leading ligands did not inhibit cytochrome and had optimal lipophilicity. Both spiro-fused quinoxaline and spiro fused benzoxazine are water soluble and have great synthetic accessibility, indicating a promising drug development potentia Furthermore, both medicines had moderate bioavailability scores. Table displays the ADMET parameters for the both ligands.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBoiled egg model\u003c/h2\u003e \u003cp\u003eThe boiled egg model was used for prediction using the SwissADME server. The cooked egg model is a valid technique for examining how small compounds are absorbed in the gastrointestinal tract and their capacity to pass the blood-brain barrier. According to this model, the BBB and gastric absorption are indicated by the yellow and white areas, respectively. Using this paradigm, it was discovered that spiro-fused quinoxaline cannot cross the blood-brain barrier and has low gastrointestinal absorption, whereas spiro-fused benzoxazine can be efficiently absorbed through the gastrointestinal system and can cross the blood-brain barrier.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePreparation of ligand and target protein\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eResults of single ligand docking with compound spirofused quinoxaline with aspartyl trna protein (PDB ID: 1l0w)\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eResults of single ligand docking with compound spirofused benzoxazine with aspartyl trna protein (PDB ID: 1l0w)\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eResults of single ligand docking with compound spirofused quinoxaline with EFGR kinase protein (PDB ID: 6zj0)\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eResults of single ligand docking with compound spirofused benzoxazine with EFGR kinase protein (PDB ID: 6zj0)\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eResults of multiple ligand docking with compounds spirofused quinoxaline and spirofused benzoxazine with protein aspartyl trna protein (PDB ID: 1l0w)\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eTable no 1: Energy values of protein-ligand complex of aspartyl trna with ligand spirofused quinoxaline.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAffinity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDistance from best mode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDistance from best mode\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-12.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.644\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-11.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.728\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.144\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.485\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-11.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.566\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-11.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31.858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-10.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.225\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-10.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12.831\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 \u003cb\u003eTable no 2: Energy values of protein-ligand complex of aspartyl trna with ligand spirofused benzoxazine.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAffinity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDistance from best mode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDistance from best mode\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.665\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.469\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e28.205\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29.654\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.755\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.944\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16.453\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 \u003cb\u003eTable no 3: Energy values of protein-ligand complex of EFGR kinase with ligand spirofused quinoxaline.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAffinity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDistance from best mode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDistance from best mode\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-10.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-9.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.722\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.684\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.131\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-9.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.530\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-9.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.775\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-9.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.978\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27.405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.493\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 \u003cb\u003eTable no 4: Energy values of protein-ligand complex of EFGR kinase with ligand spirofused benzoxazine.\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabe\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAffinity(kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDistance from best mode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDistance from best mode\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.440\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.747\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.852\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 \u003cb\u003eTable no 5\u003c/b\u003e: \u003cb\u003erepresent multiple docking energies of protein-ligand complex of aspartyl trna with both ligands (spirofused quinoxaline spirofused benzoxazine).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabf\" border=\"1\"\u003e \u003ccolgroup cols=\"4\"\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 \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\u003eBinding Affinity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ermsd/ub\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ermsd/lb\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1l0w_FR1_uff_E\u0026thinsp;=\u0026thinsp;1444.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1l0w_FR1_uff_E\u0026thinsp;=\u0026thinsp;1444.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-11.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.422\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1l0w_FR1 _uff_E\u0026thinsp;=\u0026thinsp;1444.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1l0w_FR1 _uff_E\u0026thinsp;=\u0026thinsp;1444.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.809\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1l0w_FR1 _uff_E\u0026thinsp;=\u0026thinsp;1444.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-10.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1l0w_FR1 _uff_E\u0026thinsp;=\u0026thinsp;1444.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.795\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1l0w_FR1 _uff_E\u0026thinsp;=\u0026thinsp;1444.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1l0w_FR1 _uff_E\u0026thinsp;=\u0026thinsp;1444.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.921\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1l0w_FR1 _uff_E\u0026thinsp;=\u0026thinsp;1444.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-10.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.933\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1l0w_FR2_uff_E\u0026thinsp;=\u0026thinsp;368.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1l0w_ FR2_uff_E\u0026thinsp;=\u0026thinsp;368.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1l0w_ FR2 _uff_E\u0026thinsp;=\u0026thinsp;368.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.223\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1l0w_ FR2 _uff_E\u0026thinsp;=\u0026thinsp;368.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.724\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1l0w_ FR2_uff_E\u0026thinsp;=\u0026thinsp;368.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1l0w_ FR2 _uff_E\u0026thinsp;=\u0026thinsp;368.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1l0w_ FR2 _uff_E\u0026thinsp;=\u0026thinsp;368.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1l0w_ FR2 _uff_E\u0026thinsp;=\u0026thinsp;368.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.842\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1l0w_ FR2 _uff_E\u0026thinsp;=\u0026thinsp;368.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.008\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=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eADME analysis of Spiro-fused quinoxaline and Spiro-fused benzoxazine\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabg\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eADMET parameters\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eParametric values\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSpiro-fused Quinoxaline\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eParametric values\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSpiro-fused Benzoxazine\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eFormula\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eMolecular weight\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eNum. heavy atoms\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eNum. arom. heavy atoms\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eFraction Csp3\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eNum. rotatable bonds\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eNum. H-bond acceptors\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eNum. H-bond donors\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eMolar Refractivity\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eTPSA (Toplogical Polar Surface Area)\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eWater Solubility Log S (ESOL)\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eGI Absorption\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eBBB Permeation\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eSkin Permeation (Log Kp)\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eBioavailability Score\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eSynthetic Accessibility\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eDrug-likeness\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eC24H1409\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e446.36g/mol\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e33\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e18\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0.17\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e9\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e5\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e106.85\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e153.75 \u0026Aring;\u003c/span\u003e\u003csup\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e\u0026minus;3.02\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eLow\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eNo\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e-9.01cm/s\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0.55\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e4.60\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eYes; 0 violation\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0 alert\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eC15H9NO\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e251.14\u0026nbsp;g/mol\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e19\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e12\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0.07\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e4\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e72.32\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e58.89 \u0026Aring;\u003c/span\u003e\u003csup\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e2\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e\u0026minus;3.20\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eHigh\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eYes\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e\u0026minus;6.33cm/s\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0.55\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e3.53\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eYes; 0 violation\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0 alert\u003c/span\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"},{"header":"Discussion","content":"\u003cp\u003eMolecular docking and ADMET analyses are crucial in modern drug development. They predict the pharmacological potential and safety of new compounds before in vivo testing [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e][\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In this study, two spiro-fused heterocyclic compounds\u0026mdash;spiro-fused quinoxaline and spiro-fused benzoxazine\u0026mdash;were examined for antibacterial activity against aspartyl tRNA synthetase (PDB ID: 1l0w) and anticancer activity against EGFR kinase (PDB ID: 6zj0).\u003c/p\u003e \u003cp\u003eSpirofused quinoxaline had a higher binding affinity for both protein targets than spirofused benzoxazine, according to docking tests. The affinities of quinoxaline against EGFR kinase and aspartyl tRNA synthetase were \u0026minus;\u0026thinsp;10.4 and \u0026minus;\u0026thinsp;12.1 kcal/mol, respectively. Benzoxazine showed affinities of -9.2 and \u0026minus;\u0026thinsp;8.8 kcal/mol, but the quinoxaline derivative dominated. Higher stability of the protein-ligand interaction is indicated by lower docking energies, which implies that spirofused quinoxaline might be a more potent inhibitor [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e][\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Enhanced hydrogen bonding and hydrophobic interactions in the active sites demonstrate that the molecular structure of spiro-quinoxaline is well matched to the binding pockets [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e][\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, when evaluating therapeutic potential, docking affinity is not more significant than pharmacokinetic and safety parameters [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Spiro-quinoxaline shown good binding energies, but its gastrointestinal (GI) absorption was limited, and its blood-brain barrier (BBB) permeability was low, according to ADMET simulations[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, research on benzoxazine analogs has highlighted spiro-benzoxazine's more favorable pharmacokinetic properties, such as balanced lipophilicity, high GI absorption, and likely BBB penetration [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e][\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. These factors highlight that spiro-benzoxazine's enhanced ADMET profile makes it more feasible for systemic medication development, even though spiro-quinoxaline exhibits strong receptor binding. A discrepancy between binding affinity and drug-like characteristics was found in the ADMET data. Despite having better docking results, spiro-fused quinoxaline had poor GI absorption and did not cross the blood-brain barrier. This may limit its oral bioavailability and central nervous system action. Conversely, spiro-fused benzoxazine showed good pharmacokinetics, including high GI absorption, BBB penetration, and optimal lipophilicity, suggesting that it is suitable for systemic therapy [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThese results were validated by the Swiss-ADME boiled-egg model. Benzoxazine penetrated both the GI absorption and BBB penetration zones, however quinoxaline did not. This is consistent with past research on how molecular weight and topological polar surface area (TPSA) affect medication permeability. [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e][\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, both compounds demonstrated their potential for oral formulation by passing drug-likeness filters without breaking Lipinski's rule of five. Both ligands can be produced in a lab, according to their synthetic accessibility ratings, which is essential for economical medication development. Key cytochrome P450 isoenzymes were inhibited by neither ligand, indicating a minimal likelihood of drug-drug interaction.[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eCombined collectively, these results provide two viewpoints on the matter. The binding affinity of spiro-fused quinoxaline is stronger, but its pharmacokinetic potential is limited. Because of its superior ADMET properties and low binding affinity, spiro-fused benzoxazine is a more promising option for medicinal research. A common challenge in drug discovery is highlighted by this dichotomy: pharmacokinetic viability and binding potency must be balanced [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Future developments in spiro-fused quinoxaline derivatives may improve absorption and solubility while preserving strong binding. These computational predictions need to be validated experimentally by tests of antibacterial activity and in vitro cytotoxicity.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe current in silico investigation demonstrates the therapeutic potential of spiro-fused quinoxaline and spiro-fused benzoxazine scaffolds using molecular docking and ADMET profiling. Although spiro-fused quinoxaline has higher binding affinities for EGFR kinase and aspartyl tRNA synthetase, its low gastrointestinal absorption and incapacity to cross the blood-brain barrier limit its pharmacokinetic appropriateness. Conversely, although having lower docking scores, spiro-fused benzoxazine had good pharmacokinetic properties, such as high GI absorption, BBB penetration, and outstanding lipophilicity. Both compounds met the requirements for drug-likeness and demonstrated viable synthetic accessibility, indicating that they had potential to grow. These results highlight how important it is to balance binding affinity and pharmacokinetic viability in the early stages of drug development. Further developments in quinoxaline derivatives to boost bioavailability and experimental validation in vitro and in vivo are necessary to assess the potential of both scaffolds as antibacterial and anticancer drugs.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eFuture perspective\u003c/h2\u003e \u003cp\u003eSpiro-fused benzoxazine and spiro-fused quinoxaline have been computationally evaluated, which offers a strong basis for their potential future development as antibacterial and anticancer drugs. To translate these in silico results into therapeutic applications, a number of strategies need be taken into account. Spiro-fused quinoxaline's solubility, oral absorption, and blood-brain barrier penetration may all be enhanced by structural modification while preserving its high binding affinity. In order to improve binding affinity while maintaining the advantages of ADMET, spiro-fused benzoxazine, which now exhibits favorable pharmacokinetic characteristics, should undergo certain modifications.\u003c/p\u003e \u003cp\u003eFuture studies should use quantitative structure-activity relationship (QSAR) modeling to help with the logical design of improved analogs, free energy calculations to enhance binding predictions, and molecular dynamics (MD) simulations to confirm docking stability. To validate the computer predictions, experimental investigations are needed, including antimicrobial susceptibility testing, enzyme inhibition assays, and anticancer cytotoxicity profiling. To ascertain the safety and therapeutic indices, in vivo pharmacokinetic and toxicological investigations would also be required. Spiro-fused heterocycles may become a novel class of multifunctional medicinal candidates by combining these strategies. Their potential to help develop next-generation antibacterial and anticancer medications, which would address two of the most urgent health issues facing the world, is demonstrated by their dual activity range.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDr. Madiha Khan conceived and supervised the study, designed the methodology, and led the overall research direction. Rafia Noor performed molecular docking experiments, data collection, and preliminary analysis as part of her student research work. Hira Mubeen contributed expert input in bioinformatics, including target selection, docking validation, and interpretation of computational results. Samman Ikram assisted in data analysis, visualization, and interpretation of ADMET and pharmacokinetic findings. Dr. Madiha Khan drafted the manuscript with contributions from all authors. All authors reviewed, edited, and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMunita JM, Arias CA.2016.Mechanisms of Antibiotic Resistance. Microbiol Spectr 4:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1128/microbiolspec.vmbf-0016-2015.https://doi.org/10.1128/microbiolspec.vmbf-0016-2015\u003c/span\u003e\u003cspan address=\"10.1128/microbiolspec.vmbf-0016-2015.10.1128/microbiolspec.vmbf-0016-2015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalam MA, Al-Amin MY, Salam MT, Pawar JS, Akhter N, Rabaan AA, Alqumber MAA (2023) Antimicrobial Resistance: A Growing Serious Threat for Global Public Health. Healthc (Basel) 11(13):1946. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/healthcare11131946\u003c/span\u003e\u003cspan address=\"10.3390/healthcare11131946\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003ePMID: 37444780; PMCID: PMC10340576\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInstitute of Medicine (US) Forum on Microbial Threats. Antibiotic Resistance: Implications for Global Health and Novel Intervention Strategies: Workshop Summary. Washington (DC): National Academies Press (US) (2010) Appendix A, Contributed Manuscripts. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/books/NBK54261/?utm_source=chatgpt.com\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/books/NBK54261/?utm_source=chatgpt.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeshmukh A, Kalel P, Mulani S, Kundekar K, Pol V, Shinde R, Shinde V (2025) Molecular Docking in Drug Discovery: Insights Challenges and Emerging Trends. Asian J Adv Med Sci 7(1):81\u0026ndash;99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.56557/ajoaims/2025/v7i1156\u003c/span\u003e\u003cspan address=\"10.56557/ajoaims/2025/v7i1156\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlanzi AR, Moussa AY, Alsalhi MS, Nawaz T, Ali I (2024) Integration of pharmacophore-based virtual screening, molecular docking, ADMET analysis, and MD simulation for targeting EGFR: A comprehensive drug discovery study using commercial databases. PLoS ONE 19(12):e0311527. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0311527\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0311527\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh R, Bhardwaj D, Saini MR (2021) Recent advancement in the synthesis of diverse spiro-indeno[1,2-b]quinoxalines: a review [10.1039/D0RA09130H]. RSC Adv 11(8):4760\u0026ndash;4804. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1039/D0RA09130H\u003c/span\u003e\u003cspan address=\"10.1039/D0RA09130H\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao H, Zhang Q, Shreeve JnM (2020) Fused heterocycle-based energetic materials (2012\u0026ndash;2019) [10.1039/C9TA12704F]. J Mater Chem A 8(8):4193\u0026ndash;4216. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1039/C9TA12704F\u003c/span\u003e\u003cspan address=\"10.1039/C9TA12704F\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuthar SK, Chundawat NS, Singh GP, Padr\u0026oacute;n JM, Jhala YK (2022) Quinoxaline: A comprehension of current pharmacological advancement in medicinal chemistry. Eur J Med Chem Rep 5:100040. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.ejmcr.2022.100040\u003c/span\u003e\u003cspan address=\"10.1016/j.ejmcr.2022.100040\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaseem AM, Elmagzoub RM, Abdelgadir MMM, Bahir AA, El-Gawaad NSA, Abdel-Samea AS, Rao DP, Kossenas K, Br\u0026auml;se S, Hashem H (2025) An insight into the therapeutic impact of quinoxaline derivatives: Recent advances in biological activities (2020\u0026ndash;2024). Results Chem 13:101989. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.rechem.2024.101989\u003c/span\u003e\u003cspan address=\"10.1016/j.rechem.2024.101989\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlmansour AI, Arumugam N, Kumar S, Al-thamili R, Periyasami DM, Ponmurugan G, Al-Dhabi K, Perumal NA, K., Premnath D (2019) Domino Multicomponent Approach for the Synthesis of Functionalized Spiro-Indeno[1,2-\u003cem\u003eb\u003c/em\u003e]quinoxaline Heterocyclic Hybrids and Their Antimicrobial Activity, Synergistic Effect and Molecular Docking Simulation. \u003cem\u003eMolecules\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(10), 1962\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSulistyowaty MI, Widyowati R, Putra GS, Budiati T, Matsunami K (2021) Synthesis, ADMET predictions, molecular docking studies, and in-vitro anticancer activity of some benzoxazines against A549 human lung cancer cells. J Basic Clin Physiol Pharmacol 32(4):385\u0026ndash;392. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/doi:10.1515/jbcpp-2020-0433\u003c/span\u003e\u003cspan address=\"doi:10.1515/jbcpp-2020-0433\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerman HM et al (2000) The Protein Data Bank. Nucleic Acids Res 28(1):235\u0026ndash;242\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBienfait B, Ertl P (2013) JSME: a free molecule editor in JavaScript. J Cheminform 5(1):24\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaina A, Michielin O, Zoete V (2019) SwissTargetPrediction: Updated data and new features. Nucleic Acids Res 47(W1):W357\u0026ndash;W364\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDallakyan S, Olson AJ (2015) Small-molecule library screening by docking with PyRx. Methods Mol Biol 1263:243\u0026ndash;250\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDundas J et al (2006) CASTp: Computed Atlas of Surface Topography of proteins. Nucleic Acids Res 34:W116\u0026ndash;W118 Web Server issue)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErtl P, Rohde B, Selzer P (2000) Fast calculation of molecular polar surface area. J Med Chem 43(20):3714\u0026ndash;3717\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForli S et al (2016) Computational protein\u0026ndash;ligand docking and virtual drug screening with AutoDock. Nat Protoc 11(5):905\u0026ndash;919\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarrison PT (2012) The epidermal growth factor receptor in lung cancer. Br J Cancer 107(1):1\u0026ndash;7\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuey R et al (2012) Using AutoDock for ligand-receptor docking. Curr Protocols Bioinf 24(1):8\u0026ndash;14\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKitchen DB, Decornez H, Furr JR, Bajorath J (2004) Docking and scoring in virtual screening for drug discovery. Nat Rev Drug Discovery 3(11):935\u0026ndash;949\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLagunin A et al (2000) PASS: Prediction of activity spectra for biologically active substances. Bioinformatics 16(8):747\u0026ndash;748\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X et al (2010) PharmMapper server: A web server for potential drug target identification. Nucleic Acids Res 38:W609\u0026ndash;W614 Web Server issue)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchr\u0026ouml;dinger LLC (2015) The PyMOL molecular graphics system, version 1.8\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDallakyan S, Olson AJ (2015) Small-molecule library screening by docking with PyRx. Methods Mol Biol 1263:243\u0026ndash;250. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-1-4939-2269-7_19\u003c/span\u003e\u003cspan address=\"10.1007/978-1-4939-2269-7_19\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVeerasamy R, Karunakaran R (2022) Molecular docking unveils the potential of andrographolide derivatives against COVID-19: an in silico approach. J Genetic Eng Biotechnol 20(1):58. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1186/s43141-022-00339-y\u003c/span\u003e\u003cspan address=\"10.1186/s43141-022-00339-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdeniji SE, Arthur DE, Oluwaseye A (2020) Computational modeling of 4-Phenoxynicotinamide and 4-Phenoxypyrimidine-5-carboxamide derivatives as potent anti-diabetic agent against TGR5 receptor. J King Saud Univ Sci 32:102\u0026ndash;115\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSissler M et al (1999) An in vitro approach to the identification of RNA\u0026ndash;protein interactions. J Mol Biol 285(5):1661\u0026ndash;1674\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTian W, Chen C, Lei X, Zhao J, Liang J (2018) CASTp 3.0: Computed atlas of surface topography of proteins. Nucleic Acids Res 46(W1):W363\u0026ndash;W367\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang Y, Yang W, Wang F, Zhou B (2023) In silico studies of a novel scaffold of benzoxazole derivatives as anticancer agents by 3D-QSAR, molecular docking and molecular dynamics simulations [10.1039/D3RA01316B]. RSC Adv 13(22):14808\u0026ndash;14824. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1039/D3RA01316B\u003c/span\u003e\u003cspan address=\"10.1039/D3RA01316B\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRagab A, Abusaif MS, Gohar NA, Aboul-Magd DS, Fayed EA, Ammar YA (2023) Development of new spiro[1,3]dithiine-4,11\u0026prime;-indeno[1,2-b]quinoxaline derivatives as S. aureus Sortase A inhibitors and radiosterilization with molecular modeling simulation. Bioorg Chem 131:106307. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/https://doi.org/10.1016/j.bioorg.2022.106307\u003c/span\u003e\u003cspan address=\"10.1016/j.bioorg.2022.106307\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao Z, Xie L, Bourne PE (2018) Structural insights into characterizing binding sites in epidermal growth factor receptor kinase mutants. J Chem Inf Model 59(1):453\u0026ndash;462\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlanzi AR, Moussa AY, Alsalhi MS, Nawaz T, Ali I (2024) Integration of pharmacophore-based virtual screening, molecular docking, ADMET analysis, and MD simulation for targeting EGFR: A comprehensive drug discovery study using commercial databases. PLoS ONE 19(12):e0311527. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0311527\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0311527\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOuma RBO, Ngari SM, Kibet JK et al (2024) A review of the current trends in computational approaches. Hum Genomics 18:29. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12982-024-00229-3\u003c/span\u003e\u003cspan address=\"10.1186/s12982-024-00229-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRich AS, Chan YH, Birnbaum B, Haider K, Haimson J, Hale M, Han Y, Hickman W, Hoeflich KP, Ortwine D, \u0026Ouml;zen A, Belanger DB (2024) Machine learning ADME models in practice: Four guidelines from a successful lead optimization case study. ACS Med Chem Lett 15(8):1169\u0026ndash;1173. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/acsmedchemlett.4c00290\u003c/span\u003e\u003cspan address=\"10.1021/acsmedchemlett.4c00290\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ (2009) AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J Comput Chem 30(16):2785\u0026ndash;2791. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jcc.21256\u003c/span\u003e\u003cspan address=\"10.1002/jcc.21256\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTrott O, Olson AJ (2010) AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31(2):455\u0026ndash;461. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/jcc.21334\u003c/span\u003e\u003cspan address=\"10.1002/jcc.21334\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePatel D, Yadav R, Tandon R (2021) Design, synthesis, and docking studies of novel quinoxaline-based heterocycles as potential kinase inhibitors. Arab J Chem 14(10):103386. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.arabjc.2021.103386\u003c/span\u003e\u003cspan address=\"10.1016/j.arabjc.2021.103386\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMishra A, Singh R, Chauhan A (2023) \u003cem\u003eComputational insight into the pharmacokinetic and toxicity profiles of quinoxaline derivatives as multitarget inhibitors. Molecules, 28\u003c/em\u003e(5), 2063. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/molecules28052063\u003c/span\u003e\u003cspan address=\"10.3390/molecules28052063\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar A, Verma S, Mehta N (2024) ADMET and molecular modeling analysis of quinoxaline hybrids as antimicrobial agents. J Biomol Struct Dynamics 42(7):15021\u0026ndash;15033. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/07391102.2024.2401087\u003c/span\u003e\u003cspan address=\"10.1080/07391102.2024.2401087\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChakraborty S, Alam M, Das R (2023) Design and ADMET evaluation of benzoxazine-based scaffolds with anticancer potential. ChemistrySelect 8(16):e202300458. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/slct.202300458\u003c/span\u003e\u003cspan address=\"10.1002/slct.202300458\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMitku ML, Dagnaw AD, Geremew DT, Anagaw YK, Worku MC, Limenh LW, Tadesse YB, Ergena AE (2024) In silico prediction of some pharmacokinetic, safety, biological activity and molecular docking studies of 1-piperazine indole hybrid with nicotinic amide and nicotinic acid and their analogues. SAGE open Med 12:20503121241274212. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1177/20503121241274212\u003c/span\u003e\u003cspan address=\"10.1177/20503121241274212\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaina A, Michielin O, Zoete V (2017) SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep 7(1):42717. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/srep42717\u003c/span\u003e\u003cspan address=\"10.1038/srep42717\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLipinski CA (2004) Lead- and drug-like compounds: The rule-of-five revolution. Drug Discovery Today: Technol 1(4):337\u0026ndash;341. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ddtec.2004.11.007\u003c/span\u003e\u003cspan address=\"10.1016/j.ddtec.2004.11.007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVeber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD (2002) Molecular properties that influence the oral bioavailability of drug candidates. J Med Chem 45(12):2615\u0026ndash;2623. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1021/jm020017n\u003c/span\u003e\u003cspan address=\"10.1021/jm020017n\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTesta B, Kr\u0026auml;mer SD (2009) The biochemistry of drug metabolism\u0026ndash;an introduction: part 5. Metabolism and bioactivity. Chem Biodivers 6(5):591\u0026ndash;684. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/cbdv.200900022\u003c/span\u003e\u003cspan address=\"10.1002/cbdv.200900022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiMasi JA, Hansen RW, Grabowski HG (2013) The price of innovation: New estimates of drug development costs. J Health Econ 22(2):151\u0026ndash;185. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S0167-6296(02)00126-1\u003c/span\u003e\u003cspan address=\"10.1016/S0167-6296(02)00126-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":"","lastPublishedDoi":"10.21203/rs.3.rs-8840701/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8840701/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSpiro-fused heterocyclic compounds have been widely investigated in medicinal chemistry because of their broad spectrum of pharmacological activities. Two ligands, spiro-fused quinoxaline and spiro-fused benzoxazine were discussed for their antibacterial and anticancer properties by in silico molecular docking studies along with ADMET analysis. Molecular docking was carried out on identified compounds using AutoDock Vina and PyRx against the known antibacterial target aspartyl tRNA synthetase (PDB ID 1l0w) and EGFR kinase (PDB ID 6zj0), a protein involved for progression of cancer growth. Binding affinity of spiro-fused quinoxaline for both proteins (-12.1 kcal/mol and \u0026minus;\u0026thinsp;10.4 kcal/mol) was found to be higher than that of sprio-fused benzoxazine (-9.2 kcal/mol, and \u0026minus;\u0026thinsp;8.8kcal/mol), suggesting more potent inhibition ( Table\u0026nbsp;1,2). PyRx was used to calculate the binding affinities of both (spiro fused quinoxalines and spiro fused benzoxazines) against 1l0w protein i.e., -11.7, -8.7 respectively.\u003c/p\u003e \u003cp\u003eADMET profiling indicated significant pharmacokinetic differences: spiro-fused quinoxaline had low gastrointestinal absorption and could not penetrate the blood-brain barrier, whereas spiro-fused benzoxazine had excellent oral absorption, good BBB penetration, and optimal lipophilicity. Both compounds met Lipinski's rule of five, demonstrating water solubility, synthetic accessibility, and no inhibition of cytochrome P450 enzymes, indicating a low risk of drug-drug interactions. The SwissADME boiled-egg model confirmed that the two ligands had distinct distribution and absorption properties.\u003c/p\u003e \u003cp\u003eAll of these results show that spiro-fused benzoxazine is a better prospect for future therapeutic research because of its more promising pharmacokinetic characteristics, even if spiro-fused quinoxaline has a higher binding affinity. Together, these results highlight the need for spiro-fused quinoxaline derivatives to undergo structural optimization in order to balance efficacy and bioavailability, ultimately advancing these heterocycles as possible therapies.\u003c/p\u003e","manuscriptTitle":"Structural analyses of Spiro-Fused Quinoxaline and Benzoxazine Derivatives as both Antibacterial and Anticancer Agents: Molecular Docking and ADMET Evaluation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-20 17:49:03","doi":"10.21203/rs.3.rs-8840701/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":"190d5ed7-767d-4b4b-bc5e-ed91958761aa","owner":[],"postedDate":"February 20th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T15:12:25+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-20 17:49:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8840701","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8840701","identity":"rs-8840701","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}