In silico exploration of anticancer plant phytochemicals for EGFR-targeted lung cancer therapy

In silico exploration of anticancer plant phytochemicals for EGFR-targeted lung cancer therapy | 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 Article In silico exploration of anticancer plant phytochemicals for EGFR-targeted lung cancer therapy Chaity Debnath Dipa, Sharika Hossain, Md. Moinul Karim Chy, Mohammad Sheikh Farider Rahman, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6422271/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background: Mutations in the epidermal growth factor receptor (EGFR), particularly in the tyrosine kinase domain such as exon 19 deletions and the L858R point mutation, play a critical role in the development of non-small cell lung cancer (NSCLC). EGFR is a well-established therapeutic target in the management of NSCLC. Methods: In this study, we targeted the mutated EGFR kinase domain (L858R) using its crystal structure (PDB ID: 2EB3) to design EGFR tyrosine kinase inhibitors (TKIs). We curated a library of 687 phytoconstituents from four anticancer plants ( Camellia sinensis , Curcuma longa , Ginkgo biloba , and Vitis vinifera ) using the IMPPAT database. Kaempferol, morin, and isorhamnetin, all from Ginkgo biloba , emerged as promising candidates. Drug-likeness and ADMET analyses were performed to evaluate the pharmacokinetic and safety profiles of these compounds. Pharmacophore modeling and bioactivity score analysis were also conducted. Finally, molecular dynamics (MD) simulations were performed to assess the stability of the EGFR-ligand complexes. Findings: The docking studies revealed high binding energies for kaempferol (-8.5 kcal/mol), morin (-8.5 kcal/mol), and isorhamnetin (-8.7 kcal/mol) with the EGFR active site, compared to the reference drug, erlotinib (-6.9 kcal/mol). These compounds exhibited superior pharmacokinetic properties, including high gastrointestinal absorption and non-inhibition of P-glycoprotein activity, unlike erlotinib. Toxicity predictions showed mild immunotoxicity for morin and isorhamnetin, with all compounds demonstrating no hepatotoxicity and no inhibition of CYP3A4 or CYP2D6 enzymes. Structural analysis highlighted the hydroxyl groups in the selected compounds as key for hydrogen bond (H-bond) formation with EGFR residues, enhancing their inhibitory potential. MD simulations confirmed the stability of EGFR complexes with the selected compounds, showing lower average RMSD values and better convergence compared to the EGFR-erlotinib complex. Conclusion: This research underscores the potential of kaempferol, morin, and isorhamnetin as novel EGFR inhibitors derived from Ginkgo biloba for NSCLC treatment. These compounds demonstrated strong binding affinities, favorable pharmacokinetic properties, and stability in silico . Further in vitro and in vivo validation is necessary to confirm their efficacy against mutated EGFR in NSCLC. Biological sciences/Computational biology and bioinformatics Biological sciences/Drug discovery Structure-based drug design Epidermal Growth Factor Receptor (EGFR) Non-Small Cell Lung Cancer (NSCLC) Molecular Docking Molecular Dynamics Simulation Phytochemical Screening In silico ADME Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 1. Introduction Lung cancer is a pervasive and highly aggressive global disease, with an estimated 1.8 million deaths and 2.2 million new cases anticipated in 2020. It is the leading cause of cancer-related mortality in men and the second most common cause among women, following breast cancer (1). Non-small-cell lung cancer (NSCLC) constitutes over 85% of all lung cancer cases (2). One of the primary drivers of NSCLC is mutations in the epidermal growth factor receptor (EGFR) gene, particularly in the tyrosine kinase domain (3). These mutations, which occur in about 32% of NSCLC cases globally, vary by geography and patient demographics. EGFR mutations are notably more prevalent in East Asia (38–50%) compared to the Americas (24%) and Europe (14%), and more common in women, non-smokers, and those with adenocarcinomas (4–6). The most frequently observed EGFR mutations—exon 19 deletions (E19 dels) and the L858R substitution in exon 21—account for nearly 90% of EGFR mutations (5). Given the critical role of EGFR in NSCLC development and progression, targeting this protein has become a key strategy for improving patient outcomes (7). The advent of EGFR tyrosine kinase inhibitors (TKIs) has revolutionized the treatment of NSCLC, significantly improving progression-free survival, response rates, and quality of life for patients with EGFR mutations (8). However, despite the initial success of synthetic TKIs such as osimertinib, gefitinib, erlotinib, and afatinib, their long-term efficacy is often limited due to the development of resistance mechanisms, including secondary EGFR mutations (9–13). This highlights the need for alternative therapeutic options, such as natural products, which offer several advantages. Natural compounds provide a chemically diverse space that may yield novel EGFR inhibitors with unique modes of action, potentially overcoming resistance to synthetic TKIs (14). Additionally, natural products tend to have favorable safety profiles and lower toxicity, which can improve patient compliance and tolerance (15). Their eco-friendly and sustainable characteristics also align with modern pharmaceutical development trends (16). Although previous research has primarily focused on synthetic EGFR inhibitors, there is growing interest in exploring the potential of natural compounds as EGFR-targeted therapies. Recent studies have utilized computational methods such as virtual screening, pharmacokinetic predictions, molecular docking, and molecular dynamics (MD) simulations to identify promising inhibitors for various cancers (17–23). However, much of this work has either concentrated on synthetic inhibitors or failed to perform an extensive analysis of the pharmacological properties and interaction dynamics of natural compounds. Our study addresses this gap by employing a comprehensive in silico approach to identify potential EGFR inhibitors from natural sources. Specifically, we conducted blind molecular docking, rigorous validation protocols, pharmacophore modeling to assess ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties, bioactivity score analysis, and extended MD simulations. This multi-step screening process allowed us to pinpoint novel, biocompatible EGFR inhibitors while simultaneously evaluating their pharmacological properties and interaction dynamics, setting our study apart from others in the field. By utilizing a 100-nanosecond MD simulation with GROMACS, we were able to analyze the stability and interactions of protein-ligand complexes in detail, offering deeper insights into the compounds' inhibitory potential. Furthermore, pharmacophore modeling validated our ADMET evaluations, ensuring that only the most promising candidates would progress to further testing. Overall, our study presents a robust strategy for the discovery of natural EGFR inhibitors that could serve as safer, more sustainable alternatives to synthetic TKIs. 2. Methods The study utilizes virtual screening and advanced dynamics simulation methods to investigate anticancer plant phytochemicals for EGFR-targeted lung cancer therapy (24–33). The overall systematic procedure of the study is illustrated in a flowchart as shown in Supplementary Figure S1 . 2.1. Preparation of Protein The crystal structure of the mutated EGFR kinase domain (L858R) was obtained from the Protein Data Bank (PDB) using entry code 2EB3, which has a resolution of 2.84 Å with R-values of 0.236 (Free), 0.190 (Work), and 0.190 (Observed). This structure was chosen based on its origin from Homo sapiens and its inclusion in the repository of experimentally determined protein and nucleic acid structures (34,35). Post-retrieval, the protein underwent optimization using Discovery Studio 2020 (v 20.1) (36). This optimization process involved removing heteroatoms such as the co-crystallized ligand AMPPNP (Phosphoaminophosphonic Acid-Adenylate Ester), co-factors, water molecules, and metal ions. Molecular docking investigations focused on protein groups, chain A, and active sites. The protein structure was stabilized through energy minimization, achieved using the GROMOS96 force-field in the SWISS PDB Viewer (v 4.1.0) (37). Subsequently, each compound in our dataset was bound in close proximity to the active site of the investigated protein. 2.2. Validation of structure of protein The Ramachandran plot was employed to verify the structure of protein. This plot was generated through the utilization of PROCHECK server in conjunction with the PDBsum database ( https://www.ebi.ac.uk/thornton-srv/databases/pdbsum/ ) (accessed on 27th October, 2023) (38). The Ramachandran plot visually represents highly preferred, allowed, and disallowed phi (ϕ) and psi (ψ) angles of each amino acid in the protein (39). In addition, the structure was also examined utilizing the Protein Structure Analysis (ProSA) web tool (accessed on 27th October, 2023), which calculates the Z-score for a given input protein and determines the overall model quality of the protein (40). If the Z-score of a protein model is not in the range observed in native proteins, it is possible that errors may be present in the protein structure (41). 2.3. Preparation of Ligands Four plants ( Curcuma longa, Camellia sinensis, Ginkgo biloba , and Vitis vinifera ) were selected based on a rigorous literature review highlighting their compounds' significant anticancer properties. The selection of these plants for anticancer study in the modern world is supported by their combination of traditional use, safety profile, diverse chemical composition, and scientific evidence (42–45). Diverse mechanisms of action exhibited by these plants can potentially inhibit EGFR activity (46,47). A total of 687 compounds from these four plants were curated from the IMPPAT database (accessed on 16th November, 2023) for our study. IMPPAT is a comprehensive, ethnopharmacological, phytochemically rich, and diverse database of medicinal plants in India, providing an integrated platform for implementing cheminformatic approaches to expedite drug discovery from natural products ( https://cb.imsc.res.in/imppat/ ) (48). For each plant, a ligand library was meticulously generated in SDF format utilizing the PubChem database ( https://pubchem.ncbi.nlm.nih.gov/ ) (accessed on 16th November, 2023), which offers detailed chemical compound information, including structure, molecular formula, molecular weight, and more (49). The conformation transformation from 2D to 3D was facilitated using the OpenBabel molecule format converter (50). The ensuing docking investigation targeted erlotinib, a standard drug recognized as an inhibitor of the epidermal growth factor receptor (EGFR). Erlotinib received approval from the U.S. Food and Drug Administration for its efficacy as a first-line treatment in patients with metastatic NSCLC and tumors harboring EGFR exon 19 deletions or exon 21 (L858R) substitution mutations (51,52). The control molecule, erlotinib, was retrieved from the PubChem database by searching for its chemical structure. To further refine the investigation, ligands were transformed into PDB format utilizing PyMOL (v2.0), an open-source system software specifically designed for molecular visualization (53). 2.4. Identification of Binding Site The EGFR kinase domain, characterized by a bilobed structure with a critical ATP-binding pocket, is a key target for cancer treatment due to its role in regulating pathways like MAPK/ERK and PI3K/AKT (54–56). To accurately identify and visualize EGFR binding sites, we employed the CASTp 3.0 web server ( http://sts.bioe.uic.edu/castp/index.html?3igg ) (accessed on 17th November, 2023) for detailed surface topography analysis, identifying active pockets based on surface area and volume. The first pocket, with SA 869.613 Ų and volume 987.321 ų, was selected for further study. The server provided a list of residues from the A chain involved in potential interactions (57). We then used PyMOL to generate a cartoon representation of the EGFR protein, mapping the active site, ATP-binding sites, and other critical features. This systematic approach ensured a precise identification of EGFR binding sites, facilitating subsequent docking studies. 2.5. Virtual Screening and Blind Molecular Docking In recent years, molecular docking, a crucial tool in the in silico drug discovery, has undergone significant advancements. To ascertain interaction types and binding affinities, molecular docking analysis was employed, and molecular screening was conducted to identify lead compounds with the desired biological function (58). Firstly the ligands were energy minimised by utilising conjugate gradients algorithm and Universal Force Field (UFF) (59). The ligands and protein underwent blind docking using PyRx software (v 0.8), which is based on AutoDock Vina. PyRx is an open-source platform with an intuitive user interface, compatible with major operating systems such as Linux, Windows, and Mac OS (60). A grid box was defined for the protein with the center coordinates (X: 24.2000, Y: -58.031, Z: 7.7696) and dimensions (Å) X: 55.8611, Y: 52.5603, Z: 67.5867) to encompass the binding site. The bioactive conformations of the ligands were then simulated using AutoDock Vina. Torsion angles were calculated to map flexible and unbound rotation of molecules. Subsequently, the outcomes were examined using PyMOL and BIOVIA visualizers in Discovery Studio 2020 (61) and the binding poses of the protein-ligand complexes were observed. 2.6. Docking protocol validation A systematic validation method that included both redocking and alignment techniques was employed to assess the accuracy and consistency of the docking process (62,63). To confirm the validity of docking for the mutated EGFR tyrosine kinase, the co-crystal ligand (AMPPNP) was isolated, and then docking was conducted again using the same blind docking parameters. In PyMOL, the lowest energy docked conformer was aligned with the co-crystal AMPPNP to determine the root-mean-square deviation (RMSD) value. In general, an RMSD value of ≤ 2 Å or 0.2 nm suggests the reliability of a docking method (64). 2.7. ADME Analysis Assessing pharmacokinetic properties (PKs) is crucial in drug development, as they determine key factors contributing to the efficacy of oral medications. These factors include absorption rate and extent from the gastrointestinal tract, transfer efficiency to the site of action, metabolism, and elimination without adverse effects (65–67). ADME features encompass absorption (water solubility, human GI absorption, p-glycoprotein substrate and inhibitor, skin permeability), distribution (blood-brain barrier permeability, steady state volume of distribution for humans), metabolism (CYP2D6/CYP3A4 inhibitor, CYP2D6/CYP3A4 substrate), and excretion (drug total clearance) of drugs (68). The SwissADME web server ( http://www.swissadme.ch/ ) (accessed on 21st November, 2023) was utilized, providing a range of predictive models for physicochemical properties, pharmacokinetics, drug compatibility, and medicinal chemistry friendliness, including BOILEDEgg, iLOGP, and Bioavailability Radar (69). Additionally, the pkCSM web server ( https://biosig.lab.uq.edu.au/pkcsm/ ) (accessed on 21st November, 2023) was employed, which develops predictive models for critical ADMET properties in drug development using graph-based signatures (70). The pharmacokinetic response of specific drug candidates was analyzed by querying the SwissADME and pkCSM databases using the canonical SMILES of the potential compounds obtained from the molecular docking procedure. 2.8. Toxicity Test Performing toxicological testing during the drug development process is crucial for identifying any harmful properties of a compound and determining the appropriate dosage for human use. Computational methods for predicting toxicity offer significant advantages in terms of time, effort, and cost savings in the development of safe and effective drugs (71,72). To assess the safety profiles of the selected compounds, a variety of computational tools were employed. The ProTox-II server ( https://tox-new.charite.de/protox_II/ ) (accessed on 21st November, 2023) was utilized for an initial toxicity assessment, providing insights into hepatotoxicity, carcinogenicity, immunotoxicity, mutagenicity, and cytotoxicity of the compounds (73). Additionally, the web server pkCSM was employed to reveal further toxicity-related information, including AMES toxicity, hERG channel inhibitors, and T. pyriformis toxicity, and to estimate the toxic dose threshold of chemicals in humans (70). The canonical SMILES format data for the ligands were obtained from the PubChem database and used as input structures for the aforementioned servers. 2.9. Pharmacophore Modeling Pharmacophore modeling complements ADMET analysis by providing a structural understanding of ligand-target interactions and guiding lead optimization efforts in drug discovery (74). It is a computational technique used to identify the essential structural and chemical features (pharmacophores) required for ligands to bind to a target protein or biological target (75). Pharmacophoric features such as H-bond donors, acceptors, aromatic rings, and hydrophobic regions within ligand-receptor complexes can be identified and characterized through pharmacophore modeling. These features represent the essential molecular interactions required for ligand binding and biological activity. Numerical information about the pharmacophoric features (H-bond donors, acceptors) of selected compounds was obtained using swissADME during the prediction and analysis of ADMET properties in our study. The generation of pharmacophore models based on input ligand structures and visualization of the spatial arrangement of pharmacophoric features within the binding site were conducted using the Pharmit web server ( https://pharmit.csb.pitt.edu/ ) (accessed on 1st December, 2023). Insights into the optimal binding geometry of ligands within the target binding site are provided by these models (76). The preferred binding modes of ligands within the target binding site can be predicted by pharmacophore models, which is valuable for rationalizing the observed ADMET properties of selected compounds (77). 2.10. Bioactivity Score Analysis For establishing a compound as an EGFR tyrosine kinase inhibitor, its bioactivity score specifically in the ‘kinase inhibitor' category is crucial. A compound having bioactivity score > 0.00 in the kinase inhibitor category, makes it a promising candidate for EGFR tyrosine kinase inhibition (78). The higher positive bioactivity score in this category indicates the greater chance of effective EGFR tyrosine kinase inhibition (79). The bioactivity scores in various categories (GPCR ligands, ion channel modulator, kinase inhibitor, protease inhibitor) and topological polar surface area (TPSA) of our selected compounds, along with erlotinib (control), were determined and analyzed using Molinspiration, a cheminformatics server ( https://www.molinspiration.com/ ) (80) (accessed on 2nd November, 2023), where the structures of the compounds were used as input. 2.11. Protein-Ligand Interactions Protein-ligand interactions are critical for processes like signal transduction, immunoreaction, and gene regulation, making them essential for understanding biological regulation and identifying therapeutic targets (81). In this study, Biovia Discovery Studio (36) was used to analyze the binding characteristics of EGFR with ligands such as kaempferol, morin, isorhamnetin, and erlotinib (control). The analysis included various interaction types, such as H-bonds and pi-alkyl interactions. Key parameters assessed were binding affinities, interacting residues, H-bond distances, and bond angles (Angle DHA), with a focus on the H-bond metrics due to their significance in protein-ligand stability and effectiveness (82). 2.12. Molecular dynamics simulation The docked complexes of EGFR with morin, kaempferol, isorhamnetin, and the standard drug erlotinib were subjected to MD simulations. These simulations aimed to explore the stabilizing effects of the ligands, investigate their binding modes at the EGFR binding site, and compare their inhibitory potentials to that of the standard drug. The 100 ns MD simulations of in triplicate run were afforded for each complex on the HPC cluster at Bioinformatics Resources and Applications Facility (BRAF), C-DAC, Pune with preinstalled Gromacs 2020.4 (83) package (accessed on 25th November, 2023). Briefly, the MD simulation steps include the preparation of input topologies of ligands and EGFR. The input topologies for ligands were prepared using the Acpype (84) based on the AMBER program (85). The topology for the EGFR protein structure was prepared using the Amber ff99SB protein force field (86). The complexes of EGFR with respective ligands were solvated in a dodecahedron unit cell with TIP3P water molecules (87). The resultant solvated systems were neutralized with the addition of sodium or chloride counter-ions and further subjected to the energy minimization step with steepest descent and conjugate gradient minimization algorithms until the thresholds of Fmax less than 1000 kJ mol − 1 nm − 1 were reached. Later, all the systems were equilibrated at constant volume and temperature of 300 K (NVT) condition and then at constant volume and pressure (NPT) conditions for 1 ns each, where the modified Berendsen thermostat (88) was employed to achieve NVT conditions, while Berendsen barostat (89) was employed to achieve NPT conditions of 1 atm. Each NVT and NPT equilibrations were performed with short 1 ns simulations. The equilibrated systems were subjected to the final production phase MD simulations in triplicate for the duration of 100 ns, using the Berendsen thermostat and Parrinello-Rahman barostat (90). The covalent bonds were restrained with the LINCS algorithm (91) and the long-range electrostatic interaction energies were measured with the Particle Mesh Ewald method (PME) (92) with the cut-off of 12 Å. Post-production phase MD simulations, the trajectories of each simulation after removing the periodic boundary conditions, along with the concatenated trajectories of triplicate runs, were employed in further analysis. The analysis included assessing the stability of each system in terms of root mean square deviation (RMSD) in the backbone atoms of EGFR from the starting equilibrated structure, as well as the RMSD in ligand atoms relative to the position of the EGFR backbone atoms. The analysis also included the root mean square fluctuation (RMSF), radius of gyration, solvent-accessible surface area (SASA), and H-bond interactions (93) for each MD simulation run. The average of each parameter was also considered for each analysis. Further, the buried solvent-accessible surface area (B-SASA) was calculated to get the insights into the shared SASA by the ligand and the EGFR target protein. In this calculation the total SASA was calculated for the EGFR, the ligands, and the EGFR-ligand complex. The buried solvent accessible surface area was calculated through the Eq. (1). \(\:B-SASA\:\left({nm}^{2}\right)=0.5\:({SASA}_{L}+\:{SASA}_{EGFR}-\:{SASA}_{EGFR-L\:complex}\) …. (Eq. 1) Where, B-SASA is buried solvent accessible surface area shared between Ligand and EGFR, SASA L is total SASA of ligand, SASA EGFR is total SASA of EGFR protein, and SASA EGFR−L complex is total SASA of EGFR-ligand complex. Further, to obtain detailed insights into non-bonded interactions within a distance of 3.5 Å the contact frequency for the contacts between the ligand atoms and the side chain atoms was analyzed using the program MDCiao (94). The results of contact frequency were compared with the H-bonds between the binding site residues and the respective ligands for each MD simulation run. Principal component analysis (PCA) (95) was performed on each MD simulation trajectory from a triplicate run to investigate the major path of motions in each complex. For this, the covariance matrix was constructed for the protein backbone atoms and respective ligands using the gmx covar program. The eigenvectors, representing the path of motion, and eigenvalues, reflecting the mean square fluctuations, were derived by diagonalizing the covariance matrix. The first two eigenvectors also referred to as principal components (PC1 and PC2) were further used as reaction coordinates to obtain Gibb’s free energy landscape (96). Further, the cluster analysis was performed using the TTClust program (97) to identify the most prominent conformations that existed in each of the MD simulation trajectories. Molecular mechanics energies combined with General Born surface area continuum solvation (MM-GBSA) calculations were performed on the trajectories sampled at each 100 ps from 50 to 100 ns simulation period, employing the GMX_MMPBSA program (98). The entropic energies were taken into the account and the ΔG binding kcal/mol was calculated for each trajectory. Further, the MM-GBSA calculations were performed on the average trajectory of each protein-ligand complex under study. The protein-ligand structures were rendered in PyMOL (53) and graphs were obtained from XMGRACE (99). Gibb’s FEL plots were generated using a Python-based Matplotlib package (100). 3. Results 3.1. Validation of the protein structures The obtained Ramachandran plot analysis represented that the majority of the amino acid residues are found within the most favored regions of the protein used. The protein model is reasonably good. The Z-score of -6.3 suggests that the protein model is likely of good quality, with an energy profile consistent with correctly folded, native proteins (Supplementary Figure S2 ). 3.2. Binding Site Analysis The binding site analysis of EGFR provided several key insights. CASTp 3.0 identified critical residues at A:837 (active site) and A:745 and A:855 (ATP-binding sites), which are essential for the receptor's function. The server also generated a list of amino acid residues from the A chain of the protein ( Supplementary Table S1 ), highlighting regions that may interact with potential inhibitors. Surface representations of these binding sites, produced by CASTp 3.0, were further visualized in 3D using PyMOL, confirming their spatial arrangement within the protein ( Supplementary Figure S3 ). This detailed mapping facilitated the accurate selection of a grid box for molecular docking simulations. The plant-derived compounds showed significant binding affinity to these identified sites, indicating their potential to reduce cancer cell proliferation and positioning them as promising candidates for anticancer therapy development. 3.3. Molecular Docking Analysis The three-dimensional (3D) structures of all the phytoconstituents collected from the four anticancer plants and the target protein were retrieved from PubChem and PDB databases respectively. Molecular docking results showed that out of 687 phytoconstituents, only 60 phytoconstituents had docking scores ranging from − 8.5 to -10.3 kcal/mol, while the commercially marketed drug erlotinib had a docking score of -6.9 kcal/mol against EGFR ( Supplementary Table S2 ). 3.4. Docking Protocol Validation During the superimposition analysis of the cocrystal and re-docked native ligand, an initial comparison was made between 31 atoms of each through pairwise scoring. The calculated RMSD value was 1.912 Å ( Supplementary Figure S4 ), which falls within the favorable range (≤ 2 Å). This indicates an almost identical alignment between the cocrystal and re-docked structures, with no discernible deviations. It can be inferred that utilizing the same docking methodology is likely to yield precise and reproducible conformers for the screening of the compound library sourced from the curated literature survey. 3.5. ADME Analysis Orally administered drugs must have a molecular weight of less than 500 Da, a LogP value of less than five, five or fewer H-bond donor sites, and ten or fewer H-bond acceptor sites, in accordance with the Lipinski Rule of Five. The bioavailability of the molecule may be adversely affected by a drug candidate's infringement of one of the aforementioned regulations. Out of 60 compounds which showed docking scores in the range from − 8.5 to -10.3 kcal/mol for EGFR, only 8 compounds (typhasterol, fisetin, kaempferol, quercetin, episesamin, 6-deoxyjacareubin, isorhamnetin and morin) adhered to Lipinski Rule of Five. The eight compounds showed high rate of passive gastrointestinal (GI) absorption. Except episesamin, the compounds lacked the ability to cross the blood-brain barrier (BBB). The compounds have favorable solubility properties. The favorable ADME properties of these compounds made them promising candidates for further investigation (Supplementary Table S2 ). 3.6. Toxicity Prediction The ProTox-II server evaluation of eight compounds and erlotinib (control) revealed varying toxicological profiles. Typhasterol exhibited severe mutagenicity and mild carcinogenicity. Fisetin showed severe carcinogenicity. Episesamin demonstrated mild carcinogenicity and severe immunotoxicity. 6-deoxyjacareubin had severe immunotoxicity and mild mutagenicity. Quercetin was associated with mild carcinogenicity and mutagenicity. Morin and isorhamnetin were noted for mild immunotoxicity, while kaempferol showed no toxicity. Erlotinib (control) displayed severe hepatotoxicity, immunotoxicity, mutagenicity, and cytotoxicity. Carcinogenicity and mutagenicity are significant concerns, as they could potentially lead to cancer or genetic mutations, undermining the therapeutic efficacy of a compound. Thus, avoiding compounds with known carcinogenic or mutagenic properties is essential for ensuring the safety and effectiveness of anticancer drug candidates. Mild immunotoxicity may be acceptable if the benefits outweigh the risks. Based on these findings, kaempferol (CID5280863), morin (CID5281670), and isorhamnetin (CID5281654) emerged as the top candidates due to their favorable toxicological profiles ( Supplementary Table S2 ). Further analysis using pkCSM suggested that these three candidates are unlikely to be mutagenic and may not inhibit the hERG channel. Additionally, the compounds showed nontoxicity in T. pyriformis assays and minimal acute toxicity in rats (Table 1 ). Consequently, kaempferol, morin, and isorhamnetin are considered promising pharmaceutical candidates due to their commendable oral bioavailability and protective properties. Table 1 The results of Absorption, Distribution, Metabolism, and Excretion (ADME) and Toxicity parameters of the studied three compounds along with erlotinib using pkCSM web server Compound Name Parameters Normal Range Erlotinib (control) Kaempferol Morin Isorhamnetin Absorption Intestinal absorption (human) (% Absorbed) > 30 95.549 74.29 75.408 76.014 Skin Permeability (log Kp) < -2.5 -2.738 -2.735 -2.735 -2.735 P-glycoprotein substrate Preferably "No" No Yes Yes Yes P-glycoprotein I inhibitor Preferably "No" Yes No No No P-glycoprotein II inhibitor Preferably "No" Yes No No No Distribution VDss (human) (log L/kg) -0.5 to 1.5 -0.053 1.274 1.229 1.123 Metabolism CYP2D6 substrate Preferably "No" No No No No CYP3A4 substrate Preferably "No" Yes No No No CYP2D6 inhibitior Preferably "No" No No No No CYP3A4 inhibitior Preferably "No" Yes No No No Excretion Total Clearance (log ml/min/kg) -1.0 to 1.0 0.591 0.477 0.486 0.508 Renal OCT2 substrate Preferably "No" No No No No Toxicity AMES toxicity Preferably "No" No No No No Max. tolerated dose (human) (log mg/kg/day) > 0 0.002 0.531 0.537 0.576 hERG I inhibitor Preferably "No" No No No No hERG II inhibitor Preferably "No" Yes No No No Oral Rat Acute Toxicity (LD50) (mol/kg) > 2.0 2.368 2.449 2.413 2.407 T.Pyriformis toxicity (log ug/L) Close to 0 0.334 0.312 0.308 0.296 3.7. Pharmacophore Models Analysis Pharmacophore models were generated using Pharmit for our three selected compounds kaempferol, morin, and isorhamnetin, as well as the reference compound, erlotinib, to visualize the pharmacophoric features (H-bond donors, acceptors, aromatic rings, and hydrophobic regions) of these compounds and their spatial arrangement within the binding site. The importance of hydrogen donor and acceptor groups is generally emphasized due to their specificity, contribution to binding affinity, role in selectivity, flexibility, and influence on solubility and ADMET properties (101,102) being considered more important. According to the generated pharmacophore models of the compounds, the number of hydrogen donor and acceptor groups resemble the numerical values of the pharmacophoric features predicted by swissADME server. The number of hydrogen donor and acceptor groups, their dimensions and radius of the compounds are presented in Table 2 and visualized in Fig. 1 . The dimensions (x, y, z coordinates) and radius of the hydrogen donors and acceptors of our selected compounds provide valuable insights into the spatial arrangement and interaction patterns with the target protein. The x, y, z coordinates indicate the spatial location of the pharmacophoric features within the binding site of the target protein (103). The radius of pharmacophoric features provides information about their size and shape. This information is crucial for assessing steric complementarity between our selected compounds and their binding sites (104). The number of hydrogen donor and acceptor groups of our three selected compounds are far more than those of erlotinib (control). The presence of more hydrogen donor and acceptor groups in our selected compounds facilitates the formation of favorable H-bonding interactions with the target protein, leading to enhanced specificity, optimal binding geometry, and improved binding affinity (104,105). Another observable fact is in our three selected compounds, some hydrogen donor and acceptor groups have the same dimension values (x, y, z, and radius), it indicates favorable spatial arrangement of each compound leading to enhanced conformational stability, compounds' efficacy, selectivity and pharmacological properties (106). Additionally, the radius value for the pharmacophoric features of the compounds is '1,' indicating that each feature occupies a relatively small volume in three-dimensional space. It also indicates compact and precise spatial arrangement which promotes optimal ligand-receptor interactions, minimizes steric hindrance, and allows for fine-tuning of molecular interactions critical for drug efficacy, potency and specificity (107). All the dimensions properties obtained from pharmacophore modeling helped us to justify the ADMET properties of our selected compounds as drug candidates. Table 2 H-bond Donors (HD), H-bond Acceptors (HA), Dimensions (X,Y,Z) and Radius of Kaempferol, Morin, Isorhamnetin Along With Erlotinib (Control) Using Pharmit Web Server Compound Name Interactions X Y Z Radius Erlotinib HD 2.5 -0.2 -1.1 1 HA 0.3 3.2 -0.1 1 HA 2.4 2.1 -0.5 1 HA -2.4 -1.5 -0.9 1 HA -3.8 0.8 -0.3 1 HA -4.4 -2.7 0.7 1 HA -6.6 1.0 0.1 1 Kaempferol HD 1.0 2.6 0.0 1 HD -4.2 1.8 0.0 1 HD -4.4 -3.0 0.0 1 HD 6.2 -0.9 0.0 1 HA 1.0 2.6 0.0 1 HA -4.2 1.8 0.0 1 HA -1.8 2.8 0.0 1 HA -4.4 -3.0 0.0 1 HA 6.2 -0.9 0.0 1 Morin HD 0.9 2.6 0.1 1 HD -4.3 1.8 0.0 1 HD 2.1 -0.1 -2.3 1 HD -4.5 -3.0 0.0 1 HD 6.1 -0.9 0.3 1 HA 0.9 2.6 0.1 1 HA -4.3 1.8 0.0 1 HA -1.9 2.8 0.1 1 HA 2.1 -0.1 -2.3 1 HA -4.5 -3.0 0.0 1 HA 6.1 -0.9 0.3 1 Isorhamnetin HD 0.5 2.6 -0.3 1 HD -4.6 1.8 0.2 1 HD -4.8 -3.0 0.3 1 HD 5.7 -0.9 -0.8 1 HA 0.5 2.6 -0.3 1 HA -4.6 1.8 0.2 1 HA 4.6 -0.6 1.7 1 HA -2.2 2.8 -0.1 1 HA -4.8 -3.0 0.3 1 HA 5.7 -0.9 -0.8 1 3.8. Inhibitory Properties Prediction Our selected compounds exhibited bioactivity scores > 0.00 individually in the 'kinase inhibitor' category, indicating their potential for EGFR tyrosine kinase inhibition. In contrast, the bioactivity scores of the three compounds in other categories (GPCR ligands, ion channel modulator, and protease inhibitor) were less than 0.00, suggesting minimal off-target effects and highlighting their specificity as effective EGFR TKIs. These off-target effects, such as cardiotoxicity, dermatologic reactions, gastrointestinal toxicity, hepatotoxicity, and myelosuppression, are more likely to occur with erlotinib (control), as its bioactivity scores in these categories were greater than 0.00 ( Table 3 ) (Fig. 2). Table 3 Bioactivity scores for kinase inhibition, GPCR ligand activity, ion channel modulation, and protease inhibition of Kaempferol, Morin, and Isorhamnetin, compared with Erlotinib (control), as determined using the Molinspiration web server. Compound Name Kinase inhibitor GPCR ligands Ion channel modulator Protease inhibitor Erlotinib (control) 0.68 0.13 0.11 -0.16 Kaempferol 0.21 -0.10 -0.21 -0.27 Morin 0.22 -0.09 -0.22 -0.27 Isorhamnetin 0.25 -0.10 -0.26 -0.30 3.9. Protein-Ligand Interaction Analysis The analysis of non-bond interactions is detailed in Table 4 and visualized in Fig. 3 . The interaction analysis revealed that the three selected compounds (kaempferol, morin, and isorhamnetin) demonstrate a more extensive and varied interaction profile with the EGFR protein compared to erlotinib (control). These compounds not only form H-bonds with critical residues such as MET793 and THR790, which are key to the function of the EGFR protein, but also engage in numerous pi-alkyl interactions that further stabilize the protein-ligand complex. Erlotinib primarily interacts with CYS797 and MET793, forming fewer conventional H-bonds compared to the selected compounds. Conventional H-bonds are crucial contributors to the stability of the protein-ligand complex. A greater number of H-bonds typically leads to a more stable and stronger binding interaction between the ligand and the protein (108). The H-bond distances for our selected compounds were generally within the optimal range (approximately 2.0 to 3.0 Å), indicating strong and stable interactions (109). Specifically, kaempferol exhibited H-bond distances ranging from 1.977 Å to 2.886 Å, morin showed distances from 2.010 Å to 2.445 Å, and isorhamnetin displayed H-bond distances from 1.978 Å to 2.926 Å. These distances are shorter, and therefore suggest stronger interactions, compared to those observed for erlotinib, which ranged from 2.098 Å to 2.993 Å. H-bond angles also play a critical role in the strength and stability of these interactions, with angles closer to 180° typically resulting in stronger and more stable bonds (110). Kaempferol displayed bond angles ranging from 100.125° to 157.454°. Although kaempferol has a lower minimum angle (100.125°), it achieves a higher maximum angle (157.454°), indicating the potential for stronger bonding compared to erlotinib. Morin exhibited bond angles ranging from 99.449° to 157.488°, with its highest angle (157.488°) also indicative of strong H-bonds. Isorhamnetin showed the widest range of bond angles (90.198° to 157.888°), which, while indicating variability, still includes angles that contribute to strong bonding. Based on the amino acid residues involved, interaction types, bond distances, and bond angles, it is evident that our selected compounds demonstrate superior interaction profiles with EGFR tyrosine kinase compared to the control ligand, erlotinib. The shorter bond distances and more optimal angles observed in kaempferol, morin, and isorhamnetin contribute to stronger and more stable interactions with the protein, suggesting that these compounds could be more effective inhibitors than erlotinib (control). Table 4 Non-bond interactions between amino acid residues of EGFR protein and our selected three compounds along with erlotinib. Name (PubChem CID) Binding Affinity kcal/mol Residue in Contact Interaction Type Bond Distance (Ǻ) Angle DHA (º) Erlotinib (Control) CID (176870) -6.9 A:CYS797 A:MET793 A:GLY719 A:ASP800 A:LYS745 A:MET766 A:LEU788 A:VAL726 A:LYS745 A:LEU718 A:VAL726 Conventional H-bond Conventional H-bond Carbon H-bond Carbon H-bond Alkyl Alkyl Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl 2.24909 2.09822 2.8479 2.99378 3.9483 5.06506 4.23104 5.20555 5.34892 4.16972 5.03169 156.778 145.615 136.393 113.076 Kaempferol CID (5280863) -8.5 A:THR790 A:MET793 A:MET766 A:LEU718 A:VAL726 A:ALA743 A:LEU844 A:LEU718 A:VAL726 A:VAL726 A:ALA743 A:LYS745 Conventional H-bond Conventional H-bond Conventional H-bond Conventional H-bond Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl 2.46572 1.97773 2.88692 2.77292 4.549 4.08513 5.01673 4.30011 4.77678 5.32702 5.12763 4.71305 119.28 157.454 122.832 100.125 Morin CID (5281670) -8.5 A:MET793 A:GLN791 A:MET793 A:VAL726 A:ALA743 A:LEU844 A:LEU718 A:VAL726 A:VAL726 A:ALA743 A:LYS745 Conventional H-bond Conventional H-bond Conventional H-bond Conventional H-bond Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl 2.04975 2.40729 2.0101 2.44531 4.50785 4.05262 5.02681 4.39575 4.68408 5.36186 5.22323 4.55869 157.488 148.922 137.459 99.449 Isorhamnetin CID (5281654) -8.7 A:THR790 A:MET793 A:THR854 A:GLU762 A:MET766 A:GLN791 A:MET793 A:GLU762 A:VAL726 A:ALA743 A:LEU844 A:LEU718 A:VAL726 A:VAL726 A:ALA743 A:LYS745 Conventional H-bond Conventional H-bond Conventional H-bond Conventional H-bond Conventional H-bond Conventional H-bond Conventional H-bond Carbon H-bond Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl Pi-Alkyl 2.39823 1.97848 2.92654 2.28615 3.07823 2.56702 2.54115 2.77107 4.52382 4.09176 5.04263 4.26006 4.83164 5.22413 5.12197 4.62102 120.831 157.888 91.852 127.698 115.095 132.659 90.198 127.988 3.10. Molecular Dynamics studies 3.10.1. Root mean square deviation The RMSD in backbone atoms of each EGFR ligand complex was evaluated for the triplicate runs. The complexes with kaempferol and isorhamnetin showed quite stable RMSD throughout the simulation period (Fig. 4A and B ). In both the complexes, the average RMSD for triplicate runs was around 0.3 nm. In the case of EGFR morin complex the MD simulation run 1 showed significant deviation after around 60 ns. The average RMSD for triplicate runs for this complex was around 0.32 nm (Fig. 4C). The MD simulation run 1 showed significantly higher RMSD compared to run 3 in the case of the EGFR erlotinib complex (Fig. 4D). The average of triplicate runs was around 0.32 nm for this complex. The RMSD in ligand atoms relative to the EGFR backbone atoms was evaluated in triplicate for all the complexes. The results showed that the complexes of EGFR with kaempferol and isorhamnetin had reasonably stable RMSD with overall deviations within the range of 0.1 to 0.3 nm (Fig. 5A and B ). In MDS run 2 the RMSD in kaempferol showed slightly larger deviations during the simulation period 20 to 80 ns. The average RMSD of triplicate runs for EGFR complexes with kaempferol and isorhamnetin was around 0.2 nm. A significant difference was found in MD simulation run 1 and run 3 in the case of the EGFR morin complex (Fig. 5C). While run 2 and run 3 are almost stabilizing after around 40 ns simulation period. The average of triplicate runs was around 0.25 nm. In the case of the EGFR erlotinib complex, the RMSD deviations were in the range of 0.2 to 0.4 with an average of triplicate runs around 0.3 nm (Fig. 5D). 3.10.2. Root mean square fluctuation evaluation The root mean square fluctuations in the side chain atoms of each residue of EGFR in all the complexes were evaluated for the triplicate runs. The results showed the major fluctuations in the residues in the range 850–875. In the case of EGFR kaempferol complex the MD simulation run 3 showed slightly higher fluctuations compared to run 1 and run 2 (Fig. 6A). Further, the terminal residues beyond residue number 975 showed slightly larger RMSF reaching beyond 0.6 nm. In the case of the EGFR isorhamnetin complex, almost all the MD simulation runs showed similar RMSF. However, MD simulation run 1 particularly showed slightly higher RMSF for a few residues in the range 855–860 (Fig. 6B). The EGFR morin complex also showed almost similar RMSF in all the triplicate MD simulation runs, except for a few residues in the range 855–860 in run 3 which showed slightly higher RMSF (Fig. 6C). Notably the terminal residues beyond 960 showed significantly higher RMSF reaching 0.8 nm, compared to residues in the same range in other complexes. In the case of EGFR erlotinib complex the RMSF in all the triplicate MD simulation runs was almost similar (Fig. 6D). 3.10.3. Radius of gyration evaluation To investigate whether the EGFR structure remained in folded and compact form when bound to the respective ligands during the simulation, the analysis of the radius of gyration (Rg) was performed. The results showed that all the complexes under study had the Rg within the range of 2 to 2.15 nm. Particularly in the case of EGFR kaempferol complex the Rg stabilized after 20 ns in all the triplicate runs (Fig. 7A). The MD simulation run 2 showed a slightly lower Rg, however the average Rg for the triplicate run was around 2.075 nm. In the case of EGFR isorhamnetin complex the Rg for all the runs slightly deviated until the end of the simulation with an overall average of around 2.075 nm (Fig. 7B). Here, the MD simulation run 1 showed the lowest Rg while the run 2 showed the higher Rg. The Rg in the EGFR morin complex stabilized after 25 ns to an average of around 2.05 nm in all the triplicate runs (Fig. 7C). In the case of the EGFR erlotinib complex, the Rg stabilized after 20 ns simulation period in all the triplicate runs (Fig. 7D). However, the MD simulation run 1 had significantly lower Rg compared to run 2. The average Rg for the triplicate run was around 0.075 nm. 3.10.4. Solvent accessible surface area analysis The analysis of solvent-accessible surface area (SASA) provides an impetus for the effects of solvent exposure on protein structure particularly on the deeply buried cavities. The results showed that SASA remained within a range of 160 to 190 nm 2 for all the complexes under study. The average SASA for the EGFR complex with kaempferol was around 175 nm 2 , whereas the SASA was slightly higher for the MD simulation run 3 (Fig. 8A). The SASA stabilized after around 20 ns in the case of EGFR isorhamnetin complex where the average SASA for the triplicate run was around 175 nm 2 , where the steady state was observed after an 80 ns simulation period (Fig. 8B). In the case of the complex with morin, reasonable stability was seen after around 50 ns simulation period where the average SASA for the triplicate run was around 170 nm 2 (Fig. 8C). In the case of the EGFR erlotinib complex, the SASA stabilized after around 20 ns simulation period with an average triplicate run of 175 nm 2 (Fig. 8D). Further, the B-SASA calculated for each run and for the concatenated trajectory. The results are given in Table 5 . The B-SASA for complex with erlotinib was larger compared to other complexes with the B-SASA for run 1, run 2, and run 3 of 4.483, 4.667, and 4.519 nm 2 , respectively. Comparably, the complex with isorhamnetin showed the B-SASA in the range 3.763 to 3.676 nm 2 , while the complex with kaempferol showed the B-SASA in the range 3.220 to 3.396 nm 2 for triplicate runs. The complex with morin showed the lowest B-SASA among all the complexes which was in the range 3.237 to 3.295 nm 2 . Table 5 Buried solvent-accessible surface area analysis Complex/Run number SASA (Protein) (nm 2 ) SASA (Ligand) (nm 2 ) SASA (Protein-ligand complex) (nm 2 ) Buried SASA (nm 2 ) EGFR Kaempferol Complex Run 1 172.830 (5.087) 4.885 (0.204) 170.923 (5.087) 3.396 (0.200) Run 2 170.411 (4.132) 4.789 (0.197) 168.438 (4.129) 3.381 (0.180) Run 3 178.247 (3.787) 4.762 (0.197) 176.569 (3.794) 3.220 (0.165) EGFR Isorhamnetin Complex Run 1 176.416 (4.412) 5.269 (0.198) 174.333 (4.444) 3.676 (0.180) Run 2 172.715 (4.683) 5.201 (0.186) 170.389 (4.710) 3.763 (0.200) Run 3 176.002 (2.960) 5.307 (0.191) 173.888 (2.960) 3.763 (0.200) EGFR Morin Complex Run 1 171.720 (5.026) 4.843 (0.193) 170.087 (5.019) 3.237 (0.175) Run 2 172.876 (4.438) 4.832 (0.210) 171.116 (4.463) 3.295 (0.175) Run 3 169.930 (4.847) 4.768 (0.200) 168.140 (4.895) 3.288 (0.180) EGFR Erlotinib Complex Run 1 176.158 (4.881) 7.232 (0.322) 174.422 (4.716) 4.483 (0.265) Run 2 171.041 (4.777) 7.229 (0.305) 168.935 (4.861) 4.667 (0.270) Run 3 176.267 (4.316) 7.276 (0.293) 174.505 (4.463) 4.519 (0.240) The standard deviations are given in parentheses. 3.10.5. H-bond analysis The complex of EGFR with kaempferol showed four H-bonds formed consistently throughout the simulation period in MD simulation run 1 and run 3, while in MD simulation run 2 around three consistent H-bonds were formed (Fig. 9A). In the case of EGFR isorhamnetin complex four consistent H-bonds were formed in all the triplicate MD simulation runs (Fig. 9B). In the case of both the EGFR-kaempferol and EGFR-isorhamnetin complexes, a maximum of five H-bonds were occasionally observed during the simulation. In the case of the EGFR morin complex five consistent H-bonds were formed in all the triplicate MD simulation runs (Fig. 9C). Occasionally, in run 1 and run 2, a maximum of six H-bonds were observed. In the case of the EGFR erlotinib complex, two consistent H-bonds were formed in MD simulation run 2 and run 3, while in run 1 one consistent H-bond was formed (Fig. 9D). Further, to investigate which binding site residues are involved in H-bond formation, the H-bond interactions between ligand and EGFR were captured from the initial equilibrated trajectory, at trajectories extracted at 25, 50, 75, and 100 ns simulation period. For this analysis, the trajectories of triplicate runs were combined to get the average trajectory. The EGFR kaempferol complex showed a consistent H-bond with the residue GLN791 in all the extracted trajectories (Fig. 10A). The residue MET793 participated in H-bond formation, except in the 75 ns trajectory. The residues GLU762 and THR790 also formed the H-bond as seen in the equilibrated trajectory and 75 ns trajectory. The EGFR isorhamnetin complex showed a very consistent H-bond with the residue MET793 in all the trajectories (Fig. 10B). The residue GLN791 also formed a consistent H-bond in all the trajectories, except at 75 ns. In addition to these H-bonds, the equilibrated trajectory showed a H-bond with GLU762 and THR790. In 25 ns trajectory, the H-bond with Thr290 broke and a new H-bond with residue LYS745 was formed. However, this new H-bond with residue LYS745 again broke and reformed only in a 100 ns trajectory. In the case of the EGFR morin complex, the residues GLU762, GLN791, and MET793 formed a consistent H-bond in all the trajectories (Fig. 10C). The residue ASP855 also formed a consistent H-bond in all the trajectories, except the equilibrated trajectory. In addition to these aforementioned consistent H-bonds, the equilibrated trajectory showed a transient H-bond with LEU718, THR790, and THR854. These transient H-bonds reformed at various time intervals where the H-bond was reformed with the residue THR790 in the 50 ns and 100 ns trajectory, while with the residue THR854 in the 75 ns and 100 ns trajectory. The EGFR erlotinib complex showed a consistent H-bond with the residue MET793, except in the 25 ns trajectory (Fig. 10D). The equilibrated trajectory also showed a H-bond with residue CYS797. The 25 ns trajectory showed no H-bonds. 3.10.6. Contact frequency analysis To further investigate the residues involved in the formation of non-bonded interactions such as H-bonds, the contact frequency between respective ligands and residues within a 3.5 Å distance was analyzed. In the case of EGFR kaempferol complex, all the triplicate MD simulation runs showed GLN790, THR790, and MET793 having a contact frequency of more than 90%, with almost 100% contact frequency for GLN790 (Fig. 11A). In addition to these three residues, the MD simulation run 3 showed that GLU762 had a contact frequency of more than 90%. Further, run 1 showed a contact frequency of around 70% for ALA743 and around 60% for THR854. Run 2 showed a contact frequency of around 74% for ALA743 and around 60% for LEU844. While run 3 showed a contact frequency of around 70% for LYS745. In the case of EGFR isorhamnetin complex, only MD simulation run 1 and run 2 showed MET793 having a contact frequency of more than 90%, while run 3 showed a contact frequency of around 70% for MET793 (Fig. 11B). The residues LEU792, GLY796, and ASP855 showed a contact frequency of less than 25% in all triplicate MD simulation runs. The residue GLY719 in MD simulation run 1, residue LEU718 in run 2, and residue LYS745 in run 3 also showed a subtle contact frequency of around 5%. In the case of EGFR morin complex, the residues GLU762, THR790, ASP855, and MET793 showed having more than 90% contact frequency in all the triplicate MD simulation runs (Fig. 11C). Particularly, the contact frequency of almost 100% was observed for the residues GLU762 and ASP855 in all the runs. Further, residue GLN791 in run 1 and residue THR854 in run 2 and run 3 also showed more than 90% contact frequency. In the case of the EGFR erlotinib complex, the residue THR790 showed a contact frequency of more than 90% in all the triplicate MD simulation runs (Fig. 11D). In the MD simulation run 1 and run 3 for this complex, the residues MET793, LYS745, LEU718, and ALA743 showed a contact frequency between 50 to 75%. In MD simulation run 2 the residues CYS797, ALA743, LYS745, and THR854 showed the contact frequency between 50 to 75%. 3.10.7. Principal component analysis and Gibb’s free energy analysis Using the first two principal components (PC1 and PC2) Gibb’s Free Energy Landscape (FEL) plots were generated. The backbone atoms of EGFR along with the respective ligands were used in PCA analysis. The results of Gibb’s FEL showed that the lowest energy metastable conformations for EGFR kaempferol complex existed in the energy basin with coefficients 1 to 3 on PC1 and − 2 to 0 on PC2 for MD simulation runs 1 and 2 (Fig. 12A). Whereas, in the MD simulation run 3 the lowest energy conformations were observed in the energy basin with the PC1 coefficients 2.5 to 4 and PC2 coefficients − 3 to 0. In the case of the EGFR isorhamnetin complex, the MD simulation run 1 showed three larger and two smaller energy basins in the range − 4 to 1 on PC1 and − 3 to 3 on PC2 (Fig. 12B). Whereas, the MD simulation run 3 showed a single large energy basin occupying the metastable conformations between − 3.8 to -2 on PC1 and − 1 to 1 on PC2. While the MD simulation run 2 showed two small energy basins between the range 0 to 3 on PC1 and − 1 to 2 on PC2. In the case of EGFR morin complex, the MD simulation run 1 showed two small energy basins, one between − 2.4 to -2.5 on PC1 and 1 to 2 on PC2, and the other between 6 to 7 on PC1 and − 2 to 0 on PC2 (Fig. 12C). For the MD simulation run 2 the lowest energy conformations occupied the energy basin between − 2.7 to -2.5 on PC1 and − 3 to -2.5 on PC2. Whereas, the MD simulation run 3 showed a small energy basin between 2.9 to 3.0 on PC1 and − 1 to 0 on PC2. In the case of EGFR erlotinib complex, the MD simulation run 1 showed the major low energy conformations occupying the larger energy basin between − 5 to -4 on PC1 and − 1 to 1 on PC2, and few low energy conformations occupying the small energy basin between 2.1 to 2.2 on PC1 and − 0.5 to 0 on PC2 (Fig. 12D). The MD simulation run 2 showed three small energy basins, one occupying between − 0.8 to -0.9 on PC1 and 3.9 to 4.1 on PC2, and the other two in the range 2 to 4 on PC1 and − 2 to -1 on PC2. The MD simulation run 3 showed a larger energy basin between − 3.8 to -1.6 on PC1 and − 2.5 to -1 on PC2, and the other two small energy basins in the range − 3.8 to 0 on PC1 and 1.5 to 2 on PC2. 3.10.8. Cluster analysis The cluster analysis was performed on each protein-ligand complex in triplicate. The results of cluster analysis for EGFR kaempferol complex showed that in the MD simulation run 1 most of the conformations occurred in clusters 8 and 9 originating from the simulation period 45 ns to 85 ns (Fig. 13A). Whereas, in MD simulation run 2 most of the conformations occurred in cluster 6 which originated between simulation period 40 to 55 ns. While the MD simulation run 3 the major cluster was cluster 5 originating between 38 ns to 55 ns simulation period. In the case of EGFR isorhamnetin complex, the MD simulation run 1 had most of the conformations clustered in the major cluster 10 originating from 79 ns until the end of the simulation period (Fig. 13B). While the MD simulation run 2 showed the major conformations were clustered in cluster 5 originating between 35 ns to 62 ns simulation period. The MD simulation run 3 major conformations were clustered in cluster 9 originating between 77 ns to 95 ns simulation period. In the case of the EGFR morin complex, cluster 9 in all the triplicate runs had the major conformations, whereas in the MD simulation run this cluster originated between 65 ns to 87 ns (Fig. 13C). While, in MD simulation run 2 the cluster originated from 82 ns until the end of the simulation, and the MD simulation run 3 originated from 68 ns to 95 ns. In the case of the EGFR erlotinib complex, the MD simulation run 1 and 3 had the major conformations clustered in cluster 6 originating from the simulation period 38 ns to 57 ns and 30 to 50 ns, respectively (Fig. 13D). While the MD simulation run 2 had the major conformations in cluster 5 originating from 38 ns to 55 ns simulation period. 3.10.9. MM-GBSA calculation The results of MM-GBSA calculations are presented in ( Supplementary Table S3 ) . The EGFR kaempferol complex showed ΔG binding of -34.37, -28.37, -32.95, and − 34.37 kcal/mol for run 1, 2, 3, and concatenated trajectories, respectively. Contributions of various energy parameters in ΔG binding is shown in Fig. 14 . The EGFR isorhamnetin complex showed ΔG binding of -28.34, -32.01, -30.09, and − 30.31 kcal/mol for run 1, 2, 3, and concatenated trajectories, respectively. While the EGFR morin complex showed ΔG binding of -27.48, -31.95, 33.08, and − 30.92 kcal/mol for run 1, 2, 3, and concatenated trajectories, respectively. The EGFR erlotinib complex showed ΔG binding of -37.47, -39.85, -39.17, and − 38.77 kcal/mol for run 1, 2, 3, and concatenated trajectories, respectively. In the case of the EGFR erlotinib complex, the entropic energies (-TΔS) were slightly larger, while the van der Waal’s energies (ΔVDWAALS) were significantly lower. In the case of the EGFR morin complex, the electrostatic energies (ΔEEL) were significantly lower and polar solvation energies (ΔEGB) were significantly larger. 4. Discussion EGFR is a transmembrane tyrosine kinase receptor that plays a crucial role in regulating cellular processes, including proliferation, survival, and differentiation. Mutations in EGFR, particularly exon 19 deletions and the L858R substitution in exon 21, are well-documented oncogenic drivers in NSCLC globally, contributing to constitutive activation of downstream signaling pathways such as PI3K/AKT and RAS/RAF/MEK/ERK (111). The robust characterization of EGFR in NSCLC biology is supported by extensive preclinical and clinical evidence. Preclinical studies using EGFR-mutant cell lines and animal models have demonstrated that inhibiting EGFR significantly reduces tumor growth (112). Clinically, EGFR-targeted therapies have transformed the treatment landscape. First-generation tyrosine kinase inhibitors (TKIs), such as gefitinib and erlotinib, have shown significant improvements in progression-free survival (PFS) and overall response rates (ORR) in EGFR-mutant NSCLC patients compared to chemotherapy. The IPASS trial reported a median PFS of 9.5 months with gefitinib versus 6.3 months with chemotherapy (113). Third-generation TKIs, such as osimertinib, have further improved outcomes, addressing resistance mechanisms, such as T790M mutation. The AURA3 trial demonstrated a median PFS of 10.1 months with osimertinib in T790M-positive patients (114). Compared to other potential targets, EGFR stands out due to its critical role as an oncogenic driver, its well-characterized biology, and the strong clinical evidence supporting its targeting. These factors affirm its therapeutic relevance and justify its selection for this study. In this study, we selected a crystal structure of mutated EGFR kinase domain (L858R) as suitable target for designing EGFR TKI. We curated total 687 phytoconstituents of the four selected anticancer plants ( Curcuma longa , Camellia sinensis , Ginkgo biloba and Vitis vinifera ) from renowned comprehensive, ethnopharmacological, phytochemically rich as well as diverse IMPPAT database (49). Remarkably, all of the three promising compounds (kaempferol, morin and isorhamnetin) originated from Ginkgo biloba . Our study highlighted this ancient plant's ability to produce multiple effective EGFR TKIs, which positions it as a noteworthy candidate for developing targeted cancer therapies. The selection of kaempferol (CID5280863), morin (CID5281670), and isorhamnetin (CID5281654) was based on a rigorous screening approach that included their anticancer potential as reported in the literature, favorable pharmacological properties, and structural compatibility with the EGFR binding site. These compounds were prioritized due to their promising binding affinity, safety profile, and availability in the selected anticancer plants. Detailed molecular docking analysis revealed that these compounds exhibited favorable docking scores, with binding affinities of -8.5, -8.5, and − 8.7 kcal/mol (Supplementary Table S2 ) respectively, compared to the reference drug erlotinib, which exhibited a docking score of -6.9 kcal/mol. The binding affinity of a ligand to a target protein is significantly influenced by the types and strengths of the non-bond interactions formed. These interactions contribute to the stability and specificity of the protein-ligand complex, which is reflected in the docking scores (115). H-bonds are among the most critical interactions for ligand binding. H-bonds improve binding specificity by forming direct interactions with key amino acid residues in the active site, ensuring that the ligand fits precisely into the binding pocket. These bonds also contribute to the rigidity of the ligand within the site, reducing conformational flexibility and strengthening the protein-ligand complex. This stability is crucial for maintaining a strong and lasting interaction, reflected in higher binding affinity (116). Kaempferol, morin, and isorhamnetin formed a higher number of H-bonds, particularly with critical residues like MET793 and THR790. This correlates directly with their superior binding affinities (-8.5 to -8.7 kcal/mol). The bond distances for these interactions were consistently within the optimal range (2.0–3.0 Å), indicating strong and stable bonds (109). Erlotinib, with fewer H-bonds and longer bond distances, demonstrated a lower binding affinity (-6.9 kcal/mol). This observation highlights the importance of H-bonding in achieving higher binding affinity. Hydrophobic interactions stabilize the protein-ligand complex by excluding water molecules from the binding pocket. This increases entropy and enhances the thermodynamic stability of the complex. These interactions also help orient and anchor the ligand in the hydrophobic regions of the target protein, complementing H-bonds and further reinforcing the overall binding strength (117). The extensive hydrophobic interactions observed for the selected compounds, including π-alkyl interactions with residues like VAL726 and LEU718, complement the H-bonding network and contribute to their higher binding affinities. Erlotinib, with fewer hydrophobic interactions, lacks this stabilizing contribution, reflecting its comparatively weaker binding. While variability in H-bond angles was observed, particularly in isorhamnetin (90.198° to 157.888°), the angles still included optimal ranges close to 180°, which contributed to strong and stable interactions. This variability did not appear to negatively impact the binding affinity, as isorhamnetin demonstrated the highest binding affinity (-8.7 kcal/mol), highlighting the overall robustness of its interaction profile. The superior interaction profiles of our selected compounds compared to erlotinib (control) are evident in their higher number of H-bonds, optimal bond distances and angles, and extensive hydrophobic interactions. These factors collectively contribute to their higher binding affinities, demonstrating that the nature and quality of non-bond interactions are directly correlated with binding affinity. The results align with the significance of these interaction types in stabilizing and enhancing protein-ligand binding. The drug-likeness and bioactivity assessments of the selected natural compounds indicate that they satisfy the key criteria for drug-like molecules, including adherence to Lipinski’s Rule of Five (RO5). This ensures favorable bioavailability and oral activity. The ADME and toxicity profiles of our selected compounds underscore their potential as safer and more effective EGFR inhibitors compared to erlotinib. The compounds demonstrated moderate gastrointestinal absorption rates (74.29–76.014%), sufficient for oral administration and comparable to clinically acceptable levels. VDss (volume of distribution at steady state) is a pharmacokinetic parameter that reflects the extent of drug distribution into tissues relative to the plasma concentration at steady state (118). The VDss values indicate that kaempferol, morin, and isorhamnetin exhibit higher tissue distribution than erlotinib, enabling effective systemic delivery without crossing the blood-brain barrier. The natural compounds do not inhibit or act as substrates for major drug-metabolizing enzymes (CYP3A4, CYP2D6). The clearance rates of the selected compounds ensure balanced elimination, minimizing risks of both rapid clearance and prolonged accumulation. Additionally, the absence of mutagenicity, hepatotoxicity, and hERG inhibition highlights their superior safety profiles compared to erlotinib, which exhibits significant toxicities. From a structural perspective, the inhibitory activity of kaempferol, morin, and isorhamnetin is attributed to the presence of multiple hydroxyl groups, which facilitate hydrogen bonding with amino acid residues in the ATP-binding site of EGFR. The addition of a methoxy group at the 4'-position in isorhamnetin enhances its lipophilicity and membrane permeability, likely contributing to its superior bioavailability and efficacy as an EGFR tyrosine kinase inhibitor (TKI). These structural attributes, combined with their planar geometry and favorable hydroxylation patterns, support their potential as effective EGFR TKIs with enhanced binding affinity and specificity. Pharmacophore modeling results further emphasize the optimal binding geometry and conformational stability of the selected compounds compared to erlotinib. While erlotinib's bulky and rigid quinazoline core limits its specificity and selectivity as an EGFR TKI, the natural compounds demonstrate enhanced flexibility and better adaptability within the binding site. Additionally, their bioactivity scores indicate strong potential for EGFR tyrosine kinase inhibition (> 0.00 in the kinase inhibitor category) with minimal off-target effects, as reflected in their low scores for GPCR ligand activity, ion channel modulation, and protease inhibition. This specificity may reduce the side effects commonly associated with broader-target drugs like erlotinib. The comprehensive analysis of ADME properties, pharmacophore features, and bioactivity scores suggests that the selected compounds offer significant advantages as EGFR inhibitors, including superior safety, specificity, and binding affinity. The molecular dynamics (MD) simulations provided valuable insights into the stability, conformational dynamics, and binding interactions of EGFR with kaempferol, isorhamnetin and morin. The triplicate MD simulation runs demonstrated that all complexes achieved stable conformations over the simulation period, indicating robust protein-ligand interactions. Notably, the EGFR complexes with kaempferol and isorhamnetin showed lower RMSD values in the EGFR backbone and ligand atoms compared to erlotinib, suggesting higher stability and stronger binding. Analysis of RMSF values highlighted comparable flexibility of binding site residues across all complexes, confirming the ligands’ ability to stabilize the EGFR binding site. However, the loop region (residues 855–875) exhibited higher fluctuations across all complexes, reflecting inherent flexibility in this region that did not affect overall stability. The radius of gyration and SASA analysis further validated the compactness and stability of all protein-ligand complexes, with the EGFR-morin complex showing the most compact structure. The H-bond analysis revealed that kaempferol, isorhamnetin, and morin consistently formed more H-bonds with critical residues (e.g., MET793, THR790, GLN791) than erlotinib, indicating stronger and more stable interactions. Contact frequency analysis corroborated these findings, with morin exhibiting frequent interactions with key residues, reinforcing its potential as a strong EGFR binder. Energy-based evaluations, including MM-GBSA calculations, indicated that erlotinib had a slightly stronger binding affinity due to its hydrophobic interactions and van der Waals contributions. However, kaempferol and isorhamnetin exhibited comparable binding free energies, with kaempferol showing superior electrostatic interactions. The polar nature of morin resulted in higher solvation energies, which could affect its binding affinity. Overall, the MD simulation analysis suggests that kaempferol and isorhamnetin exhibit higher stability and stronger binding interactions with EGFR compared to erlotinib, while morin demonstrates unique binding dynamics that merit further investigation. These findings highlight the potential of the selected natural compounds as viable EGFR inhibitors for NSCLC therapy. This study highlights several advantages of kaempferol, morin, and isorhamnetin as EGFR inhibitors. The natural availability of these flavonoids makes them cost-effective and eco-friendly, especially for resource-limited settings. Their structural attributes, such as hydroxylation patterns, enhance binding affinity and specificity for EGFR. Their moderate absorption rates and favorable tissue distribution (higher VDss scores) ensure effective systemic delivery while avoiding CNS-related side effects. Unlike erlotinib, these compounds avoid CYP3A4 and CYP2D6 inhibition, reducing metabolism-related drug-drug interaction risks and enhancing their therapeutic suitability. The selected compounds lack mutagenicity, hepatotoxicity, and hERG inhibition, suggesting minimal risk of side effects. Despite the promising findings, this study has several limitations that should be acknowledged. The study primarily relies on computational predictions, including molecular docking and ADME-Tox analysis, which do not account for the complexity of biological systems such as metabolism, enzymatic interactions, and immune responses. Experimental validation through in vitro and in vivo studies is required to confirm binding affinities, pharmacokinetics, and anticancer efficacy. All the selected compounds are P-gp substrates, potentially limiting their oral bioavailability due to efflux by P-glycoprotein. The study focuses exclusively on EGFR inhibition without evaluating activity against other cancer-related pathways, such as PI3K/AKT or VEGF. Investigating these pathways could provide a broader understanding of the therapeutic potential and safety profile of the compounds. Although erlotinib was used as a reference drug, head-to-head experimental comparisons with the selected compounds were not performed. This limits the ability to directly evaluate their clinical superiority over erlotinib. Factors essential for clinical development, such as the chemical stability, scalability for production and formulation challenges of the selected natural compounds, were not addressed. These aspects need to be explored in future studies to facilitate their potential use in clinical settings. The future of this research lies in the comprehensive experimental validation and further optimization of isorhamnetin, kaempferol, and morin as EGFR inhibitors. First, in vitro assays (e.g., kinase activity and cytotoxicity studies) and in vivo studies in animal models are essential to confirm their binding affinities, anticancer efficacy, pharmacokinetics, and safety profiles. These validations will establish their translational potential for clinical applications. Second, advanced drug delivery strategies, such as nanoparticle-based systems, liposomes, and micelles, can address the P-gp substrate activity of these compounds, improving their bioavailability and therapeutic outcomes (119–121). The development of prodrugs may also enhance their metabolic stability and tissue specificity, optimizing their pharmacokinetic properties (122). Third, their potential in combination therapies warrants investigation. Synergistic effects with existing EGFR inhibitors, such as erlotinib, or other anticancer agents could enhance efficacy, reduce resistance, and minimize side effects (123). Future studies should focus on preclinical evaluation of such combinations to identify optimal therapeutic regimens. Fourth, while this study focuses on EGFR inhibition, future research should explore the activity of these compounds against other cancer-related pathways, including PI3K/AKT/mTOR signaling, VEGF-mediated angiogenesis, and MAPK/ERK pathways. This broader target exploration can uncover additional therapeutic applications and improve their versatility as anticancer agents. Fifth, as naturally derived compounds, isorhamnetin, kaempferol, and morin offer a sustainable and cost-effective approach to cancer therapy. Future research should explore scalable and eco-friendly extraction and production methods to ensure their accessibility, particularly in resource-limited settings. Finally, these compounds hold potential for integration into personalized medicine. Patients with EGFR mutations or overexpression could benefit from targeted therapies based on these flavonoids. Advances in biomarker-driven cancer therapies and pharmacogenomics can further tailor their use to specific patient populations, ensuring maximum therapeutic benefit. By addressing these future directions, this research could significantly contribute to the development of safe, effective, and accessible flavonoid-based therapies for EGFR-dependent cancers and beyond. 5. Conclusion EGFR-mutated NSCLC is a significant and evolving disease, heavily dependent on EGFR signaling. The emergence of resistance to EGFR inhibitors presents a challenge, highlighting the need for novel targeted therapies. Overall, our study presents a robust strategy for the discovery of natural EGFR inhibitors as safer and more sustainable alternatives to synthetic TKIs. The pharmacological properties and interaction dynamics assessed through our comprehensive computational workflow lay a solid foundation for future in vitro and in vivo studies. These studies will be essential to validate the therapeutic potential of the identified compounds against mutant EGFR in NSCLC. Declarations 7. Acknowledgement We would like to extend a special acknowledgment to Mahmudul Islam (Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore 7408, Bangladesh) for his invaluable contributions in collecting data from the SwissADME and ProTox-II web servers and for expertly preparing the tables related to ADME and toxicity analysis. Your support has been instrumental to the success of this research. This research is dedicated to the extraordinary undergraduate and graduate students of Bangladesh. Their steadfast dedication, integrity, and unwavering pursuit of scientific excellence serve as a continuous source of inspiration. Despite facing significant challenges—including limited access to laboratories, a lack of research funding, and minimal institutional support—these remarkable individuals find ways to carve out time from their rigorous academic schedules to conduct and publish impactful research. Their tireless efforts not only enhance the prestige of their departments and universities but also stand as a testament to their commitment to advancing knowledge. We express our heartfelt gratitude to all those who champion and nurture the spirit of quality research, whether in computational or laboratory settings. Your encouragement fuels our ambition to persevere and excel. Instead of seeking discouragement, we embrace motivation to grow and thrive, no matter our starting point. Our journey is defined by resilience and passion, and we are dedicated to making meaningful contributions to the scientific community. 8. Data availability statement Data included in the article/supplementary material is referenced in the article. Author Contribution C.D.D. performed conceptualization, formal analysis, data curation, methodology, manuscript writing, and reviewing. S.H., M.M.K.C., M.S.F.R., and T.K. contributed to methodology, visualization, manuscript writing, and reviewing. A.M.E. contributed to methodology, manuscript writing, and reviewing. A.T.M. was involved in methodology, visualization, manuscript writing, reviewing, project administration, and supervision. S.N.V. and R.B.P. contributed to methodology, visualization, manuscript writing, and reviewing, with R.B.P. also contributing to supervision. B.K.S. was involved in methodology, visualization, manuscript writing, reviewing, project administration, and supervision. All authors reviewed the manuscript References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. 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The dataset illustrates the compounds' name, source, PubChem CID, canonical SMILES, binding affinity, molecular weight, number of H-bond donors, number of H-bond acceptors, lipophilicity, water solubility, GI absorption, BBB permeability, Lipinski rule violation, hepatotoxicity, carcinogenicity, immunotoxicity, mutagenicity and cytotoxicity. SupplementaryTableS3.docx Supplementary Table S3: MM-GBSA results SupplementaryFigureS1.docx Supplementary Figure S1. Stepwise flow diagram of the methods followed in the study SupplementaryFigureS2.docx Supplementary Figure S2. (A) Ramachandran plot and (B) Z-score plot for EGFR protein SupplementaryFigureS3.docx Supplementary Figure S3. (A) The surface representation of the ATP-binding pocket of the EGFR protein (B) The cartoon representation of the EGFR protein, highlighting the key structural features SupplementaryFigureS4.docx Supplementary Figure S4. Superimposed structures of cocrystal ligand (Cyan), and its re-docked conformer (Magenta) with an RMSD of 0.00 Å. Cite Share Download PDF Status: Published Journal Publication published 30 Jul, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 21 May, 2025 Reviews received at journal 20 May, 2025 Reviews received at journal 12 May, 2025 Reviewers agreed at journal 02 May, 2025 Reviewers agreed at journal 02 May, 2025 Reviewers invited by journal 02 May, 2025 Editor assigned by journal 02 May, 2025 Editor invited by journal 23 Apr, 2025 Submission checks completed at journal 22 Apr, 2025 First submitted to journal 10 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6422271","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":452079175,"identity":"44d53f1b-b8c5-4ab0-bb03-146d1116ebc9","order_by":0,"name":"Chaity Debnath Dipa","email":"","orcid":"","institution":"Comilla University","correspondingAuthor":false,"prefix":"","firstName":"Chaity","middleName":"Debnath","lastName":"Dipa","suffix":""},{"id":452079176,"identity":"eee770f8-103a-45fa-9e21-9b2856ab4276","order_by":1,"name":"Sharika Hossain","email":"","orcid":"","institution":"Jahangirnagar University","correspondingAuthor":false,"prefix":"","firstName":"Sharika","middleName":"","lastName":"Hossain","suffix":""},{"id":452079177,"identity":"d5984272-888f-40b6-bf8a-2a5239d93b6f","order_by":2,"name":"Md. Moinul Karim Chy","email":"","orcid":"","institution":"University of Science and Technology Chittagong","correspondingAuthor":false,"prefix":"","firstName":"Md.","middleName":"Moinul Karim","lastName":"Chy","suffix":""},{"id":452079178,"identity":"63982de4-f7a6-4e8f-af11-f1fca780677d","order_by":3,"name":"Mohammad Sheikh Farider Rahman","email":"","orcid":"","institution":"Anhalt University of Applied Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mohammad","middleName":"Sheikh Farider","lastName":"Rahman","suffix":""},{"id":452079179,"identity":"99e6b18c-ff15-4342-9a2e-4577a4c20b3c","order_by":4,"name":"Tanvir Kayes","email":"","orcid":"","institution":"East West University","correspondingAuthor":false,"prefix":"","firstName":"Tanvir","middleName":"","lastName":"Kayes","suffix":""},{"id":452079180,"identity":"4e6af0b5-5aa7-4c2e-b45e-80fb6b9838c4","order_by":5,"name":"Afia Maimuna Easha","email":"","orcid":"","institution":"Viqarunnisa Noon School and College","correspondingAuthor":false,"prefix":"","firstName":"Afia","middleName":"Maimuna","lastName":"Easha","suffix":""},{"id":452079181,"identity":"39e5823c-c9a8-4d85-a86d-e06272fb6374","order_by":6,"name":"Abu Tayab Moin","email":"","orcid":"","institution":"University of Chittagong","correspondingAuthor":false,"prefix":"","firstName":"Abu","middleName":"Tayab","lastName":"Moin","suffix":""},{"id":452079182,"identity":"1974ae97-0231-4c3d-b95c-e441383f1dc0","order_by":7,"name":"Suvarna N. 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A, C.E, G represent 3D structure of erlotinib (control), kaempferol, morin, isorhamnetin respectively. B, D, F, H represent 2D structure of erlotinib, kaempferol, morin, isorhamnetin respectively.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-6422271/v1/260f2201647e927e49387c1c.png"},{"id":82154852,"identity":"59ff9d3a-70e5-42f5-acb0-c9f521610fac","added_by":"auto","created_at":"2025-05-07 07:36:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2808264,"visible":true,"origin":"","legend":"\u003cp\u003eThe RMSD in EGFR backbone atoms for the complexes of EGFR with A) Kaempferol, B) Isorhamnetin, C) Morin, and D) Erlotinib (Each panel shows the RMSD against time plot for triplicate runs along with the average of triplicate runs).\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-6422271/v1/f25a6786c967b79dc781a4b1.png"},{"id":82153885,"identity":"1bcffc60-aa17-4aea-a01a-3a10d33adab2","added_by":"auto","created_at":"2025-05-07 07:28:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2986212,"visible":true,"origin":"","legend":"\u003cp\u003eThe RMSD in ligand atoms relative to EGFR backbone atoms for the complexes of EGFR with A) Kaempferol, B) Isorhamnetin, C) Morin, and D) Erlotinib (Each panel shows the RMSD against time plot for triplicate runs along with the average of triplicate runs)\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-6422271/v1/24ad7153b6b803a0ceb0475f.png"},{"id":82156148,"identity":"26a27db9-4ae4-4059-9dd3-d1a5d0700c4b","added_by":"auto","created_at":"2025-05-07 07:44:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3648337,"visible":true,"origin":"","legend":"\u003cp\u003eThe RMSF in residue atoms. The EGFR complexes with A) Kaempferol, B) Isorhamnetin, C) Morin, and D) Erlotinib (Each panel shows the RMSF against residue plot for triplicate runs along with the average of triplicate runs).\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-6422271/v1/0198bef91639f0c747471034.png"},{"id":82154849,"identity":"460b94ca-0928-404f-8e4a-cd5285fdfd25","added_by":"auto","created_at":"2025-05-07 07:36:36","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3121723,"visible":true,"origin":"","legend":"\u003cp\u003eTotal Rg plotted against simulation time. EGFR complexes with A) Kaempferol, B) Isorhamnetin, C) Morin, and D) Erlotinib (Each panel shows the Rg against time plot for triplicate runs along with the average of triplicate runs).\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-6422271/v1/cdaeed5f377ba53487b8fca2.png"},{"id":82153896,"identity":"c288798e-bfb6-4391-8583-465d944bcd47","added_by":"auto","created_at":"2025-05-07 07:28:36","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":3012553,"visible":true,"origin":"","legend":"\u003cp\u003eSolvent accessible surface area analysis. EGFR complexes with A) Kaempferol, B) Isorhamnetin, C) Morin, and D) Erlotinib (Each panel shows the area against the time plot for triplicate runs along with the average of triplicate runs).\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-6422271/v1/4247b85592e9627bf729f57c.png"},{"id":82156150,"identity":"1a17d742-141b-4925-956d-e581c5bd13fe","added_by":"auto","created_at":"2025-05-07 07:44:36","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1815787,"visible":true,"origin":"","legend":"\u003cp\u003eH-bond analysis plots. EGFR complexes with A) Kaempferol, B) Isorhamnetin, C) Morin, and D) Erlotinib (Each panel shows the number of H-bonds formed against the time plot for triplicate runs along with the average of triplicate runs).\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-6422271/v1/7205a281719848c6a75a05e8.png"},{"id":82156157,"identity":"11b013ea-cd48-4096-bc83-b9e39e6b7e58","added_by":"auto","created_at":"2025-05-07 07:44:37","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":35854010,"visible":true,"origin":"","legend":"\u003cp\u003eH-bond interactions at different time intervals. EGFR complexes with A) Kaempferol, B) Isorhamnetin, C) Morin, and D) Erlotinib.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-6422271/v1/9c6f892a0f3ac9ac3fb6d136.png"},{"id":82153905,"identity":"d5d5d729-48bb-4e43-bfe6-0a713d8b430c","added_by":"auto","created_at":"2025-05-07 07:28:36","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":11028464,"visible":true,"origin":"","legend":"\u003cp\u003eContact frequency analysis. EGFR complexes with A) Kaempferol, B) Isorhamnetin, C) Morin, and D) Erlotinib\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-6422271/v1/8b2274dd2d254c8ddba1c53c.png"},{"id":82153912,"identity":"b6808d79-0c84-4fd9-bad0-c5f005c55f04","added_by":"auto","created_at":"2025-05-07 07:28:36","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":6366123,"visible":true,"origin":"","legend":"\u003cp\u003eGibb’s FEL for EGFR complexes with A) Kaempferol, B) Isorhamnetin, C) Morin, and D) Erlotinib (for triplicate MD simulation runs).\u003c/p\u003e","description":"","filename":"Figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-6422271/v1/b1c0d59ee0d5adf777413781.png"},{"id":82153909,"identity":"2cc0b9b9-cda9-4b68-8881-9e35aaeebc75","added_by":"auto","created_at":"2025-05-07 07:28:36","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":5208497,"visible":true,"origin":"","legend":"\u003cp\u003eThe cluster analysis for EGFR complexes with A) Kaempferol, B) Isorhamnetin, C) Morin, and D) Erlotinib (for triplicate MD simulation runs. Besides each cluster plot the centroid of the respective protein-ligand complex is shown).\u003c/p\u003e","description":"","filename":"Figure13.png","url":"https://assets-eu.researchsquare.com/files/rs-6422271/v1/7048aee3d4af82e277477b1d.png"},{"id":82156154,"identity":"9ea698d9-8adf-4490-92b9-8e3be59a4b58","added_by":"auto","created_at":"2025-05-07 07:44:36","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":1683050,"visible":true,"origin":"","legend":"\u003cp\u003eMM-GBSA calculations depicting energy contributions of each energy parameter.\u003c/p\u003e","description":"","filename":"Figure14.png","url":"https://assets-eu.researchsquare.com/files/rs-6422271/v1/f6131bfa0d51cf3b9525e0e2.png"},{"id":88269212,"identity":"1e2431bf-9e48-4597-b139-12054421d80b","added_by":"auto","created_at":"2025-08-04 16:53:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":74616499,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6422271/v1/ba6bef35-e94d-4cc0-b021-1632d23268e3.pdf"},{"id":82153879,"identity":"976f57b8-8a50-45d9-99e1-d13460198d3d","added_by":"auto","created_at":"2025-05-07 07:28:36","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":53694,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table S1: \u003c/strong\u003eList of amino acid residues from A chain of the EGFR protein\u003c/p\u003e","description":"","filename":"SupplementaryTableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6422271/v1/4020bd82f49354226bb0e569.xlsx"},{"id":82154850,"identity":"694bf3ca-b8bb-4550-b94d-be441223fd6d","added_by":"auto","created_at":"2025-05-07 07:36:36","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":75962,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table S2: \u003c/strong\u003ePhysiochemical properties, drug-likeness and toxicity prediction of the compounds having binding affinity (-8.5 to -10.3 kcal/mol).\u003c/p\u003e\n\u003cp\u003eThese compounds were chosen for their relatively high binding affinities. The dataset illustrates the compounds' name, source, PubChem CID, canonical SMILES, binding affinity, molecular weight, number of H-bond donors, number of H-bond acceptors, lipophilicity, water solubility, GI absorption, BBB permeability, Lipinski rule violation, hepatotoxicity, carcinogenicity, immunotoxicity, mutagenicity and cytotoxicity.\u003c/p\u003e","description":"","filename":"SupplementaryTableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6422271/v1/1e2e34da53820ce9738ccc9e.xlsx"},{"id":82156147,"identity":"623095bf-00f0-4df9-9a10-4504e6c61de1","added_by":"auto","created_at":"2025-05-07 07:44:36","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":10871,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table S3: \u003c/strong\u003eMM-GBSA results\u003c/p\u003e","description":"","filename":"SupplementaryTableS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6422271/v1/d6158199615421ce547d03f3.docx"},{"id":82154861,"identity":"ee0e1d27-8beb-4b3f-bee4-bd73d418ee59","added_by":"auto","created_at":"2025-05-07 07:36:36","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":704060,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S1. \u003c/strong\u003eStepwise flow diagram of the methods followed in the study\u003c/p\u003e","description":"","filename":"SupplementaryFigureS1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6422271/v1/8be4eab3becd3be26ca52d3a.docx"},{"id":82154855,"identity":"df2d87a1-becd-4230-abd1-b959477e3850","added_by":"auto","created_at":"2025-05-07 07:36:36","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":363245,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S2. \u003c/strong\u003e(A) Ramachandran plot and (B) Z-score plot for EGFR protein\u003c/p\u003e","description":"","filename":"SupplementaryFigureS2.docx","url":"https://assets-eu.researchsquare.com/files/rs-6422271/v1/33700edda8e596a508293eaf.docx"},{"id":82153893,"identity":"adaf69a6-a689-45f7-b582-be32f260b9b2","added_by":"auto","created_at":"2025-05-07 07:28:36","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":757293,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S3. \u003c/strong\u003e(A) The surface representation of the ATP-binding pocket of the EGFR protein (B) The cartoon representation of the EGFR protein, highlighting the key structural features\u003c/p\u003e","description":"","filename":"SupplementaryFigureS3.docx","url":"https://assets-eu.researchsquare.com/files/rs-6422271/v1/a9deabae19a26f53a789faee.docx"},{"id":82154858,"identity":"e706b7c5-7738-4541-8cad-4a7ddbbeee65","added_by":"auto","created_at":"2025-05-07 07:36:36","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":193430,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S4.\u003c/strong\u003e Superimposed structures of cocrystal ligand (Cyan), and its re-docked conformer (Magenta) with an RMSD of 0.00 Å.\u003c/p\u003e","description":"","filename":"SupplementaryFigureS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-6422271/v1/8133950217ce7bbbba27df22.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003e\u003cem\u003eIn silico\u003c/em\u003e exploration of anticancer plant phytochemicals for EGFR-targeted lung cancer therapy\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLung cancer is a pervasive and highly aggressive global disease, with an estimated 1.8\u0026nbsp;million deaths and 2.2\u0026nbsp;million new cases anticipated in 2020. It is the leading cause of cancer-related mortality in men and the second most common cause among women, following breast cancer (1). Non-small-cell lung cancer (NSCLC) constitutes over 85% of all lung cancer cases (2). One of the primary drivers of NSCLC is mutations in the epidermal growth factor receptor (EGFR) gene, particularly in the tyrosine kinase domain (3). These mutations, which occur in about 32% of NSCLC cases globally, vary by geography and patient demographics. EGFR mutations are notably more prevalent in East Asia (38\u0026ndash;50%) compared to the Americas (24%) and Europe (14%), and more common in women, non-smokers, and those with adenocarcinomas (4\u0026ndash;6). The most frequently observed EGFR mutations\u0026mdash;exon 19 deletions (E19 dels) and the L858R substitution in exon 21\u0026mdash;account for nearly 90% of EGFR mutations (5). Given the critical role of EGFR in NSCLC development and progression, targeting this protein has become a key strategy for improving patient outcomes (7).\u003c/p\u003e \u003cp\u003eThe advent of EGFR tyrosine kinase inhibitors (TKIs) has revolutionized the treatment of NSCLC, significantly improving progression-free survival, response rates, and quality of life for patients with EGFR mutations (8). However, despite the initial success of synthetic TKIs such as osimertinib, gefitinib, erlotinib, and afatinib, their long-term efficacy is often limited due to the development of resistance mechanisms, including secondary EGFR mutations (9\u0026ndash;13). This highlights the need for alternative therapeutic options, such as natural products, which offer several advantages. Natural compounds provide a chemically diverse space that may yield novel EGFR inhibitors with unique modes of action, potentially overcoming resistance to synthetic TKIs (14). Additionally, natural products tend to have favorable safety profiles and lower toxicity, which can improve patient compliance and tolerance (15). Their eco-friendly and sustainable characteristics also align with modern pharmaceutical development trends (16).\u003c/p\u003e \u003cp\u003eAlthough previous research has primarily focused on synthetic EGFR inhibitors, there is growing interest in exploring the potential of natural compounds as EGFR-targeted therapies. Recent studies have utilized computational methods such as virtual screening, pharmacokinetic predictions, molecular docking, and molecular dynamics (MD) simulations to identify promising inhibitors for various cancers (17\u0026ndash;23). However, much of this work has either concentrated on synthetic inhibitors or failed to perform an extensive analysis of the pharmacological properties and interaction dynamics of natural compounds.\u003c/p\u003e \u003cp\u003eOur study addresses this gap by employing a comprehensive \u003cem\u003ein silico\u003c/em\u003e approach to identify potential EGFR inhibitors from natural sources. Specifically, we conducted blind molecular docking, rigorous validation protocols, pharmacophore modeling to assess ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties, bioactivity score analysis, and extended MD simulations. This multi-step screening process allowed us to pinpoint novel, biocompatible EGFR inhibitors while simultaneously evaluating their pharmacological properties and interaction dynamics, setting our study apart from others in the field. By utilizing a 100-nanosecond MD simulation with GROMACS, we were able to analyze the stability and interactions of protein-ligand complexes in detail, offering deeper insights into the compounds' inhibitory potential. Furthermore, pharmacophore modeling validated our ADMET evaluations, ensuring that only the most promising candidates would progress to further testing.\u003c/p\u003e \u003cp\u003eOverall, our study presents a robust strategy for the discovery of natural EGFR inhibitors that could serve as safer, more sustainable alternatives to synthetic TKIs.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003eThe study utilizes virtual screening and advanced dynamics simulation methods to investigate anticancer plant phytochemicals for EGFR-targeted lung cancer therapy (24\u0026ndash;33). The overall systematic procedure of the study is illustrated in a flowchart as shown in \u003cb\u003eSupplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Preparation of Protein\u003c/h2\u003e \u003cp\u003eThe crystal structure of the mutated EGFR kinase domain (L858R) was obtained from the Protein Data Bank (PDB) using entry code 2EB3, which has a resolution of 2.84 \u0026Aring; with R-values of 0.236 (Free), 0.190 (Work), and 0.190 (Observed). This structure was chosen based on its origin from \u003cem\u003eHomo sapiens\u003c/em\u003e and its inclusion in the repository of experimentally determined protein and nucleic acid structures (34,35). Post-retrieval, the protein underwent optimization using Discovery Studio 2020 (v 20.1) (36). This optimization process involved removing heteroatoms such as the co-crystallized ligand AMPPNP (Phosphoaminophosphonic Acid-Adenylate Ester), co-factors, water molecules, and metal ions. Molecular docking investigations focused on protein groups, chain A, and active sites. The protein structure was stabilized through energy minimization, achieved using the GROMOS96 force-field in the SWISS PDB Viewer (v 4.1.0) (37). Subsequently, each compound in our dataset was bound in close proximity to the active site of the investigated protein.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Validation of structure of protein\u003c/h2\u003e \u003cp\u003eThe Ramachandran plot was employed to verify the structure of protein. This plot was generated through the utilization of PROCHECK server in conjunction with the PDBsum database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/thornton-srv/databases/pdbsum/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/thornton-srv/databases/pdbsum/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e (accessed on 27th October, 2023) (38). The Ramachandran plot visually represents highly preferred, allowed, and disallowed phi (ϕ) and psi (ψ) angles of each amino acid in the protein (39). In addition, the structure was also examined utilizing the Protein Structure Analysis (ProSA) web tool (accessed on 27th October, 2023), which calculates the Z-score for a given input protein and determines the overall model quality of the protein (40). If the Z-score of a protein model is not in the range observed in native proteins, it is possible that errors may be present in the protein structure (41).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Preparation of Ligands\u003c/h2\u003e \u003cp\u003eFour plants (\u003cem\u003eCurcuma longa, Camellia sinensis, Ginkgo biloba\u003c/em\u003e, and \u003cem\u003eVitis vinifera\u003c/em\u003e) were selected based on a rigorous literature review highlighting their compounds' significant anticancer properties. The selection of these plants for anticancer study in the modern world is supported by their combination of traditional use, safety profile, diverse chemical composition, and scientific evidence (42\u0026ndash;45). Diverse mechanisms of action exhibited by these plants can potentially inhibit EGFR activity (46,47). A total of 687 compounds from these four plants were curated from the IMPPAT database (accessed on 16th November, 2023) for our study. IMPPAT is a comprehensive, ethnopharmacological, phytochemically rich, and diverse database of medicinal plants in India, providing an integrated platform for implementing cheminformatic approaches to expedite drug discovery from natural products (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cb.imsc.res.in/imppat/\u003c/span\u003e\u003cspan address=\"https://cb.imsc.res.in/imppat/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e (48). For each plant, a ligand library was meticulously generated in SDF format utilizing the PubChem database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (accessed on 16th November, 2023), which offers detailed chemical compound information, including structure, molecular formula, molecular weight, and more (49). The conformation transformation from 2D to 3D was facilitated using the OpenBabel molecule format converter (50). The ensuing docking investigation targeted erlotinib, a standard drug recognized as an inhibitor of the epidermal growth factor receptor (EGFR). Erlotinib received approval from the U.S. Food and Drug Administration for its efficacy as a first-line treatment in patients with metastatic NSCLC and tumors harboring EGFR exon 19 deletions or exon 21 (L858R) substitution mutations (51,52). The control molecule, erlotinib, was retrieved from the PubChem database by searching for its chemical structure. To further refine the investigation, ligands were transformed into PDB format utilizing PyMOL (v2.0), an open-source system software specifically designed for molecular visualization (53).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Identification of Binding Site\u003c/h2\u003e \u003cp\u003eThe EGFR kinase domain, characterized by a bilobed structure with a critical ATP-binding pocket, is a key target for cancer treatment due to its role in regulating pathways like MAPK/ERK and PI3K/AKT (54\u0026ndash;56). To accurately identify and visualize EGFR binding sites, we employed the CASTp 3.0 web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://sts.bioe.uic.edu/castp/index.html?3igg\u003c/span\u003e\u003cspan address=\"http://sts.bioe.uic.edu/castp/index.html?3igg\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (accessed on 17th November, 2023) for detailed surface topography analysis, identifying active pockets based on surface area and volume. The first pocket, with SA 869.613 \u0026Aring;\u0026sup2; and volume 987.321 \u0026Aring;\u0026sup3;, was selected for further study. The server provided a list of residues from the A chain involved in potential interactions (57). We then used PyMOL to generate a cartoon representation of the EGFR protein, mapping the active site, ATP-binding sites, and other critical features. This systematic approach ensured a precise identification of EGFR binding sites, facilitating subsequent docking studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Virtual Screening and Blind Molecular Docking\u003c/h2\u003e \u003cp\u003eIn recent years, molecular docking, a crucial tool in the \u003cem\u003ein silico\u003c/em\u003e drug discovery, has undergone significant advancements. To ascertain interaction types and binding affinities, molecular docking analysis was employed, and molecular screening was conducted to identify lead compounds with the desired biological function (58). Firstly the ligands were energy minimised by utilising conjugate gradients algorithm and Universal Force Field (UFF) (59). The ligands and protein underwent blind docking using PyRx software (v 0.8), which is based on AutoDock Vina. PyRx is an open-source platform with an intuitive user interface, compatible with major operating systems such as Linux, Windows, and Mac OS (60). A grid box was defined for the protein with the center coordinates (X: 24.2000, Y: -58.031, Z: 7.7696) and dimensions (\u0026Aring;) X: 55.8611, Y: 52.5603, Z: 67.5867) to encompass the binding site. The bioactive conformations of the ligands were then simulated using AutoDock Vina. Torsion angles were calculated to map flexible and unbound rotation of molecules. Subsequently, the outcomes were examined using PyMOL and BIOVIA visualizers in Discovery Studio 2020 (61) and the binding poses of the protein-ligand complexes were observed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Docking protocol validation\u003c/h2\u003e \u003cp\u003eA systematic validation method that included both redocking and alignment techniques was employed to assess the accuracy and consistency of the docking process (62,63). To confirm the validity of docking for the mutated EGFR tyrosine kinase, the co-crystal ligand (AMPPNP) was isolated, and then docking was conducted again using the same blind docking parameters. In PyMOL, the lowest energy docked conformer was aligned with the co-crystal AMPPNP to determine the root-mean-square deviation (RMSD) value. In general, an RMSD value of \u0026le;\u0026thinsp;2 \u0026Aring; or 0.2 nm suggests the reliability of a docking method (64).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. ADME Analysis\u003c/h2\u003e \u003cp\u003eAssessing pharmacokinetic properties (PKs) is crucial in drug development, as they determine key factors contributing to the efficacy of oral medications. These factors include absorption rate and extent from the gastrointestinal tract, transfer efficiency to the site of action, metabolism, and elimination without adverse effects (65\u0026ndash;67). ADME features encompass absorption (water solubility, human GI absorption, p-glycoprotein substrate and inhibitor, skin permeability), distribution (blood-brain barrier permeability, steady state volume of distribution for humans), metabolism (CYP2D6/CYP3A4 inhibitor, CYP2D6/CYP3A4 substrate), and excretion (drug total clearance) of drugs (68). The SwissADME web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.swissadme.ch/\u003c/span\u003e\u003cspan address=\"http://www.swissadme.ch/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (accessed on 21st November, 2023) was utilized, providing a range of predictive models for physicochemical properties, pharmacokinetics, drug compatibility, and medicinal chemistry friendliness, including BOILEDEgg, iLOGP, and Bioavailability Radar (69). Additionally, the pkCSM web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biosig.lab.uq.edu.au/pkcsm/\u003c/span\u003e\u003cspan address=\"https://biosig.lab.uq.edu.au/pkcsm/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (accessed on 21st November, 2023) was employed, which develops predictive models for critical ADMET properties in drug development using graph-based signatures (70). The pharmacokinetic response of specific drug candidates was analyzed by querying the SwissADME and pkCSM databases using the canonical SMILES of the potential compounds obtained from the molecular docking procedure.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Toxicity Test\u003c/h2\u003e \u003cp\u003ePerforming toxicological testing during the drug development process is crucial for identifying any harmful properties of a compound and determining the appropriate dosage for human use. Computational methods for predicting toxicity offer significant advantages in terms of time, effort, and cost savings in the development of safe and effective drugs (71,72). To assess the safety profiles of the selected compounds, a variety of computational tools were employed. The ProTox-II server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://tox-new.charite.de/protox_II/\u003c/span\u003e\u003cspan address=\"https://tox-new.charite.de/protox_II/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (accessed on 21st November, 2023) was utilized for an initial toxicity assessment, providing insights into hepatotoxicity, carcinogenicity, immunotoxicity, mutagenicity, and cytotoxicity of the compounds (73). Additionally, the web server pkCSM was employed to reveal further toxicity-related information, including AMES toxicity, hERG channel inhibitors, and \u003cem\u003eT. pyriformis\u003c/em\u003e toxicity, and to estimate the toxic dose threshold of chemicals in humans (70). The canonical SMILES format data for the ligands were obtained from the PubChem database and used as input structures for the aforementioned servers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9. Pharmacophore Modeling\u003c/h2\u003e \u003cp\u003ePharmacophore modeling complements ADMET analysis by providing a structural understanding of ligand-target interactions and guiding lead optimization efforts in drug discovery (74). It is a computational technique used to identify the essential structural and chemical features (pharmacophores) required for ligands to bind to a target protein or biological target (75). Pharmacophoric features such as H-bond donors, acceptors, aromatic rings, and hydrophobic regions within ligand-receptor complexes can be identified and characterized through pharmacophore modeling. These features represent the essential molecular interactions required for ligand binding and biological activity. Numerical information about the pharmacophoric features (H-bond donors, acceptors) of selected compounds was obtained using swissADME during the prediction and analysis of ADMET properties in our study. The generation of pharmacophore models based on input ligand structures and visualization of the spatial arrangement of pharmacophoric features within the binding site were conducted using the Pharmit web server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pharmit.csb.pitt.edu/\u003c/span\u003e\u003cspan address=\"https://pharmit.csb.pitt.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (accessed on 1st December, 2023). Insights into the optimal binding geometry of ligands within the target binding site are provided by these models (76). The preferred binding modes of ligands within the target binding site can be predicted by pharmacophore models, which is valuable for rationalizing the observed ADMET properties of selected compounds (77).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10. Bioactivity Score Analysis\u003c/h2\u003e \u003cp\u003eFor establishing a compound as an EGFR tyrosine kinase inhibitor, its bioactivity score specifically in the \u0026lsquo;kinase inhibitor' category is crucial. A compound having bioactivity score\u0026thinsp;\u0026gt;\u0026thinsp;0.00 in the kinase inhibitor category, makes it a promising candidate for EGFR tyrosine kinase inhibition (78). The higher positive bioactivity score in this category indicates the greater chance of effective EGFR tyrosine kinase inhibition (79). The bioactivity scores in various categories (GPCR ligands, ion channel modulator, kinase inhibitor, protease inhibitor) and topological polar surface area (TPSA) of our selected compounds, along with erlotinib (control), were determined and analyzed using Molinspiration, a cheminformatics server (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.molinspiration.com/\u003c/span\u003e\u003cspan address=\"https://www.molinspiration.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) (80) (accessed on 2nd November, 2023), where the structures of the compounds were used as input.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11. Protein-Ligand Interactions\u003c/h2\u003e \u003cp\u003eProtein-ligand interactions are critical for processes like signal transduction, immunoreaction, and gene regulation, making them essential for understanding biological regulation and identifying therapeutic targets (81). In this study, Biovia Discovery Studio (36) was used to analyze the binding characteristics of EGFR with ligands such as kaempferol, morin, isorhamnetin, and erlotinib (control). The analysis included various interaction types, such as H-bonds and pi-alkyl interactions. Key parameters assessed were binding affinities, interacting residues, H-bond distances, and bond angles (Angle DHA), with a focus on the H-bond metrics due to their significance in protein-ligand stability and effectiveness (82).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.12. Molecular dynamics simulation\u003c/h2\u003e \u003cp\u003eThe docked complexes of EGFR with morin, kaempferol, isorhamnetin, and the standard drug erlotinib were subjected to MD simulations. These simulations aimed to explore the stabilizing effects of the ligands, investigate their binding modes at the EGFR binding site, and compare their inhibitory potentials to that of the standard drug. The 100 ns MD simulations of in triplicate run were afforded for each complex on the HPC cluster at Bioinformatics Resources and Applications Facility (BRAF), C-DAC, Pune with preinstalled Gromacs 2020.4 (83) package (accessed on 25th November, 2023). Briefly, the MD simulation steps include the preparation of input topologies of ligands and EGFR. The input topologies for ligands were prepared using the Acpype (84) based on the AMBER program (85). The topology for the EGFR protein structure was prepared using the Amber ff99SB protein force field (86). The complexes of EGFR with respective ligands were solvated in a dodecahedron unit cell with TIP3P water molecules (87). The resultant solvated systems were neutralized with the addition of sodium or chloride counter-ions and further subjected to the energy minimization step with steepest descent and conjugate gradient minimization algorithms until the thresholds of Fmax less than 1000 kJ mol\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e nm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e were reached. Later, all the systems were equilibrated at constant volume and temperature of 300 K (NVT) condition and then at constant volume and pressure (NPT) conditions for 1 ns each, where the modified Berendsen thermostat (88) was employed to achieve NVT conditions, while Berendsen barostat (89) was employed to achieve NPT conditions of 1 atm. Each NVT and NPT equilibrations were performed with short 1 ns simulations. The equilibrated systems were subjected to the final production phase MD simulations in triplicate for the duration of 100 ns, using the Berendsen thermostat and Parrinello-Rahman barostat (90). The covalent bonds were restrained with the LINCS algorithm (91) and the long-range electrostatic interaction energies were measured with the Particle Mesh Ewald method (PME) (92) with the cut-off of 12 \u0026Aring;.\u003c/p\u003e \u003cp\u003ePost-production phase MD simulations, the trajectories of each simulation after removing the periodic boundary conditions, along with the concatenated trajectories of triplicate runs, were employed in further analysis. The analysis included assessing the stability of each system in terms of root mean square deviation (RMSD) in the backbone atoms of EGFR from the starting equilibrated structure, as well as the RMSD in ligand atoms relative to the position of the EGFR backbone atoms. The analysis also included the root mean square fluctuation (RMSF), radius of gyration, solvent-accessible surface area (SASA), and H-bond interactions (93) for each MD simulation run. The average of each parameter was also considered for each analysis. Further, the buried solvent-accessible surface area (B-SASA) was calculated to get the insights into the shared SASA by the ligand and the EGFR target protein. In this calculation the total SASA was calculated for the EGFR, the ligands, and the EGFR-ligand complex. The buried solvent accessible surface area was calculated through the Eq.\u0026nbsp;(1).\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:B-SASA\\:\\left({nm}^{2}\\right)=0.5\\:({SASA}_{L}+\\:{SASA}_{EGFR}-\\:{SASA}_{EGFR-L\\:complex}\\)\u003c/span\u003e \u003c/span\u003e \u0026hellip;. (Eq.\u0026nbsp;1)\u003c/p\u003e \u003cp\u003eWhere, B-SASA is buried solvent accessible surface area shared between Ligand and EGFR, SASA\u003csub\u003eL\u003c/sub\u003e is total SASA of ligand, SASA\u003csub\u003eEGFR\u003c/sub\u003e is total SASA of EGFR protein, and SASA\u003csub\u003eEGFR\u0026minus;L complex\u003c/sub\u003e is total SASA of EGFR-ligand complex.\u003c/p\u003e \u003cp\u003eFurther, to obtain detailed insights into non-bonded interactions within a distance of 3.5 \u0026Aring; the contact frequency for the contacts between the ligand atoms and the side chain atoms was analyzed using the program MDCiao (94). The results of contact frequency were compared with the H-bonds between the binding site residues and the respective ligands for each MD simulation run. Principal component analysis (PCA) (95) was performed on each MD simulation trajectory from a triplicate run to investigate the major path of motions in each complex. For this, the covariance matrix was constructed for the protein backbone atoms and respective ligands using the gmx covar program. The eigenvectors, representing the path of motion, and eigenvalues, reflecting the mean square fluctuations, were derived by diagonalizing the covariance matrix. The first two eigenvectors also referred to as principal components (PC1 and PC2) were further used as reaction coordinates to obtain Gibb\u0026rsquo;s free energy landscape (96). Further, the cluster analysis was performed using the TTClust program (97) to identify the most prominent conformations that existed in each of the MD simulation trajectories. Molecular mechanics energies combined with General Born surface area continuum solvation (MM-GBSA) calculations were performed on the trajectories sampled at each 100 ps from 50 to 100 ns simulation period, employing the GMX_MMPBSA program (98). The entropic energies were taken into the account and the ΔG\u003csub\u003ebinding\u003c/sub\u003e kcal/mol was calculated for each trajectory. Further, the MM-GBSA calculations were performed on the average trajectory of each protein-ligand complex under study.\u003c/p\u003e \u003cp\u003eThe protein-ligand structures were rendered in PyMOL (53) and graphs were obtained from XMGRACE (99). Gibb\u0026rsquo;s FEL plots were generated using a Python-based Matplotlib package (100).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Validation of the protein structures\u003c/h2\u003e \u003cp\u003eThe obtained Ramachandran plot analysis represented that the majority of the amino acid residues are found within the most favored regions of the protein used. The protein model is reasonably good. The Z-score of -6.3 suggests that the protein model is likely of good quality, with an energy profile consistent with correctly folded, native proteins \u003cb\u003e(Supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Binding Site Analysis\u003c/h2\u003e \u003cp\u003eThe binding site analysis of EGFR provided several key insights. CASTp 3.0 identified critical residues at A:837 (active site) and A:745 and A:855 (ATP-binding sites), which are essential for the receptor's function. The server also generated a list of amino acid residues from the A chain of the protein (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e), highlighting regions that may interact with potential inhibitors. Surface representations of these binding sites, produced by CASTp 3.0, were further visualized in 3D using PyMOL, confirming their spatial arrangement within the protein (\u003cb\u003eSupplementary Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/b\u003e). This detailed mapping facilitated the accurate selection of a grid box for molecular docking simulations. The plant-derived compounds showed significant binding affinity to these identified sites, indicating their potential to reduce cancer cell proliferation and positioning them as promising candidates for anticancer therapy development.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Molecular Docking Analysis\u003c/h2\u003e \u003cp\u003eThe three-dimensional (3D) structures of all the phytoconstituents collected from the four anticancer plants and the target protein were retrieved from PubChem and PDB databases respectively. Molecular docking results showed that out of 687 phytoconstituents, only 60 phytoconstituents had docking scores ranging from \u0026minus;\u0026thinsp;8.5 to -10.3 kcal/mol, while the commercially marketed drug erlotinib had a docking score of -6.9 kcal/mol against EGFR (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Docking Protocol Validation\u003c/h2\u003e \u003cp\u003eDuring the superimposition analysis of the cocrystal and re-docked native ligand, an initial comparison was made between 31 atoms of each through pairwise scoring. The calculated RMSD value was 1.912 \u0026Aring; (\u003cb\u003eSupplementary Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/b\u003e), which falls within the favorable range (\u0026le;\u0026thinsp;2 \u0026Aring;). This indicates an almost identical alignment between the cocrystal and re-docked structures, with no discernible deviations. It can be inferred that utilizing the same docking methodology is likely to yield precise and reproducible conformers for the screening of the compound library sourced from the curated literature survey.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.5. ADME Analysis\u003c/h2\u003e \u003cp\u003eOrally administered drugs must have a molecular weight of less than 500 Da, a LogP value of less than five, five or fewer H-bond donor sites, and ten or fewer H-bond acceptor sites, in accordance with the Lipinski Rule of Five. The bioavailability of the molecule may be adversely affected by a drug candidate's infringement of one of the aforementioned regulations. Out of 60 compounds which showed docking scores in the range from \u0026minus;\u0026thinsp;8.5 to -10.3 kcal/mol for EGFR, only 8 compounds (typhasterol, fisetin, kaempferol, quercetin, episesamin, 6-deoxyjacareubin, isorhamnetin and morin) adhered to Lipinski Rule of Five. The eight compounds showed high rate of passive gastrointestinal (GI) absorption. Except episesamin, the compounds lacked the ability to cross the blood-brain barrier (BBB). The compounds have favorable solubility properties. The favorable ADME properties of these compounds made them promising candidates for further investigation \u003cb\u003e(Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Toxicity Prediction\u003c/h2\u003e \u003cp\u003eThe ProTox-II server evaluation of eight compounds and erlotinib (control) revealed varying toxicological profiles. Typhasterol exhibited severe mutagenicity and mild carcinogenicity. Fisetin showed severe carcinogenicity. Episesamin demonstrated mild carcinogenicity and severe immunotoxicity. 6-deoxyjacareubin had severe immunotoxicity and mild mutagenicity. Quercetin was associated with mild carcinogenicity and mutagenicity. Morin and isorhamnetin were noted for mild immunotoxicity, while kaempferol showed no toxicity. Erlotinib (control) displayed severe hepatotoxicity, immunotoxicity, mutagenicity, and cytotoxicity.\u003c/p\u003e \u003cp\u003eCarcinogenicity and mutagenicity are significant concerns, as they could potentially lead to cancer or genetic mutations, undermining the therapeutic efficacy of a compound. Thus, avoiding compounds with known carcinogenic or mutagenic properties is essential for ensuring the safety and effectiveness of anticancer drug candidates. Mild immunotoxicity may be acceptable if the benefits outweigh the risks. Based on these findings, kaempferol (CID5280863), morin (CID5281670), and isorhamnetin (CID5281654) emerged as the top candidates due to their favorable toxicological profiles (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eFurther analysis using pkCSM suggested that these three candidates are unlikely to be mutagenic and may not inhibit the hERG channel. Additionally, the compounds showed nontoxicity in \u003cem\u003eT. pyriformis\u003c/em\u003e assays and minimal acute toxicity in rats (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Consequently, kaempferol, morin, and isorhamnetin are considered promising pharmaceutical candidates due to their commendable oral bioavailability and protective properties.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe results of Absorption, Distribution, Metabolism, and Excretion (ADME) and Toxicity parameters of the studied three compounds along with erlotinib using pkCSM web server\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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\u003eCompound Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNormal Range\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eErlotinib (control)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKaempferol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMorin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eIsorhamnetin\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eAbsorption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntestinal absorption (human) (% Absorbed)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e74.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e75.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSkin Permeability (log Kp)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt; -2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-2.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-2.735\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-2.735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-glycoprotein substrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreferably \"No\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-glycoprotein I inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreferably \"No\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-glycoprotein II inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreferably \"No\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDistribution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVDss (human) (log L/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.5 to 1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.123\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eMetabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCYP2D6 substrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreferably \"No\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCYP3A4 substrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreferably \"No\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCYP2D6 inhibitior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreferably \"No\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCYP3A4 inhibitior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreferably \"No\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExcretion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal Clearance (log ml/min/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.0 to 1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.591\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.508\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRenal OCT2 substrate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreferably \"No\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eToxicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAMES toxicity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreferably \"No\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMax. tolerated dose (human) (log mg/kg/day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.537\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.576\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehERG I inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreferably \"No\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehERG II inhibitor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePreferably \"No\"\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOral Rat Acute Toxicity (LD50) (mol/kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.407\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eT.Pyriformis\u003c/em\u003e toxicity (log ug/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClose to 0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.296\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=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Pharmacophore Models Analysis\u003c/h2\u003e \u003cp\u003ePharmacophore models were generated using Pharmit for our three selected compounds kaempferol, morin, and isorhamnetin, as well as the reference compound, erlotinib, to visualize the pharmacophoric features (H-bond donors, acceptors, aromatic rings, and hydrophobic regions) of these compounds and their spatial arrangement within the binding site. The importance of hydrogen donor and acceptor groups is generally emphasized due to their specificity, contribution to binding affinity, role in selectivity, flexibility, and influence on solubility and ADMET properties (101,102) being considered more important. According to the generated pharmacophore models of the compounds, the number of hydrogen donor and acceptor groups resemble the numerical values of the pharmacophoric features predicted by swissADME server. The number of hydrogen donor and acceptor groups, their dimensions and radius of the compounds are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and visualized in \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e. The dimensions (x, y, z coordinates) and radius of the hydrogen donors and acceptors of our selected compounds provide valuable insights into the spatial arrangement and interaction patterns with the target protein. The x, y, z coordinates indicate the spatial location of the pharmacophoric features within the binding site of the target protein (103). The radius of pharmacophoric features provides information about their size and shape. This information is crucial for assessing steric complementarity between our selected compounds and their binding sites (104). The number of hydrogen donor and acceptor groups of our three selected compounds are far more than those of erlotinib (control). The presence of more hydrogen donor and acceptor groups in our selected compounds facilitates the formation of favorable H-bonding interactions with the target protein, leading to enhanced specificity, optimal binding geometry, and improved binding affinity (104,105). Another observable fact is in our three selected compounds, some hydrogen donor and acceptor groups have the same dimension values (x, y, z, and radius), it indicates favorable spatial arrangement of each compound leading to enhanced conformational stability, compounds' efficacy, selectivity and pharmacological properties (106). Additionally, the radius value for the pharmacophoric features of the compounds is '1,' indicating that each feature occupies a relatively small volume in three-dimensional space. It also indicates compact and precise spatial arrangement which promotes optimal ligand-receptor interactions, minimizes steric hindrance, and allows for fine-tuning of molecular interactions critical for drug efficacy, potency and specificity (107). All the dimensions properties obtained from pharmacophore modeling helped us to justify the ADMET properties of our selected compounds as drug candidates.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eH-bond Donors (HD), H-bond Acceptors (HA), Dimensions (X,Y,Z) and Radius of Kaempferol, Morin, Isorhamnetin Along With Erlotinib (Control) Using Pharmit Web Server\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInteractions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eRadius\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eErlotinib\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eKaempferol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"10\" rowspan=\"11\"\u003e \u003cp\u003eMorin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eIsorhamnetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\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=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.8. Inhibitory Properties Prediction\u003c/h2\u003e \u003cp\u003eOur selected compounds exhibited bioactivity scores\u0026thinsp;\u0026gt;\u0026thinsp;0.00 individually in the 'kinase inhibitor' category, indicating their potential for EGFR tyrosine kinase inhibition. In contrast, the bioactivity scores of the three compounds in other categories (GPCR ligands, ion channel modulator, and protease inhibitor) were less than 0.00, suggesting minimal off-target effects and highlighting their specificity as effective EGFR TKIs. These off-target effects, such as cardiotoxicity, dermatologic reactions, gastrointestinal toxicity, hepatotoxicity, and myelosuppression, are more likely to occur with erlotinib (control), as its bioactivity scores in these categories were greater than 0.00 \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e) (Fig.\u0026nbsp;2).\u003c/b\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBioactivity scores for kinase inhibition, GPCR ligand activity, ion channel modulation, and protease inhibition of Kaempferol, Morin, and Isorhamnetin, compared with Erlotinib (control), as determined using the Molinspiration web server.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompound Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKinase inhibitor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGPCR ligands\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIon channel modulator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProtease inhibitor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eErlotinib (control)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKaempferol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsorhamnetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.30\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=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.9. Protein-Ligand Interaction Analysis\u003c/h2\u003e \u003cp\u003eThe analysis of non-bond interactions is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and visualized in \u003cb\u003eFig.\u0026nbsp;3\u003c/b\u003e. The interaction analysis revealed that the three selected compounds (kaempferol, morin, and isorhamnetin) demonstrate a more extensive and varied interaction profile with the EGFR protein compared to erlotinib (control). These compounds not only form H-bonds with critical residues such as MET793 and THR790, which are key to the function of the EGFR protein, but also engage in numerous pi-alkyl interactions that further stabilize the protein-ligand complex. Erlotinib primarily interacts with CYS797 and MET793, forming fewer conventional H-bonds compared to the selected compounds. Conventional H-bonds are crucial contributors to the stability of the protein-ligand complex. A greater number of H-bonds typically leads to a more stable and stronger binding interaction between the ligand and the protein (108). The H-bond distances for our selected compounds were generally within the optimal range (approximately 2.0 to 3.0 \u0026Aring;), indicating strong and stable interactions (109). Specifically, kaempferol exhibited H-bond distances ranging from 1.977 \u0026Aring; to 2.886 \u0026Aring;, morin showed distances from 2.010 \u0026Aring; to 2.445 \u0026Aring;, and isorhamnetin displayed H-bond distances from 1.978 \u0026Aring; to 2.926 \u0026Aring;. These distances are shorter, and therefore suggest stronger interactions, compared to those observed for erlotinib, which ranged from 2.098 \u0026Aring; to 2.993 \u0026Aring;. H-bond angles also play a critical role in the strength and stability of these interactions, with angles closer to 180\u0026deg; typically resulting in stronger and more stable bonds (110). Kaempferol displayed bond angles ranging from 100.125\u0026deg; to 157.454\u0026deg;. Although kaempferol has a lower minimum angle (100.125\u0026deg;), it achieves a higher maximum angle (157.454\u0026deg;), indicating the potential for stronger bonding compared to erlotinib. Morin exhibited bond angles ranging from 99.449\u0026deg; to 157.488\u0026deg;, with its highest angle (157.488\u0026deg;) also indicative of strong H-bonds. Isorhamnetin showed the widest range of bond angles (90.198\u0026deg; to 157.888\u0026deg;), which, while indicating variability, still includes angles that contribute to strong bonding.\u003c/p\u003e \u003cp\u003eBased on the amino acid residues involved, interaction types, bond distances, and bond angles, it is evident that our selected compounds demonstrate superior interaction profiles with EGFR tyrosine kinase compared to the control ligand, erlotinib. The shorter bond distances and more optimal angles observed in kaempferol, morin, and isorhamnetin contribute to stronger and more stable interactions with the protein, suggesting that these compounds could be more effective inhibitors than erlotinib (control).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNon-bond interactions between amino acid residues of EGFR protein and our selected three compounds along with erlotinib.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eName\u003c/p\u003e \u003cp\u003e(PubChem CID)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBinding Affinity\u003c/p\u003e \u003cp\u003ekcal/mol\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidue in Contact\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eInteraction Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBond Distance\u003c/p\u003e \u003cp\u003e(Ǻ)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAngle DHA\u003c/p\u003e \u003cp\u003e(\u0026ordm;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eErlotinib (Control)\u003c/p\u003e \u003cp\u003eCID (176870)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA:CYS797\u003c/p\u003e \u003cp\u003eA:MET793\u003c/p\u003e \u003cp\u003eA:GLY719\u003c/p\u003e \u003cp\u003eA:ASP800\u003c/p\u003e \u003cp\u003eA:LYS745\u003c/p\u003e \u003cp\u003eA:MET766\u003c/p\u003e \u003cp\u003eA:LEU788\u003c/p\u003e \u003cp\u003eA:VAL726\u003c/p\u003e \u003cp\u003eA:LYS745\u003c/p\u003e \u003cp\u003eA:LEU718\u003c/p\u003e \u003cp\u003eA:VAL726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConventional H-bond\u003c/p\u003e \u003cp\u003eConventional H-bond\u003c/p\u003e \u003cp\u003eCarbon H-bond\u003c/p\u003e \u003cp\u003eCarbon H-bond\u003c/p\u003e \u003cp\u003eAlkyl\u003c/p\u003e \u003cp\u003eAlkyl\u003c/p\u003e \u003cp\u003eAlkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.24909\u003c/p\u003e \u003cp\u003e2.09822\u003c/p\u003e \u003cp\u003e2.8479\u003c/p\u003e \u003cp\u003e2.99378\u003c/p\u003e \u003cp\u003e3.9483\u003c/p\u003e \u003cp\u003e5.06506\u003c/p\u003e \u003cp\u003e4.23104\u003c/p\u003e \u003cp\u003e5.20555\u003c/p\u003e \u003cp\u003e5.34892\u003c/p\u003e \u003cp\u003e4.16972\u003c/p\u003e \u003cp\u003e5.03169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e156.778\u003c/p\u003e \u003cp\u003e145.615\u003c/p\u003e \u003cp\u003e136.393\u003c/p\u003e \u003cp\u003e113.076\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKaempferol\u003c/p\u003e \u003cp\u003eCID (5280863)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA:THR790\u003c/p\u003e \u003cp\u003eA:MET793\u003c/p\u003e \u003cp\u003eA:MET766\u003c/p\u003e \u003cp\u003eA:LEU718\u003c/p\u003e \u003cp\u003eA:VAL726\u003c/p\u003e \u003cp\u003eA:ALA743\u003c/p\u003e \u003cp\u003eA:LEU844\u003c/p\u003e \u003cp\u003eA:LEU718\u003c/p\u003e \u003cp\u003eA:VAL726\u003c/p\u003e \u003cp\u003eA:VAL726\u003c/p\u003e \u003cp\u003eA:ALA743\u003c/p\u003e \u003cp\u003eA:LYS745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConventional H-bond\u003c/p\u003e \u003cp\u003eConventional H-bond\u003c/p\u003e \u003cp\u003eConventional H-bond\u003c/p\u003e \u003cp\u003eConventional H-bond\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.46572\u003c/p\u003e \u003cp\u003e1.97773\u003c/p\u003e \u003cp\u003e2.88692\u003c/p\u003e \u003cp\u003e2.77292\u003c/p\u003e \u003cp\u003e4.549\u003c/p\u003e \u003cp\u003e4.08513\u003c/p\u003e \u003cp\u003e5.01673\u003c/p\u003e \u003cp\u003e4.30011\u003c/p\u003e \u003cp\u003e4.77678\u003c/p\u003e \u003cp\u003e5.32702\u003c/p\u003e \u003cp\u003e5.12763\u003c/p\u003e \u003cp\u003e4.71305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e119.28\u003c/p\u003e \u003cp\u003e157.454\u003c/p\u003e \u003cp\u003e122.832\u003c/p\u003e \u003cp\u003e100.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMorin\u003c/p\u003e \u003cp\u003eCID (5281670)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA:MET793\u003c/p\u003e \u003cp\u003eA:GLN791\u003c/p\u003e \u003cp\u003eA:MET793\u003c/p\u003e \u003cp\u003eA:VAL726\u003c/p\u003e \u003cp\u003eA:ALA743\u003c/p\u003e \u003cp\u003eA:LEU844\u003c/p\u003e \u003cp\u003eA:LEU718\u003c/p\u003e \u003cp\u003eA:VAL726\u003c/p\u003e \u003cp\u003eA:VAL726\u003c/p\u003e \u003cp\u003eA:ALA743\u003c/p\u003e \u003cp\u003eA:LYS745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConventional H-bond\u003c/p\u003e \u003cp\u003eConventional H-bond\u003c/p\u003e \u003cp\u003eConventional H-bond\u003c/p\u003e \u003cp\u003eConventional H-bond\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.04975\u003c/p\u003e \u003cp\u003e2.40729\u003c/p\u003e \u003cp\u003e2.0101\u003c/p\u003e \u003cp\u003e2.44531\u003c/p\u003e \u003cp\u003e4.50785\u003c/p\u003e \u003cp\u003e4.05262\u003c/p\u003e \u003cp\u003e5.02681\u003c/p\u003e \u003cp\u003e4.39575\u003c/p\u003e \u003cp\u003e4.68408\u003c/p\u003e \u003cp\u003e5.36186\u003c/p\u003e \u003cp\u003e5.22323\u003c/p\u003e \u003cp\u003e4.55869\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e157.488\u003c/p\u003e \u003cp\u003e148.922\u003c/p\u003e \u003cp\u003e137.459\u003c/p\u003e \u003cp\u003e99.449\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIsorhamnetin\u003c/p\u003e \u003cp\u003eCID (5281654)\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=\"left\" colname=\"c3\"\u003e \u003cp\u003eA:THR790\u003c/p\u003e \u003cp\u003eA:MET793\u003c/p\u003e \u003cp\u003eA:THR854\u003c/p\u003e \u003cp\u003eA:GLU762\u003c/p\u003e \u003cp\u003eA:MET766\u003c/p\u003e \u003cp\u003eA:GLN791\u003c/p\u003e \u003cp\u003eA:MET793\u003c/p\u003e \u003cp\u003eA:GLU762\u003c/p\u003e \u003cp\u003eA:VAL726\u003c/p\u003e \u003cp\u003eA:ALA743\u003c/p\u003e \u003cp\u003eA:LEU844\u003c/p\u003e \u003cp\u003eA:LEU718\u003c/p\u003e \u003cp\u003eA:VAL726\u003c/p\u003e \u003cp\u003eA:VAL726\u003c/p\u003e \u003cp\u003eA:ALA743\u003c/p\u003e \u003cp\u003eA:LYS745\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConventional H-bond\u003c/p\u003e \u003cp\u003eConventional H-bond\u003c/p\u003e \u003cp\u003eConventional H-bond\u003c/p\u003e \u003cp\u003eConventional H-bond\u003c/p\u003e \u003cp\u003eConventional H-bond\u003c/p\u003e \u003cp\u003eConventional H-bond\u003c/p\u003e \u003cp\u003eConventional H-bond\u003c/p\u003e \u003cp\u003eCarbon H-bond\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003cp\u003ePi-Alkyl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.39823\u003c/p\u003e \u003cp\u003e1.97848\u003c/p\u003e \u003cp\u003e2.92654\u003c/p\u003e \u003cp\u003e2.28615\u003c/p\u003e \u003cp\u003e3.07823\u003c/p\u003e \u003cp\u003e2.56702\u003c/p\u003e \u003cp\u003e2.54115\u003c/p\u003e \u003cp\u003e2.77107\u003c/p\u003e \u003cp\u003e4.52382\u003c/p\u003e \u003cp\u003e4.09176\u003c/p\u003e \u003cp\u003e5.04263\u003c/p\u003e \u003cp\u003e4.26006\u003c/p\u003e \u003cp\u003e4.83164\u003c/p\u003e \u003cp\u003e5.22413\u003c/p\u003e \u003cp\u003e5.12197\u003c/p\u003e \u003cp\u003e4.62102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e120.831\u003c/p\u003e \u003cp\u003e157.888\u003c/p\u003e \u003cp\u003e91.852\u003c/p\u003e \u003cp\u003e127.698\u003c/p\u003e \u003cp\u003e115.095\u003c/p\u003e \u003cp\u003e132.659\u003c/p\u003e \u003cp\u003e90.198\u003c/p\u003e \u003cp\u003e127.988\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=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.10. Molecular Dynamics studies\u003c/h2\u003e \u003cdiv id=\"Sec26\" class=\"Section3\"\u003e \u003ch2\u003e3.10.1. Root mean square deviation\u003c/h2\u003e \u003cp\u003eThe RMSD in backbone atoms of each EGFR ligand complex was evaluated for the triplicate runs. The complexes with kaempferol and isorhamnetin showed quite stable RMSD throughout the simulation period (Fig.\u0026nbsp;4A \u003cb\u003eand B\u003c/b\u003e). In both the complexes, the average RMSD for triplicate runs was around 0.3 nm. In the case of EGFR morin complex the MD simulation run 1 showed significant deviation after around 60 ns. The average RMSD for triplicate runs for this complex was around 0.32 nm (Fig.\u0026nbsp;4C). The MD simulation run 1 showed significantly higher RMSD compared to run 3 in the case of the EGFR erlotinib complex (Fig.\u0026nbsp;4D). The average of triplicate runs was around 0.32 nm for this complex.\u003c/p\u003e \u003cp\u003eThe RMSD in ligand atoms relative to the EGFR backbone atoms was evaluated in triplicate for all the complexes. The results showed that the complexes of EGFR with kaempferol and isorhamnetin had reasonably stable RMSD with overall deviations within the range of 0.1 to 0.3 nm (Fig.\u0026nbsp;5A \u003cb\u003eand B\u003c/b\u003e). In MDS run 2 the RMSD in kaempferol showed slightly larger deviations during the simulation period 20 to 80 ns. The average RMSD of triplicate runs for EGFR complexes with kaempferol and isorhamnetin was around 0.2 nm. A significant difference was found in MD simulation run 1 and run 3 in the case of the EGFR morin complex (Fig.\u0026nbsp;5C). While run 2 and run 3 are almost stabilizing after around 40 ns simulation period. The average of triplicate runs was around 0.25 nm. In the case of the EGFR erlotinib complex, the RMSD deviations were in the range of 0.2 to 0.4 with an average of triplicate runs around 0.3 nm (Fig.\u0026nbsp;5D).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003e3.10.2. Root mean square fluctuation evaluation\u003c/h2\u003e \u003cp\u003eThe root mean square fluctuations in the side chain atoms of each residue of EGFR in all the complexes were evaluated for the triplicate runs. The results showed the major fluctuations in the residues in the range 850\u0026ndash;875. In the case of EGFR kaempferol complex the MD simulation run 3 showed slightly higher fluctuations compared to run 1 and run 2 (Fig.\u0026nbsp;6A). Further, the terminal residues beyond residue number 975 showed slightly larger RMSF reaching beyond 0.6 nm. In the case of the EGFR isorhamnetin complex, almost all the MD simulation runs showed similar RMSF. However, MD simulation run 1 particularly showed slightly higher RMSF for a few residues in the range 855\u0026ndash;860 (Fig.\u0026nbsp;6B). The EGFR morin complex also showed almost similar RMSF in all the triplicate MD simulation runs, except for a few residues in the range 855\u0026ndash;860 in run 3 which showed slightly higher RMSF (Fig.\u0026nbsp;6C). Notably the terminal residues beyond 960 showed significantly higher RMSF reaching 0.8 nm, compared to residues in the same range in other complexes. In the case of EGFR erlotinib complex the RMSF in all the triplicate MD simulation runs was almost similar (Fig.\u0026nbsp;6D).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section3\"\u003e \u003ch2\u003e3.10.3. Radius of gyration evaluation\u003c/h2\u003e \u003cp\u003eTo investigate whether the EGFR structure remained in folded and compact form when bound to the respective ligands during the simulation, the analysis of the radius of gyration (Rg) was performed. The results showed that all the complexes under study had the Rg within the range of 2 to 2.15 nm. Particularly in the case of EGFR kaempferol complex the Rg stabilized after 20 ns in all the triplicate runs (Fig.\u0026nbsp;7A). The MD simulation run 2 showed a slightly lower Rg, however the average Rg for the triplicate run was around 2.075 nm. In the case of EGFR isorhamnetin complex the Rg for all the runs slightly deviated until the end of the simulation with an overall average of around 2.075 nm (Fig.\u0026nbsp;7B). Here, the MD simulation run 1 showed the lowest Rg while the run 2 showed the higher Rg. The Rg in the EGFR morin complex stabilized after 25 ns to an average of around 2.05 nm in all the triplicate runs (Fig.\u0026nbsp;7C). In the case of the EGFR erlotinib complex, the Rg stabilized after 20 ns simulation period in all the triplicate runs (Fig.\u0026nbsp;7D). However, the MD simulation run 1 had significantly lower Rg compared to run 2. The average Rg for the triplicate run was around 0.075 nm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section3\"\u003e \u003ch2\u003e3.10.4. Solvent accessible surface area analysis\u003c/h2\u003e \u003cp\u003eThe analysis of solvent-accessible surface area (SASA) provides an impetus for the effects of solvent exposure on protein structure particularly on the deeply buried cavities. The results showed that SASA remained within a range of 160 to 190 nm\u003csup\u003e2\u003c/sup\u003e for all the complexes under study. The average SASA for the EGFR complex with kaempferol was around 175 nm\u003csup\u003e2\u003c/sup\u003e, whereas the SASA was slightly higher for the MD simulation run 3 (Fig.\u0026nbsp;8A). The SASA stabilized after around 20 ns in the case of EGFR isorhamnetin complex where the average SASA for the triplicate run was around 175 nm\u003csup\u003e2\u003c/sup\u003e, where the steady state was observed after an 80 ns simulation period (Fig.\u0026nbsp;8B). In the case of the complex with morin, reasonable stability was seen after around 50 ns simulation period where the average SASA for the triplicate run was around 170 nm\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;8C). In the case of the EGFR erlotinib complex, the SASA stabilized after around 20 ns simulation period with an average triplicate run of 175 nm\u003csup\u003e2\u003c/sup\u003e (Fig.\u0026nbsp;8D).\u003c/p\u003e \u003cp\u003eFurther, the B-SASA calculated for each run and for the concatenated trajectory. The results are given in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. The B-SASA for complex with erlotinib was larger compared to other complexes with the B-SASA for run 1, run 2, and run 3 of 4.483, 4.667, and 4.519 nm\u003csup\u003e2\u003c/sup\u003e, respectively. Comparably, the complex with isorhamnetin showed the B-SASA in the range 3.763 to 3.676 nm\u003csup\u003e2\u003c/sup\u003e, while the complex with kaempferol showed the B-SASA in the range 3.220 to 3.396 nm\u003csup\u003e2\u003c/sup\u003e for triplicate runs. The complex with morin showed the lowest B-SASA among all the complexes which was in the range 3.237 to 3.295 nm\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBuried solvent-accessible surface area analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComplex/Run number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSASA (Protein)\u003c/p\u003e \u003cp\u003e(nm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSASA (Ligand) (nm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSASA (Protein-ligand complex) (nm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBuried SASA (nm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eEGFR Kaempferol Complex\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRun 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172.830 (5.087)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.885 (0.204)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170.923 (5.087)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.396 (0.200)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRun 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170.411 (4.132)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.789 (0.197)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168.438 (4.129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.381 (0.180)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRun 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e178.247 (3.787)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.762 (0.197)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e176.569 (3.794)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.220 (0.165)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEGFR Isorhamnetin Complex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRun 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176.416 (4.412)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.269 (0.198)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e174.333 (4.444)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.676 (0.180)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRun 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172.715 (4.683)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.201 (0.186)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170.389 (4.710)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.763 (0.200)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRun 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176.002 (2.960)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.307 (0.191)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e173.888 (2.960)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.763 (0.200)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEGFR Morin Complex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRun 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171.720 (5.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.843 (0.193)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e170.087 (5.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.237 (0.175)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRun 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e172.876 (4.438)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.832 (0.210)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171.116 (4.463)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.295 (0.175)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRun 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e169.930 (4.847)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.768 (0.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168.140 (4.895)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.288 (0.180)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEGFR Erlotinib Complex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRun 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176.158 (4.881)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.232 (0.322)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e174.422 (4.716)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.483 (0.265)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRun 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e171.041 (4.777)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.229 (0.305)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e168.935 (4.861)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.667 (0.270)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRun 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e176.267 (4.316)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.276 (0.293)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e174.505 (4.463)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.519 (0.240)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe standard deviations are given in parentheses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section3\"\u003e \u003ch2\u003e3.10.5. H-bond analysis\u003c/h2\u003e \u003cp\u003eThe complex of EGFR with kaempferol showed four H-bonds formed consistently throughout the simulation period in MD simulation run 1 and run 3, while in MD simulation run 2 around three consistent H-bonds were formed (Fig.\u0026nbsp;9A). In the case of EGFR isorhamnetin complex four consistent H-bonds were formed in all the triplicate MD simulation runs (Fig.\u0026nbsp;9B). In the case of both the EGFR-kaempferol and EGFR-isorhamnetin complexes, a maximum of five H-bonds were occasionally observed during the simulation. In the case of the EGFR morin complex five consistent H-bonds were formed in all the triplicate MD simulation runs (Fig.\u0026nbsp;9C). Occasionally, in run 1 and run 2, a maximum of six H-bonds were observed. In the case of the EGFR erlotinib complex, two consistent H-bonds were formed in MD simulation run 2 and run 3, while in run 1 one consistent H-bond was formed (Fig.\u0026nbsp;9D).\u003c/p\u003e \u003cp\u003eFurther, to investigate which binding site residues are involved in H-bond formation, the H-bond interactions between ligand and EGFR were captured from the initial equilibrated trajectory, at trajectories extracted at 25, 50, 75, and 100 ns simulation period. For this analysis, the trajectories of triplicate runs were combined to get the average trajectory. The EGFR kaempferol complex showed a consistent H-bond with the residue GLN791 in all the extracted trajectories (Fig.\u0026nbsp;10A). The residue MET793 participated in H-bond formation, except in the 75 ns trajectory. The residues GLU762 and THR790 also formed the H-bond as seen in the equilibrated trajectory and 75 ns trajectory. The EGFR isorhamnetin complex showed a very consistent H-bond with the residue MET793 in all the trajectories (Fig.\u0026nbsp;10B). The residue GLN791 also formed a consistent H-bond in all the trajectories, except at 75 ns. In addition to these H-bonds, the equilibrated trajectory showed a H-bond with GLU762 and THR790. In 25 ns trajectory, the H-bond with Thr290 broke and a new H-bond with residue LYS745 was formed. However, this new H-bond with residue LYS745 again broke and reformed only in a 100 ns trajectory. In the case of the EGFR morin complex, the residues GLU762, GLN791, and MET793 formed a consistent H-bond in all the trajectories (Fig.\u0026nbsp;10C). The residue ASP855 also formed a consistent H-bond in all the trajectories, except the equilibrated trajectory. In addition to these aforementioned consistent H-bonds, the equilibrated trajectory showed a transient H-bond with LEU718, THR790, and THR854. These transient H-bonds reformed at various time intervals where the H-bond was reformed with the residue THR790 in the 50 ns and 100 ns trajectory, while with the residue THR854 in the 75 ns and 100 ns trajectory. The EGFR erlotinib complex showed a consistent H-bond with the residue MET793, except in the 25 ns trajectory (Fig.\u0026nbsp;10D). The equilibrated trajectory also showed a H-bond with residue CYS797. The 25 ns trajectory showed no H-bonds.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section3\"\u003e \u003ch2\u003e3.10.6. Contact frequency analysis\u003c/h2\u003e \u003cp\u003eTo further investigate the residues involved in the formation of non-bonded interactions such as H-bonds, the contact frequency between respective ligands and residues within a 3.5 \u0026Aring; distance was analyzed. In the case of EGFR kaempferol complex, all the triplicate MD simulation runs showed GLN790, THR790, and MET793 having a contact frequency of more than 90%, with almost 100% contact frequency for GLN790 (Fig.\u0026nbsp;11A). In addition to these three residues, the MD simulation run 3 showed that GLU762 had a contact frequency of more than 90%. Further, run 1 showed a contact frequency of around 70% for ALA743 and around 60% for THR854. Run 2 showed a contact frequency of around 74% for ALA743 and around 60% for LEU844. While run 3 showed a contact frequency of around 70% for LYS745. In the case of EGFR isorhamnetin complex, only MD simulation run 1 and run 2 showed MET793 having a contact frequency of more than 90%, while run 3 showed a contact frequency of around 70% for MET793 (Fig.\u0026nbsp;11B). The residues LEU792, GLY796, and ASP855 showed a contact frequency of less than 25% in all triplicate MD simulation runs. The residue GLY719 in MD simulation run 1, residue LEU718 in run 2, and residue LYS745 in run 3 also showed a subtle contact frequency of around 5%. In the case of EGFR morin complex, the residues GLU762, THR790, ASP855, and MET793 showed having more than 90% contact frequency in all the triplicate MD simulation runs (Fig.\u0026nbsp;11C). Particularly, the contact frequency of almost 100% was observed for the residues GLU762 and ASP855 in all the runs. Further, residue GLN791 in run 1 and residue THR854 in run 2 and run 3 also showed more than 90% contact frequency. In the case of the EGFR erlotinib complex, the residue THR790 showed a contact frequency of more than 90% in all the triplicate MD simulation runs (Fig.\u0026nbsp;11D). In the MD simulation run 1 and run 3 for this complex, the residues MET793, LYS745, LEU718, and ALA743 showed a contact frequency between 50 to 75%. In MD simulation run 2 the residues CYS797, ALA743, LYS745, and THR854 showed the contact frequency between 50 to 75%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section3\"\u003e \u003ch2\u003e3.10.7. Principal component analysis and Gibb\u0026rsquo;s free energy analysis\u003c/h2\u003e \u003cp\u003eUsing the first two principal components (PC1 and PC2) Gibb\u0026rsquo;s Free Energy Landscape (FEL) plots were generated. The backbone atoms of EGFR along with the respective ligands were used in PCA analysis. The results of Gibb\u0026rsquo;s FEL showed that the lowest energy metastable conformations for EGFR kaempferol complex existed in the energy basin with coefficients 1 to 3 on PC1 and \u0026minus;\u0026thinsp;2 to 0 on PC2 for MD simulation runs 1 and 2 (Fig.\u0026nbsp;12A). Whereas, in the MD simulation run 3 the lowest energy conformations were observed in the energy basin with the PC1 coefficients 2.5 to 4 and PC2 coefficients \u0026minus;\u0026thinsp;3 to 0. In the case of the EGFR isorhamnetin complex, the MD simulation run 1 showed three larger and two smaller energy basins in the range \u0026minus;\u0026thinsp;4 to 1 on PC1 and \u0026minus;\u0026thinsp;3 to 3 on PC2 (Fig.\u0026nbsp;12B). Whereas, the MD simulation run 3 showed a single large energy basin occupying the metastable conformations between \u0026minus;\u0026thinsp;3.8 to -2 on PC1 and \u0026minus;\u0026thinsp;1 to 1 on PC2. While the MD simulation run 2 showed two small energy basins between the range 0 to 3 on PC1 and \u0026minus;\u0026thinsp;1 to 2 on PC2. In the case of EGFR morin complex, the MD simulation run 1 showed two small energy basins, one between \u0026minus;\u0026thinsp;2.4 to -2.5 on PC1 and 1 to 2 on PC2, and the other between 6 to 7 on PC1 and \u0026minus;\u0026thinsp;2 to 0 on PC2 (Fig.\u0026nbsp;12C). For the MD simulation run 2 the lowest energy conformations occupied the energy basin between \u0026minus;\u0026thinsp;2.7 to -2.5 on PC1 and \u0026minus;\u0026thinsp;3 to -2.5 on PC2. Whereas, the MD simulation run 3 showed a small energy basin between 2.9 to 3.0 on PC1 and \u0026minus;\u0026thinsp;1 to 0 on PC2. In the case of EGFR erlotinib complex, the MD simulation run 1 showed the major low energy conformations occupying the larger energy basin between \u0026minus;\u0026thinsp;5 to -4 on PC1 and \u0026minus;\u0026thinsp;1 to 1 on PC2, and few low energy conformations occupying the small energy basin between 2.1 to 2.2 on PC1 and \u0026minus;\u0026thinsp;0.5 to 0 on PC2 (Fig.\u0026nbsp;12D). The MD simulation run 2 showed three small energy basins, one occupying between \u0026minus;\u0026thinsp;0.8 to -0.9 on PC1 and 3.9 to 4.1 on PC2, and the other two in the range 2 to 4 on PC1 and \u0026minus;\u0026thinsp;2 to -1 on PC2. The MD simulation run 3 showed a larger energy basin between \u0026minus;\u0026thinsp;3.8 to -1.6 on PC1 and \u0026minus;\u0026thinsp;2.5 to -1 on PC2, and the other two small energy basins in the range \u0026minus;\u0026thinsp;3.8 to 0 on PC1 and 1.5 to 2 on PC2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section3\"\u003e \u003ch2\u003e3.10.8. Cluster analysis\u003c/h2\u003e \u003cp\u003eThe cluster analysis was performed on each protein-ligand complex in triplicate. The results of cluster analysis for EGFR kaempferol complex showed that in the MD simulation run 1 most of the conformations occurred in clusters 8 and 9 originating from the simulation period 45 ns to 85 ns (Fig.\u0026nbsp;13A). Whereas, in MD simulation run 2 most of the conformations occurred in cluster 6 which originated between simulation period 40 to 55 ns. While the MD simulation run 3 the major cluster was cluster 5 originating between 38 ns to 55 ns simulation period. In the case of EGFR isorhamnetin complex, the MD simulation run 1 had most of the conformations clustered in the major cluster 10 originating from 79 ns until the end of the simulation period (Fig.\u0026nbsp;13B). While the MD simulation run 2 showed the major conformations were clustered in cluster 5 originating between 35 ns to 62 ns simulation period. The MD simulation run 3 major conformations were clustered in cluster 9 originating between 77 ns to 95 ns simulation period. In the case of the EGFR morin complex, cluster 9 in all the triplicate runs had the major conformations, whereas in the MD simulation run this cluster originated between 65 ns to 87 ns (Fig.\u0026nbsp;13C). While, in MD simulation run 2 the cluster originated from 82 ns until the end of the simulation, and the MD simulation run 3 originated from 68 ns to 95 ns. In the case of the EGFR erlotinib complex, the MD simulation run 1 and 3 had the major conformations clustered in cluster 6 originating from the simulation period 38 ns to 57 ns and 30 to 50 ns, respectively (Fig.\u0026nbsp;13D). While the MD simulation run 2 had the major conformations in cluster 5 originating from 38 ns to 55 ns simulation period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section3\"\u003e \u003ch2\u003e3.10.9. MM-GBSA calculation\u003c/h2\u003e \u003cp\u003eThe results of MM-GBSA calculations are presented in (\u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e)\u003c/b\u003e. The EGFR kaempferol complex showed ΔG\u003csub\u003ebinding\u003c/sub\u003e of -34.37, -28.37, -32.95, and \u0026minus;\u0026thinsp;34.37 kcal/mol for run 1, 2, 3, and concatenated trajectories, respectively. Contributions of various energy parameters in ΔG\u003csub\u003ebinding\u003c/sub\u003e is shown in \u003cb\u003eFig.\u0026nbsp;14\u003c/b\u003e. The EGFR isorhamnetin complex showed ΔG\u003csub\u003ebinding\u003c/sub\u003e of -28.34, -32.01, -30.09, and \u0026minus;\u0026thinsp;30.31 kcal/mol for run 1, 2, 3, and concatenated trajectories, respectively. While the EGFR morin complex showed ΔG\u003csub\u003ebinding\u003c/sub\u003e of -27.48, -31.95, 33.08, and \u0026minus;\u0026thinsp;30.92 kcal/mol for run 1, 2, 3, and concatenated trajectories, respectively. The EGFR erlotinib complex showed ΔG\u003csub\u003ebinding\u003c/sub\u003e of -37.47, -39.85, -39.17, and \u0026minus;\u0026thinsp;38.77 kcal/mol for run 1, 2, 3, and concatenated trajectories, respectively. In the case of the EGFR erlotinib complex, the entropic energies (-TΔS) were slightly larger, while the van der Waal\u0026rsquo;s energies (ΔVDWAALS) were significantly lower. In the case of the EGFR morin complex, the electrostatic energies (ΔEEL) were significantly lower and polar solvation energies (ΔEGB) were significantly larger.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eEGFR is a transmembrane tyrosine kinase receptor that plays a crucial role in regulating cellular processes, including proliferation, survival, and differentiation. Mutations in EGFR, particularly exon 19 deletions and the L858R substitution in exon 21, are well-documented oncogenic drivers in NSCLC globally, contributing to constitutive activation of downstream signaling pathways such as PI3K/AKT and RAS/RAF/MEK/ERK (111). The robust characterization of EGFR in NSCLC biology is supported by extensive preclinical and clinical evidence. Preclinical studies using EGFR-mutant cell lines and animal models have demonstrated that inhibiting EGFR significantly reduces tumor growth (112). Clinically, EGFR-targeted therapies have transformed the treatment landscape. First-generation tyrosine kinase inhibitors (TKIs), such as gefitinib and erlotinib, have shown significant improvements in progression-free survival (PFS) and overall response rates (ORR) in EGFR-mutant NSCLC patients compared to chemotherapy. The IPASS trial reported a median PFS of 9.5 months with gefitinib versus 6.3 months with chemotherapy (113). Third-generation TKIs, such as osimertinib, have further improved outcomes, addressing resistance mechanisms, such as T790M mutation. The AURA3 trial demonstrated a median PFS of 10.1 months with osimertinib in T790M-positive patients (114). Compared to other potential targets, EGFR stands out due to its critical role as an oncogenic driver, its well-characterized biology, and the strong clinical evidence supporting its targeting. These factors affirm its therapeutic relevance and justify its selection for this study.\u003c/p\u003e \u003cp\u003eIn this study, we selected a crystal structure of mutated EGFR kinase domain (L858R) as suitable target for designing EGFR TKI. We curated total 687 phytoconstituents of the four selected anticancer plants (\u003cem\u003eCurcuma longa\u003c/em\u003e, \u003cem\u003eCamellia sinensis\u003c/em\u003e, \u003cem\u003eGinkgo biloba\u003c/em\u003e and \u003cem\u003eVitis vinifera\u003c/em\u003e) from renowned comprehensive, ethnopharmacological, phytochemically rich as well as diverse IMPPAT database (49). Remarkably, all of the three promising compounds (kaempferol, morin and isorhamnetin) originated from \u003cem\u003eGinkgo biloba\u003c/em\u003e. Our study highlighted this ancient plant's ability to produce multiple effective EGFR TKIs, which positions it as a noteworthy candidate for developing targeted cancer therapies.\u003c/p\u003e \u003cp\u003eThe selection of kaempferol (CID5280863), morin (CID5281670), and isorhamnetin (CID5281654) was based on a rigorous screening approach that included their anticancer potential as reported in the literature, favorable pharmacological properties, and structural compatibility with the EGFR binding site. These compounds were prioritized due to their promising binding affinity, safety profile, and availability in the selected anticancer plants. Detailed molecular docking analysis revealed that these compounds exhibited favorable docking scores, with binding affinities of -8.5, -8.5, and \u0026minus;\u0026thinsp;8.7 kcal/mol \u003cb\u003e(Supplementary Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e)\u003c/b\u003e respectively, compared to the reference drug erlotinib, which exhibited a docking score of -6.9 kcal/mol. The binding affinity of a ligand to a target protein is significantly influenced by the types and strengths of the non-bond interactions formed. These interactions contribute to the stability and specificity of the protein-ligand complex, which is reflected in the docking scores (115). H-bonds are among the most critical interactions for ligand binding. H-bonds improve binding specificity by forming direct interactions with key amino acid residues in the active site, ensuring that the ligand fits precisely into the binding pocket. These bonds also contribute to the rigidity of the ligand within the site, reducing conformational flexibility and strengthening the protein-ligand complex. This stability is crucial for maintaining a strong and lasting interaction, reflected in higher binding affinity (116). Kaempferol, morin, and isorhamnetin formed a higher number of H-bonds, particularly with critical residues like MET793 and THR790. This correlates directly with their superior binding affinities (-8.5 to -8.7 kcal/mol). The bond distances for these interactions were consistently within the optimal range (2.0\u0026ndash;3.0 \u0026Aring;), indicating strong and stable bonds (109). Erlotinib, with fewer H-bonds and longer bond distances, demonstrated a lower binding affinity (-6.9 kcal/mol). This observation highlights the importance of H-bonding in achieving higher binding affinity. Hydrophobic interactions stabilize the protein-ligand complex by excluding water molecules from the binding pocket. This increases entropy and enhances the thermodynamic stability of the complex. These interactions also help orient and anchor the ligand in the hydrophobic regions of the target protein, complementing H-bonds and further reinforcing the overall binding strength (117). The extensive hydrophobic interactions observed for the selected compounds, including π-alkyl interactions with residues like VAL726 and LEU718, complement the H-bonding network and contribute to their higher binding affinities. Erlotinib, with fewer hydrophobic interactions, lacks this stabilizing contribution, reflecting its comparatively weaker binding. While variability in H-bond angles was observed, particularly in isorhamnetin (90.198\u0026deg; to 157.888\u0026deg;), the angles still included optimal ranges close to 180\u0026deg;, which contributed to strong and stable interactions. This variability did not appear to negatively impact the binding affinity, as isorhamnetin demonstrated the highest binding affinity (-8.7 kcal/mol), highlighting the overall robustness of its interaction profile. The superior interaction profiles of our selected compounds compared to erlotinib (control) are evident in their higher number of H-bonds, optimal bond distances and angles, and extensive hydrophobic interactions. These factors collectively contribute to their higher binding affinities, demonstrating that the nature and quality of non-bond interactions are directly correlated with binding affinity. The results align with the significance of these interaction types in stabilizing and enhancing protein-ligand binding.\u003c/p\u003e \u003cp\u003eThe drug-likeness and bioactivity assessments of the selected natural compounds indicate that they satisfy the key criteria for drug-like molecules, including adherence to Lipinski\u0026rsquo;s Rule of Five (RO5). This ensures favorable bioavailability and oral activity. The ADME and toxicity profiles of our selected compounds underscore their potential as safer and more effective EGFR inhibitors compared to erlotinib. The compounds demonstrated moderate gastrointestinal absorption rates (74.29\u0026ndash;76.014%), sufficient for oral administration and comparable to clinically acceptable levels. VDss (volume of distribution at steady state) is a pharmacokinetic parameter that reflects the extent of drug distribution into tissues relative to the plasma concentration at steady state (118). The VDss values indicate that kaempferol, morin, and isorhamnetin exhibit higher tissue distribution than erlotinib, enabling effective systemic delivery without crossing the blood-brain barrier. The natural compounds do not inhibit or act as substrates for major drug-metabolizing enzymes (CYP3A4, CYP2D6). The clearance rates of the selected compounds ensure balanced elimination, minimizing risks of both rapid clearance and prolonged accumulation. Additionally, the absence of mutagenicity, hepatotoxicity, and hERG inhibition highlights their superior safety profiles compared to erlotinib, which exhibits significant toxicities.\u003c/p\u003e \u003cp\u003eFrom a structural perspective, the inhibitory activity of kaempferol, morin, and isorhamnetin is attributed to the presence of multiple hydroxyl groups, which facilitate hydrogen bonding with amino acid residues in the ATP-binding site of EGFR. The addition of a methoxy group at the 4'-position in isorhamnetin enhances its lipophilicity and membrane permeability, likely contributing to its superior bioavailability and efficacy as an EGFR tyrosine kinase inhibitor (TKI). These structural attributes, combined with their planar geometry and favorable hydroxylation patterns, support their potential as effective EGFR TKIs with enhanced binding affinity and specificity. Pharmacophore modeling results further emphasize the optimal binding geometry and conformational stability of the selected compounds compared to erlotinib. While erlotinib's bulky and rigid quinazoline core limits its specificity and selectivity as an EGFR TKI, the natural compounds demonstrate enhanced flexibility and better adaptability within the binding site. Additionally, their bioactivity scores indicate strong potential for EGFR tyrosine kinase inhibition (\u0026gt;\u0026thinsp;0.00 in the kinase inhibitor category) with minimal off-target effects, as reflected in their low scores for GPCR ligand activity, ion channel modulation, and protease inhibition. This specificity may reduce the side effects commonly associated with broader-target drugs like erlotinib. The comprehensive analysis of ADME properties, pharmacophore features, and bioactivity scores suggests that the selected compounds offer significant advantages as EGFR inhibitors, including superior safety, specificity, and binding affinity.\u003c/p\u003e \u003cp\u003eThe molecular dynamics (MD) simulations provided valuable insights into the stability, conformational dynamics, and binding interactions of EGFR with kaempferol, isorhamnetin and morin. The triplicate MD simulation runs demonstrated that all complexes achieved stable conformations over the simulation period, indicating robust protein-ligand interactions. Notably, the EGFR complexes with kaempferol and isorhamnetin showed lower RMSD values in the EGFR backbone and ligand atoms compared to erlotinib, suggesting higher stability and stronger binding. Analysis of RMSF values highlighted comparable flexibility of binding site residues across all complexes, confirming the ligands\u0026rsquo; ability to stabilize the EGFR binding site. However, the loop region (residues 855\u0026ndash;875) exhibited higher fluctuations across all complexes, reflecting inherent flexibility in this region that did not affect overall stability. The radius of gyration and SASA analysis further validated the compactness and stability of all protein-ligand complexes, with the EGFR-morin complex showing the most compact structure. The H-bond analysis revealed that kaempferol, isorhamnetin, and morin consistently formed more H-bonds with critical residues (e.g., MET793, THR790, GLN791) than erlotinib, indicating stronger and more stable interactions. Contact frequency analysis corroborated these findings, with morin exhibiting frequent interactions with key residues, reinforcing its potential as a strong EGFR binder. Energy-based evaluations, including MM-GBSA calculations, indicated that erlotinib had a slightly stronger binding affinity due to its hydrophobic interactions and van der Waals contributions. However, kaempferol and isorhamnetin exhibited comparable binding free energies, with kaempferol showing superior electrostatic interactions. The polar nature of morin resulted in higher solvation energies, which could affect its binding affinity. Overall, the MD simulation analysis suggests that kaempferol and isorhamnetin exhibit higher stability and stronger binding interactions with EGFR compared to erlotinib, while morin demonstrates unique binding dynamics that merit further investigation. These findings highlight the potential of the selected natural compounds as viable EGFR inhibitors for NSCLC therapy.\u003c/p\u003e \u003cp\u003eThis study highlights several advantages of kaempferol, morin, and isorhamnetin as EGFR inhibitors. The natural availability of these flavonoids makes them cost-effective and eco-friendly, especially for resource-limited settings. Their structural attributes, such as hydroxylation patterns, enhance binding affinity and specificity for EGFR. Their moderate absorption rates and favorable tissue distribution (higher VDss scores) ensure effective systemic delivery while avoiding CNS-related side effects. Unlike erlotinib, these compounds avoid CYP3A4 and CYP2D6 inhibition, reducing metabolism-related drug-drug interaction risks and enhancing their therapeutic suitability. The selected compounds lack mutagenicity, hepatotoxicity, and hERG inhibition, suggesting minimal risk of side effects.\u003c/p\u003e \u003cp\u003eDespite the promising findings, this study has several limitations that should be acknowledged. The study primarily relies on computational predictions, including molecular docking and ADME-Tox analysis, which do not account for the complexity of biological systems such as metabolism, enzymatic interactions, and immune responses. Experimental validation through \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e studies is required to confirm binding affinities, pharmacokinetics, and anticancer efficacy. All the selected compounds are P-gp substrates, potentially limiting their oral bioavailability due to efflux by P-glycoprotein. The study focuses exclusively on EGFR inhibition without evaluating activity against other cancer-related pathways, such as PI3K/AKT or VEGF. Investigating these pathways could provide a broader understanding of the therapeutic potential and safety profile of the compounds. Although erlotinib was used as a reference drug, head-to-head experimental comparisons with the selected compounds were not performed. This limits the ability to directly evaluate their clinical superiority over erlotinib. Factors essential for clinical development, such as the chemical stability, scalability for production and formulation challenges of the selected natural compounds, were not addressed. These aspects need to be explored in future studies to facilitate their potential use in clinical settings.\u003c/p\u003e \u003cp\u003eThe future of this research lies in the comprehensive experimental validation and further optimization of isorhamnetin, kaempferol, and morin as EGFR inhibitors. First, \u003cem\u003ein vitro\u003c/em\u003e assays (e.g., kinase activity and cytotoxicity studies) and \u003cem\u003ein vivo\u003c/em\u003e studies in animal models are essential to confirm their binding affinities, anticancer efficacy, pharmacokinetics, and safety profiles. These validations will establish their translational potential for clinical applications. Second, advanced drug delivery strategies, such as nanoparticle-based systems, liposomes, and micelles, can address the P-gp substrate activity of these compounds, improving their bioavailability and therapeutic outcomes (119\u0026ndash;121). The development of prodrugs may also enhance their metabolic stability and tissue specificity, optimizing their pharmacokinetic properties (122). Third, their potential in combination therapies warrants investigation. Synergistic effects with existing EGFR inhibitors, such as erlotinib, or other anticancer agents could enhance efficacy, reduce resistance, and minimize side effects (123). Future studies should focus on preclinical evaluation of such combinations to identify optimal therapeutic regimens. Fourth, while this study focuses on EGFR inhibition, future research should explore the activity of these compounds against other cancer-related pathways, including PI3K/AKT/mTOR signaling, VEGF-mediated angiogenesis, and MAPK/ERK pathways. This broader target exploration can uncover additional therapeutic applications and improve their versatility as anticancer agents. Fifth, as naturally derived compounds, isorhamnetin, kaempferol, and morin offer a sustainable and cost-effective approach to cancer therapy. Future research should explore scalable and eco-friendly extraction and production methods to ensure their accessibility, particularly in resource-limited settings. Finally, these compounds hold potential for integration into personalized medicine. Patients with EGFR mutations or overexpression could benefit from targeted therapies based on these flavonoids. Advances in biomarker-driven cancer therapies and pharmacogenomics can further tailor their use to specific patient populations, ensuring maximum therapeutic benefit. By addressing these future directions, this research could significantly contribute to the development of safe, effective, and accessible flavonoid-based therapies for EGFR-dependent cancers and beyond.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eEGFR-mutated NSCLC is a significant and evolving disease, heavily dependent on EGFR signaling. The emergence of resistance to EGFR inhibitors presents a challenge, highlighting the need for novel targeted therapies. Overall, our study presents a robust strategy for the discovery of natural EGFR inhibitors as safer and more sustainable alternatives to synthetic TKIs. The pharmacological properties and interaction dynamics assessed through our comprehensive computational workflow lay a solid foundation for future \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e studies. These studies will be essential to validate the therapeutic potential of the identified compounds against mutant EGFR in NSCLC.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e7. Acknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to extend a special acknowledgment to Mahmudul Islam (Department of Genetic Engineering and Biotechnology, Jashore University of Science and Technology, Jashore 7408, Bangladesh) for his invaluable contributions in collecting data from the SwissADME and ProTox-II web servers and for expertly preparing the tables related to ADME and toxicity analysis. Your support has been instrumental to the success of this research.\u003c/p\u003e\n\u003cp\u003eThis research is dedicated to the extraordinary undergraduate and graduate students of Bangladesh. Their steadfast dedication, integrity, and unwavering pursuit of scientific excellence serve as a continuous source of inspiration. Despite facing significant challenges\u0026mdash;including limited access to laboratories, a lack of research funding, and minimal institutional support\u0026mdash;these remarkable individuals find ways to carve out time from their rigorous academic schedules to conduct and publish impactful research. Their tireless efforts not only enhance the prestige of their departments and universities but also stand as a testament to their commitment to advancing knowledge.\u003c/p\u003e\n\u003cp\u003eWe express our heartfelt gratitude to all those who champion and nurture the spirit of quality research, whether in computational or laboratory settings. Your encouragement fuels our ambition to persevere and excel. Instead of seeking discouragement, we embrace motivation to grow and thrive, no matter our starting point. Our journey is defined by resilience and passion, and we are dedicated to making meaningful contributions to the scientific community.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e8. Data availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData included in the article/supplementary material is referenced in the article.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eC.D.D. performed conceptualization, formal analysis, data curation, methodology, manuscript writing, and reviewing. S.H., M.M.K.C., M.S.F.R., and T.K. contributed to methodology, visualization, manuscript writing, and reviewing. A.M.E. contributed to methodology, manuscript writing, and reviewing. A.T.M. was involved in methodology, visualization, manuscript writing, reviewing, project administration, and supervision. S.N.V. and R.B.P. contributed to methodology, visualization, manuscript writing, and reviewing, with R.B.P. also contributing to supervision. B.K.S. was involved in methodology, visualization, manuscript writing, reviewing, project administration, and supervision. All authors reviewed the manuscript\u003c/p\u003e\n\u003ch2\u003e\u0026nbsp;\u003c/h2\u003e\n\u003ch2\u003e\u0026nbsp;\u003c/h2\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003e Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021 May;71(3):209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Gridelli C, Rossi A, Carbone DP, Guarize J, Karachaliou N, Mok T, et al. Non-small-cell lung cancer. Nat Rev Dis Prim. 2015;1:1\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e Yasuda H, Kobayashi S, Costa DB. EGFR exon 20 insertion mutations in non-small-cell lung cancer: Preclinical data and clinical implications. 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Available from: https://doi.org/10.1021/tx049833j\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Structure-based drug design, Epidermal Growth Factor Receptor (EGFR), Non-Small Cell Lung Cancer (NSCLC), Molecular Docking, Molecular Dynamics Simulation, Phytochemical Screening, In silico ADME Analysis","lastPublishedDoi":"10.21203/rs.3.rs-6422271/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6422271/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Mutations in the epidermal growth factor receptor (EGFR), particularly in the tyrosine kinase domain such as exon 19 deletions and the L858R point mutation, play a critical role in the development of non-small cell lung cancer (NSCLC). EGFR is a well-established therapeutic target in the management of NSCLC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e In this study, we targeted the mutated EGFR kinase domain (L858R) using its crystal structure (PDB ID: 2EB3) to design EGFR tyrosine kinase inhibitors (TKIs). We curated a library of 687 phytoconstituents from four anticancer plants (\u003cem\u003eCamellia sinensis\u003c/em\u003e, \u003cem\u003eCurcuma longa\u003c/em\u003e, \u003cem\u003eGinkgo biloba\u003c/em\u003e, and \u003cem\u003eVitis vinifera\u003c/em\u003e) using the IMPPAT database. Kaempferol, morin, and isorhamnetin, all from \u003cem\u003eGinkgo biloba\u003c/em\u003e, emerged as promising candidates. Drug-likeness and ADMET analyses were performed to evaluate the pharmacokinetic and safety profiles of these compounds. Pharmacophore modeling and bioactivity score analysis were also conducted. Finally, molecular dynamics (MD) simulations were performed to assess the stability of the EGFR-ligand complexes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings:\u003c/strong\u003e The docking studies revealed high binding energies for kaempferol (-8.5 kcal/mol), morin (-8.5 kcal/mol), and isorhamnetin (-8.7 kcal/mol) with the EGFR active site, compared to the reference drug, erlotinib (-6.9 kcal/mol). These compounds exhibited superior pharmacokinetic properties, including high gastrointestinal absorption and non-inhibition of P-glycoprotein activity, unlike erlotinib. Toxicity predictions showed mild immunotoxicity for morin and isorhamnetin, with all compounds demonstrating no hepatotoxicity and no inhibition of CYP3A4 or CYP2D6 enzymes. Structural analysis highlighted the hydroxyl groups in the selected compounds as key for hydrogen bond (H-bond) formation with EGFR residues, enhancing their inhibitory potential. MD simulations confirmed the stability of EGFR complexes with the selected compounds, showing lower average RMSD values and better convergence compared to the EGFR-erlotinib complex.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e This research underscores the potential of kaempferol, morin, and isorhamnetin as novel EGFR inhibitors derived from \u003cem\u003eGinkgo biloba\u003c/em\u003e for NSCLC treatment. These compounds demonstrated strong binding affinities, favorable pharmacokinetic properties, and stability \u003cem\u003ein silico\u003c/em\u003e. Further \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e validation is necessary to confirm their efficacy against mutated EGFR in NSCLC.\u003c/p\u003e","manuscriptTitle":"In silico exploration of anticancer plant phytochemicals for EGFR-targeted lung cancer therapy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-07 07:28:31","doi":"10.21203/rs.3.rs-6422271/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-21T08:19:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-20T11:23:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-12T10:37:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"62070857509753291310373704934631944378","date":"2025-05-03T02:40:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"50460314400981120209083139260525310243","date":"2025-05-02T09:39:15+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-02T09:33:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-02T09:14:33+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-24T03:32:22+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-22T16:01:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-10T17:13:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f858088e-90d4-4c0a-a2fe-834cede299b5","owner":[],"postedDate":"May 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":48180597,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":48180598,"name":"Biological sciences/Drug discovery"}],"tags":[],"updatedAt":"2025-08-04T16:48:08+00:00","versionOfRecord":{"articleIdentity":"rs-6422271","link":"https://doi.org/10.1038/s41598-025-10412-4","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-07-30 16:29:36","publishedOnDateReadable":"July 30th, 2025"},"versionCreatedAt":"2025-05-07 07:28:31","video":"","vorDoi":"10.1038/s41598-025-10412-4","vorDoiUrl":"https://doi.org/10.1038/s41598-025-10412-4","workflowStages":[]},"version":"v1","identity":"rs-6422271","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6422271","identity":"rs-6422271","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}