Identification of Novel Tubulin Dimer Inhibitors for Pancreatic Cancer: An Integrated Computational Approach

Identification of Novel Tubulin Dimer Inhibitors for Pancreatic Cancer: An Integrated Computational Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of Novel Tubulin Dimer Inhibitors for Pancreatic Cancer: An Integrated Computational Approach Hemantha Mani Kumar Chakravarthi Chanda, Sudheer Kumar Katari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8016981/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Pancreatic cancer remains one of the most lethal malignancies, with mortality rates exceeding 90% in males and 89% in females, largely due to late-stage diagnosis and chemotherapy resistance. Despite advances in cancer research, effective treatment options for pancreatic cancer remain limited, highlighting the urgent need for novel therapeutic strategies. Identification of potent tubulin dimer inhibitors, disrupting the Alpha-Beta dimer complex essential for microtubule dynamics and cancer cell proliferation, offers a promising therapeutic avenue. Computational drug discovery provides an effective strategy to find novel inhibitors with enhanced binding efficiency, stability, and adaptability for personalized treatments. We implemented a multi-faceted computational approach combining molecular docking, density functional theory (DFT), molecular dynamics (MD) simulations, and principal component analysis (PCA). DrugBank screening identified Bisphosphonate-8 and Fenoldopam as potential inhibitors, with Paclitaxel as a reference drug. DFT calculations provided quantum mechanical insights into ligand-tubulin interactions, while binding energy analysis revealed Bisphosphonate-8 exhibiting a nearly 2-fold higher binding affinity than Paclitaxel. Microsecond-scale MD simulations assessed the stability and flexibility of ligand-protein complexes, and PCA analysis of MD simulations trajectories demonstrated significant conformational adaptability, reinforcing the potential of Bisphosphonate-8 and Fenoldopam as effective tubulin dimer inhibitors. Bisphosphonate-8, demonstrating superior binding properties in silico, emerges as a promising candidate for preclinical evaluation in pancreatic cancer, offering translational potential for improved targeted therapy. Pancreatic cancer alpha-beta tubulin dimer Bisphosphonate-8 tubulin inhibitors binding free energy conformational adaptability molecular dynamics simulations 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 Figure 15 Figure 16 Figure 17 Figure 18 Introduction Pancreatic cancer remains one of the most challenging malignancies in oncology, characterized by aggressive progression, poor prognosis, and a dismal five-year survival rate. The urgent need for new therapeutic strategies is underscored by the alarming statistics: in 2021 alone, over half a million new cases were diagnosed globally, with a similar number of deaths attributed to the disease [ 1 , 2 ]. These figures highlight the increasing incidence and exceptionally high mortality rates, often exceeding 90% in both males and females [ 3 , 4 ]. This grim reality positions pancreatic cancer as a leading cause of cancer-related deaths worldwide, emphasizing the critical need for more effective therapeutic interventions [ 5 , 6 ]. The consistently high mortality across different reporting periods and geographical locations further underscores the limitations of current therapies. The rising incidence further amplifies the urgency for ground-breaking approaches to combat this devastating disease [ 7 ]. Table 1 outlines worldwide data for pancreatic cancer from 2017 to 2022, focusing on new cases, mortality, and age-standardized incidence and death rates. The data reveals an upward trend in both incidence and deaths over the years, solidifying pancreatic cancer as one of the deadliest cancers globally. The age-standardized incidence and death rates reflect the substantial impact of this disease across diverse populations, with the highest numbers observed in 2021. This data underscores the urgent need for effective treatments and interventions for pancreatic cancer, which continues to pose a significant challenge to global healthcare systems. Notably, while data for 2022 shows some improvements in mortality rates, the overall impact remains devastating [ 3 , 8 – 12 ] (Table 1 ). Table 1 Global Incidence and Mortality Statistics for Pancreatic Cancer (2017–2022) Year New Cases (Global) Deaths (Global) Age-Standardized Incidence Rate (per 100,000) Age-Standardized Mortality Rate (per 100,000) Source 2017 447,665 441,083 58.6 57.7 [ 3 , 8 ] 2019 489,862 486,869 6.04 6.03 [ 8 ] 2020 495,773 466,003 4.9 4.5 [ 9 ] 2021 508,532 505,752 5.96 5.95 [ 10 , 11 ] 2022 510,992 N/A 4.7 3.5 [ 12 ] This table presents the global data on new cases, deaths, age-standardized incidence, and mortality rates for pancreatic cancer over recent years. The data highlights the increasing trend of pancreatic cancer cases and deaths, emphasizing the severity of the disease and its growing global burden. The age-standardized incidence and mortality rates provide insights into the impact of pancreatic cancer, adjusted for population age structures across different regions. Despite progress in cancer research and treatment, options for pancreatic cancer remain severely limited, primarily due to late-stage diagnosis and the development of chemotherapy resistance [ 13 ]. Surgical resection, often followed by adjuvant chemotherapy, is currently the only treatment offering a potential cure [ 14 ]. However, this option is available to fewer than 20% of patients, as the majority are diagnosed with advanced-stage disease, precluding surgical intervention [ 15 ]. Even for those who undergo surgery, the five-year survival rate remains below 10%, indicating a high probability of recurrence. Moreover, surgical procedures like the Whipple procedure carry significant morbidity risks [ 16 ]. The overall survival rate for pancreatic cancer stands at a meager 5%, even among patients with initially resectable tumours [ 17 ]. A considerable percentage of patients receive no active treatment, highlighting the inadequacy of current approaches for managing this aggressive disease [ 17 ]. Tumour size, invasion into blood vessels, and lymph node metastasis are associated with poorer outcomes, underscoring the complex and aggressive nature of pancreatic cancer [ 18 , 19 ]. This challenging therapeutic landscape necessitates the urgent exploration of novel and targeted therapies to improve patient outcomes. A promising avenue for therapeutic intervention lies in disrupting the function of the tubulin α-β dimer, a critical component of microtubules [ 20 ]. Microtubules, dynamic structures composed of α- and β-tubulin heterodimers, play essential roles in various cellular processes, including cell movement, intracellular transport, and mitosis. Given the rapid proliferation of cancer cells, the microtubule network, and specifically the tubulin dimer, is a compelling therapeutic target [ 21 , 22 ]. Tubulin-binding chemotherapeutic agents have long been recognized for their ability to disrupt the dynamics of the mitotic spindle, leading to mitotic arrest and subsequent cell death in rapidly dividing cancer cells [ 23 ]. Alterations in tubulin dimer dynamics, including changes in stability, isotype expression, and post-translational modifications, have been observed in various cancers, often correlating with poor prognosis and chemoresistance [ 24 – 26 ]. Recent studies suggest that these alterations in dimer dynamics may contribute to drug resistance by modifying the drug binding affinity at the tubulin interface, promoting microtubule destabilization that reduces drug efficacy, and altering tubulin post-translational modifications that interfere with chemotherapeutic targeting [ 47 – 50 ]. Targeting the α-β tubulin dimer specifically offers the potential to circumvent resistance mechanisms associated with targeting polymerized microtubules, as the dimer may be more abundant or accessible in cancer cells exhibiting microtubule destabilization or resistance phenotypes [ 27 , 28 ]. This rationale strongly supports the investigation of molecules that directly stabilize or inhibit the tubulin dimer, potentially offering improved therapeutic outcomes in drug-resistant pancreatic cancer. Among these, Paclitaxel remains one of the most widely used microtubule-stabilizing agents (Table 2 ); however, it often exhibits suboptimal stability and binding affinity when interacting with the isolated tubulin heterodimer, potentially limiting its efficacy in cases of microtubule destabilization or drug resistance [ 29 – 38 ]. This highlights a critical unmet need for agents that effectively target the unassembled tubulin dimer with enhanced stability and interaction dynamics [ 31 ]. Furthermore, the potential of non-traditional tubulin-targeting compounds, such as Bisphosphonate-8 and Fenoldopam, has not been thoroughly explored in this context (Table 2 ). Table 2 Overview of Selected Clinically Used Microtubule-Targeting Agents (MTAs), Their Mechanisms, and Target Cancers Drug Name Mechanism of Action Target Binding Site Examples of Cancers Treated Source Paclitaxel Microtubule stabilizer Taxane Ovarian, breast, lung, pancreatic [ 29 ] Docetaxel Microtubule stabilizer Taxane Breast, prostate, lung, gastric, head and neck [ 32 ] Vincristine Microtubule destabilizer Vinca Leukemia, lymphoma [ 33 ] Vinblastine Microtubule destabilizer Vinca Lymphoma, Hodgkin's disease, testicular cancer [ 34 ] Cabazitaxel Microtubule stabilizer Taxane Metastatic castration-resistant prostate cancer [ 35 ] Eribulin Microtubule destabilizer Non-taxane Metastatic breast cancer, metastatic or unresectable liposarcoma [ 36 ] Ixabepilone Microtubule stabilizer Epothilone Metastatic or locally advanced breast cancer [ 37 ] Trastuzumab Emtansine Antibody-drug conjugate (MTI) Tubulin HER2-overexpressing breast cancer [ 38 ] This table provides a summary of prominent microtubule-targeting agents (MTAs) used in oncology. It details their primary mechanism of action (microtubule stabilizer or destabilizer), their general target binding site on tubulin (e.g., Taxane site, Vinca site), and examples of cancers for which they are clinically indicated. This provides context for the established therapeutic strategy of targeting tubulin. This research addresses these gaps by employing an integrated computational approach to investigate the stability, binding affinity, and molecular dynamics of novel and repurposed compounds, identified through DrugBank screening, with the tubulin α-β dimer. Paclitaxel serves as a clinically relevant control due to its widespread use in pancreatic cancer therapy and its well-characterized mechanism of (primarily polymerized) microtubule stabilization, providing a benchmark for comparing the novel dimer-targeting agents. Bisphosphonate-8 and Fenoldopam were selected based on preliminary screening data suggesting potential tubulin-binding activity and their unique chemical structures, offering potential for repurposing or development as alternative tubulin inhibitors. Our computational investigation utilizes molecular docking, density functional theory (DFT), molecular dynamics (MD) simulations, and principal component analysis (PCA) to thoroughly evaluate the interactions of these compounds with the tubulin dimer. The primary objective is to provide in silico evidence supporting the potential of Bisphosphonate-8 as a promising tubulin dimer inhibitor for pancreatic cancer, warranting further experimental validation and preclinical evaluation (Fig. 1 ). Methodology This study investigated the potential of Bisphosphonate-8 as a novel tubulin dimer inhibitor for pancreatic cancer therapy using a comprehensive in silico approach. The methodology integrated target identification, molecular docking, DFT calculations, optimized docking, MD simulations, and PCA. Paclitaxel, a clinically established microtubule stabilizer used in cancer therapy, served as a control due to its well-characterized mechanism of action and clinical relevance in pancreatic cancer (Fig. 1 ). 3.1. Target Identification and Control Selection for Pancreatic Cancer Tubulin, a critical protein involved in cell division, was selected as the primary therapeutic target due to its essential role in cellular proliferation and established relevance in cancer treatment [ 26 ]. Specifically, the α-β tubulin dimer, the building block of microtubules, was targeted in this study. Microtubules, polymers of α- and β-tubulin, form the mitotic spindle, crucial for chromosome segregation during cell division [ 39 ]. Disrupting microtubule dynamics through dimer stabilization or inhibition can lead to mitotic arrest and apoptosis in rapidly dividing cancer cells. Paclitaxel, a member of the taxane family and a clinically used chemotherapeutic agent for various solid tumours, including pancreatic cancer [ 40 ], served as the control. Paclitaxel stabilizes microtubules by binding to β-tubulin, preventing depolymerization, which leads to cell cycle arrest at the G2/M phase and subsequent apoptosis [ 41 ]. While Paclitaxel's primary interaction is with polymerized microtubules, its clinical significance and well-understood mechanism make it a relevant benchmark for comparing novel tubulin dimer inhibitors. Bisphosphonate-8 and Fenoldopam were selected as potential dimer inhibitors based on preliminary in silico screening and their distinct chemical structures, suggesting potential for novel mechanisms of action. 3.2. Screening With Databases Based on Molecular Docking Using AutoDock Vina The high-resolution crystal structure of the tubulin α-β heterodimer (PDB ID: 7PJF) was retrieved from the Protein Data Bank (PDB). Both the protein and ligands (Bisphosphonate-8, Fenoldopam, and Paclitaxel) obtained from the DrugBank database were prepared for docking using Python Molecular Viewer (PMV) [ 42 , 43 ]. Water molecules were removed, hydrogen atoms were added, and Kollman and Gasteiger charges were assigned to ensure accurate representation of electrostatic interactions [ 44 ]. A docking grid encompassing the known Paclitaxel binding site on β-tubulin [ 45 ] was defined with dimensions of 60 Å x 60 Å x 60 Å, centred at coordinates X = 148.46, Y = 7.17, and Z = 46.73. Initial docking and virtual screening were performed using AutoDock Vina (version 1.1.2) [ 46 ] with an exhaustiveness parameter of 32 for independent runs. The top two scoring compounds from the DrugBank screen, along with Paclitaxel, were then subjected to more precise docking using AutoDock4 (version 4.2.6) [ 47 ]. AutoDock4, with its more sophisticated search algorithm and energy function, was employed with an exhaustiveness of 32 and 100 of runs to refine the binding poses and calculate more accurate binding energies. 3.3. DFT Optimization of Ligands and Binding Affinity Calculations To gain deeper insights into the nature of ligand-tubulin interactions at the quantum mechanical level DFT calculations were performed using Gaussian version 16 [ 48 ]. The B3LYP hybrid functional, known for its good performance in describing non-covalent interactions, was employed in conjunction with the 6-311G(d,p) basis set, which includes polarization functions for both hydrogen and heavy atoms, enhancing accuracy [ 48 , 49 ]. Geometry optimizations and frequency calculations were performed for the individual ligands, the tubulin residues within 2.5 Å of the predicted binding site, and the ligand-protein complexes. The binding energy (EB) for each complex was calculated as: EB = EC - (EP + EL), where EC, EP, and EL represent the energies of the complex, protein, and ligand, respectively. This analysis provided a detailed assessment of the energetic contributions, including electrostatic interactions, hydrogen bonding, and van der Waals forces, to the ligand-protein binding. 3.4. Optimized Ligands Docking Through AutoDock4 Following DFT optimization of ligand geometries, redocking was performed using AutoDock4 (parameters as described in 3.2) to evaluate the impact of geometry refinement on binding affinity and pose. This step ensured that the docking results accurately reflected the ligands' preferred conformations [ 50 , 51 ]. 3.5. MD Simulations for Complexes of Tubulin with Optimized Ligands Using Desmond MD simulations were performed using Desmond (version 2021-4, Schrödinger, 2023.2) [ 52 ] to assess the stability and dynamic behaviour of the tubulin-ligand complexes in a simulated physiological environment. The OPLS4 force field [ 53 ] was used to parameterize the systems. The optimized ligand structures and Paclitaxel were prepared using the System Builder tool in Maestro (Schrödinger). The protein was prepared using the Protein Preparation Wizard, removing unnecessary components and performing energy minimization [ 52 , 54 ]. Each complex was solvated in an orthorhombic box with the SPC, extending 10 Å beyond the protein in all directions. Sodium (Na⁺) and chloride (Cl⁻) ions were added to neutralize the system and mimic a physiological salt concentration of 0.15 M. The systems were energy minimized using the steepest descent algorithm and equilibrated in two phases: NVT (constant number of particles, volume, and temperature) for 500 picoseconds (ps) followed by NPT (constant number of particles, pressure, and temperature) for 1 nanosecond (ns) at 300 K and 1 bar pressure to ensure system stability. A 2 femtoseconds (fs) time step was used, and trajectories were saved every 1 ps. Periodic boundary conditions and the Particle Mesh Ewald (PME) method were employed to handle long-range electrostatic interactions [ 52 , 54 ]. Trajectories were analyzed for root mean square deviation (RMSD) and root mean square fluctuation (RMSF) of the protein backbone and ligand atoms, as well as for persistent intermolecular interactions (hydrogen bonds, van der Waals, metallic, electrostatic) [ 55 , 56 ]. 3.6. MD Simulations Trajectory Analysis Clustering The MD simulations trajectories were clustered using the Desmond Trajectory Clustering algorithm implemented in Desmond based on the protein C-α RMSD matrix, using a cut-off of 10 to identify the dominant conformational states of the tubulin-ligand complexes. Clustering analysis Opens the RMSD Based Clustering of Frames from Desmond Trajectory panel.[ 57 ] Radial Distribution Function (RDF) Analysis RDF analysis was performed to characterize the distribution of ligand atoms and residues in chain B of tubulin and identify persistent interactions during the simulation using RDF tool in Desmond software. RDFs were calculated for ligand atoms and residues in chain B of tubulin to determine the average distances and fluctuations of these interactions [ 58 , 59 ]. PCA for MD Simulations Trajectories PCA was performed using the Bio3D package in R programming language [ 60 ] to identify the dominant conformational changes and dynamic flexibility of the tubulin α-β dimer upon ligand binding (Bisphosphonate-8, Fenoldopam, and Paclitaxel). MD simulations trajectories from 1 microsecond (µs) simulations, with frames saved every 10 ps, were used for this analysis. The trajectories were first aligned to the initial reference structure, and a covariance matrix of the positional fluctuations of all Cα atoms was constructed to capture correlated and anticorrelated motions. Eigenvectors and eigenvalues were then computed, with eigenvectors representing the direction of atomic motion and eigenvalues quantifying the magnitude of those motions. PCA was also extended to include ligand atoms within the binding pocket to assess their flexibility and interaction stability. The first three principal components (PC1-PC3), which captured the most significant collective motions, were analyzed, with particular focus on the first two for projecting conformational space and comparing the apo and holo forms. This approach enabled visualization of ligand-induced shifts in protein dynamics and provided insights into the structural adaptability of the tubulin-ligand complexes [ 61 – 64 ]. Results 4.1 Molecular Docking, Interactions and Binding Site Analysis Molecular docking was performed to assess the binding affinity and characterize the interactions of Bisphosphonate-8, Fenoldopam, and Paclitaxel with the tubulin α-β dimer. Initial screening using AutoDock Vina identified Bisphosphonate-8 and Fenoldopam as potential tubulin inhibitors, exhibiting docking scores of -13.224 and − 12.339 kcal/mol, respectively, compared to -4.718 kcal/mol for Paclitaxel. Subsequent docking with AutoDock4 (Table 3 ) provided a more refined assessment of binding energies. Bisphosphonate-8 displayed the most favourable binding free energy (-13.63 kcal/mol) and a remarkably low inhibition constant (Ki = 102.26 picomolar [pM]), indicating high binding potency. Fenoldopam exhibited a less favourable binding free energy (-8.63 kcal/mol) and a Ki of 468.90 nanomolar (nM), while Paclitaxel showed the weakest binding (-6.19 kcal/mol and a Ki of 29.05 micromolar [µM]). Decomposition of the binding free energy revealed that van der Waals interactions and desolvation energy were the primary driving forces for binding in all three complexes, with Bisphosphonate-8 demonstrating the strongest contributions. Electrostatic interactions were negligible for Bisphosphonate-8 and Paclitaxel, while Fenoldopam exhibited a slight negative electrostatic contribution (-1.28 kcal/mol). Bisphosphonate-8 and Fenoldopam maintained low RMSD values (1.409 Å and 1.594 Å, respectively) relative to the initial docked pose, indicating conformational stability within the binding site. In contrast, Paclitaxel displayed a higher RMSD (4.664 Å), suggesting a less stable binding conformation (Table 3 ). Table 3 Comparative Molecular Docking Parameters of Bisphosphonate-8, Fenoldopam, and Paclitaxel with the Tubulin α-β Dimer S. No Parameter Bisphosphonates-8 Fenoldopam Paclitaxel 1 Estimated Free Energy of Binding (kcal/mol) -13.63 -8.63 -6.19 2 Estimated Inhibition Constant, Ki 102.26 pM 468.90 nM 29.05 µM 3 Final Intermolecular Energy (kcal/mol) -16.02 -9.53 -11.26 4 vdW + Hbond + Desolvation Energy (kcal/mol) -16.02 -8.25 -10.92 5 Electrostatic Energy (kcal/mol) 0.00 -1.28 -0.34 6 Torsional Free Energy (kcal/mol) 2.39 0.89 5.07 7 Internal Energy After Binding (kcal/mol) -3.74 -0.38 -5.07 8 Internal Energy brfore Binding (kcal/mol) -3.74 -0.38 -5.07 9 RMSD from Reference Structure (Å) 1.409 1.594 4.664 10 Interactions Hydrogen bonds (3) Ser 140, Glu 183, Tyr 224 Hydrogen bonds (2) Gln 11, Glu 183 Hydrogen bonds (3) Asn 101, Glu 183, Asn 206 Metal bonds (1) Mg 502 This table summarizes the predicted binding affinities and interaction parameters from AutoDock4 calculations for the studied ligands with the tubulin α-β dimer (PDB ID: 7PJF). Lower estimated binding free energy (kcal/mol) and estimated inhibition constant (Ki) values indicate stronger predicted binding. Energy components (intermolecular, vdW + Hbond + Desolvation, electrostatic, torsional) provide insights into the forces driving complex formation and ligand strain. • Internal energy before and after binding was identical for each ligand, indicating no significant induced conformational strain upon binding. • RMSD (Root Mean Square Deviation) of ligand heavy atoms from the reference docked pose, indicating conformational stability. • Predominant hydrogen bond interactions are listed; "Metal bonds" refers to coordination with Mg-502. Analysis of the docking poses (Fig. 2 ) revealed key interactions between the ligands and the tubulin dimer. Bisphosphonate-8 formed three hydrogen bonds with Ser140, Glu183, and Tyr224 within the β-tubulin binding pocket. Fenoldopam formed two hydrogen bonds with Gln11 and Glu183 (Fig. 3 ). Paclitaxel interacted with Asn101, Glu183, and Asn206 via hydrogen bonds and formed a metal coordination bond with Mg502 (Fig. 4 ). The more extensive hydrogen bonding network observed for Bisphosphonate-8 likely contributes to its higher binding affinity compared to Fenoldopam and Paclitaxel. All three ligands interacted exclusively with the β-tubulin monomer. Bisphosphonate-8 penetrated deeply into the binding pocket, exhibiting a close interaction with the magnesium ion present in the active site. Fenoldopam also exhibited a good fit within the pocket, whereas Paclitaxel showed a less optimal fit, consistent with its higher RMSD and weaker binding energy (Fig. 2 ). 4.2 In Silico Physicochemical and Pharmacokinetic Profiling Highlights Bisphosphonate - 8's Favourable Drug-Like Properties To evaluate the drug development potential of the identified compounds, key physicochemical characteristics, lipophilicity, water solubility, pharmacokinetic properties (ADMET), drug-likeness, and medicinal chemistry attributes were computationally predicted, with detailed parameters presented in Table 4 (Table 4 ). Table 4 Predicted Physicochemical, ADMET, and Drug-Likeness Properties of Bisphosphonate-8, Fenoldopam, and Paclitaxel S. No Property Bisphosphonate-8 Fenoldopam Paclitaxel Physicochemical Properties 1 Molecular Formula C 20 H 18 O 8 P 2 C 16 H 16 ClNO 3 C 47 H 51 NO 14 2 Molecular Weight 448.3 305.76 853.91 3 Heavy atoms 30 21 62 4 Aromatic heavy atoms 19 12 18 5 Fraction of sp3 hybridized carbons 0.1 0.25 0.45 6 Rotatable bonds 5 1 15 7 H-bond acceptors/donors 8 − 5 4–4 14 − 4 8 Molar Refractivity 112.1 86.16 218.96 9 TPSA 168.05 72.72 221.29 Lipophilicity 1 iLOGP 0.44 2.26 4.26 2 XLOGP3 1.29 2.39 3.66 3 WLOGP 3.8 2.35 3.41 4 MLOGP 0.99 2.11 1.7 5 Silicos-IT Log P 0.78 2.92 4.59 6 Consensus Log P 1.46 2.41 3.52 Water Solubility 1 ESOL Log S -3.57 -3.6 -6.66 2 ESOL Solubility (mg/ml) 1.20E-01 7.71E-02 1.85E-04 3 ESOL Solubility (mol/l) 2.69E-04 2.52E-04 2.16E-07 4 Solubility class (ESOL) Soluble Soluble Poorly soluble 5 Ali Log S -4.42 -3.56 -8 6 Ali Solubility (mg/ml) 1.71E-02 8.45E-02 8.61E-06 7 Ali Solubility (mol/l) 3.81E-05 2.76E-04 1.01E-08 8 Ali Class Moderately soluble Soluble Poorly soluble 9 Silicos-IT LogSw -4.97 -4.72 -8.8 10 Silicos-IT Solubility (mg/ml) 4.79E-03 5.80E-03 1.34E-06 11 Silicos-IT Solubility (mol/l) 1.07E-05 1.90E-05 1.57E-09 12 Solubility class (Silicos-IT) Moderately soluble Moderately soluble Poorly soluble Pharmacokinetics 1 Gastrointestinal absorption Low High Low 2 BBB permeant No Yes No 3 P-glycoprotein substrate No Yes Yes 4 CYP1A2 inhibitor No No No 5 CYP2C19 inhibitor No No No 6 CYP2C9 inhibitor No No No 7 CYP2D6 inhibitor No Yes No 8 CYP3A4 inhibitor No Yes No 9 log Kp (cm/s) -8.12 -6.47 -8.91 Druglikeness 1 Lipinski violations 0 0 2 2 Ghose/Veber/Egan/Muegge Violations 0-1-1-1 0-0-0-0 3-2-1-3 3 Bioavailability Score 0.55 0.55 0.17 Medicinal Chemistry 1 Pains/Brenk alerts 0–1 1–1 0–1 2 Leadlikeness violations 1 0 3 3 Synthetic Accessibility 4 3.11 8.34 This table presents computationally predicted properties relevant to the drug development potential of the studied compounds. Parameters include: Physicochemical Properties, Lipophilicity, Water Solubility, Pharmacokinetics, Drug-Likeness, and Medicinal Chemistry Predictions obtained using SwissADME server. Bisphosphonate-8 (Molecular Weight [MW] = 448.3 g/mol) exhibited a favourable physicochemical profile, including 5 rotatable bonds, and 8 H-bond acceptors with 5 donors. It demonstrated good predicted water solubility across multiple models (e.g., ESOL LogS = -3.57, classified as "soluble") and a balanced lipophilicity (e.g., Consensus LogP = 1.46). In pharmacokinetic predictions, Bisphosphonate-8 showed low predicted gastrointestinal (GI) absorption, was not predicted to be a P-glycoprotein (P-gp) substrate, and was not predicted to permeate the blood-brain barrier (BBB). Crucially, Bisphosphonate-8 had no Lipinski violations and a good bioavailability score of 0.55. It also showed no PAINS (Pan Assay Interference Compounds) alerts, though one Brenk alert and one lead-likeness violation were noted. Its synthetic accessibility score was 4.0. Fenoldopam (MW = 305.76 g/mol) also showed good predicted solubility (ESOL LogS = -3.60) and a bioavailability score of 0.55. It was predicted to have high GI absorption and to be BBB permeant, but it was also flagged as a P-gp substrate and with one PAINS alert. In stark contrast, Paclitaxel (MW = 853.91 g/mol), the reference drug, displayed several characteristics less favourable for typical oral drug development. It had poor predicted water solubility (ESOL LogS = -6.66), a higher number of rotatable bonds (15), and exhibited 2 Lipinski violations with a significantly lower bioavailability score of 0.17 (Table 4 ). Paclitaxel was also predicted to be a P-gp substrate, though not BBB permeant, and its synthetic accessibility score (8.34) indicated greater synthetic complexity. 4.3 DFT Calculations Reveal Enhanced Stability and Favourable Electronic Properties for Bisphosphonate-8 To further investigate the intrinsic stability and electronic characteristics of the selected ligands, DFT calculations were performed using Gaussian 16 with the B3LYP functional and the 6-311G(d,p) basis set. Key quantum chemical properties, including vibrational frequencies and thermodynamic parameters, are presented in Table 5 . Vibrational frequency analysis confirmed that Bisphosphonate-8 achieved a true energy minimum, as indicated by the absence of imaginary frequencies and a positive lowest frequency of 41.838 cm (Table 5 ). In contrast, Fenoldopam and Paclitaxel exhibited one (-77.31 cm) and three (lowest at -39.177 cm) imaginary frequencies, respectively, suggesting that their DFT-optimized geometries may represent transition states or less stable conformations. Thermodynamic properties calculated at 298.15 K and 1 atm (Table 5 ) further differentiated the ligands. Bisphosphonate-8 exhibited a total Gibbs free energy of -2056.376507 atomic units (au), which was the most favourable among the three isolated ligands, indicating greater intrinsic stability. For instance, its Gibbs free energy correction was − 32.014 kcal/mol. The zero-point energy, enthalpy, and internal energy values also reflected these stability trends (Table 5 ). Table 5 DFT-Calculated Physicochemical Properties of Isolated Ligands After Geometry Optimization Property Bisphosphonate-8 Fenoldopam Paclitaxel Vibrational Analysis Lowest Frequency (cm − 1 ) 41.838 -77.31 -39.177 Number of Imaginary Frequencies 0 1 3 Thermodynamic Properties Zero-Point Energy (kcal/mol) 207.816 187.632 575.654 Enthalpy (kcal/mol) 16.122 10.854 34.183 Total Gibbs Free Energy (au) -2056.376507 -1359.484274 -2928.670783 Gibbs Free Energy (kcal/mol) -32.014 -26.924 -48.784 Internal Energy (kcal/mol) 15.53 10.261 33.591 Heat Capacity (kcal/mol) 101.559 67.62 219.336 Entropy (kcal/mol) 161.448 126.708 278.272 Electronic Properties HOMO Energy (eV) -6.429 -5.894 -6.926 LUMO Energy (eV) -1.721 -2.546 -2.263 HOMO-LUMO Gap (eV) 4.707 3.347 4.665 Solvation Energy (kcal/mol) -201.11 -91.09 -46.79 Total Energies (au) Total Internal Energy (au) -2056.300743 -1359.425015 -2928.539511 Total Enthalpy (au) -2056.299798 -1359.42407 -2928.538567 Total Free Energy (au) -2056.376507 -1359.484274 -2928.670783 This table summarizes key quantum chemical and thermodynamic properties for the isolated ligands Bisphosphonate-8, Fenoldopam, and Paclitaxel, following geometry optimization using Density Functional Theory (DFT). These properties offer insights into the intrinsic stability, reactivity, and behaviour of the ligands. The vibrational analysis includes the lowest vibrational frequency and the number of imaginary frequencies, where zero imaginary frequencies confirm that the optimized structures represent true minima on the potential energy surface. Thermodynamic data include thermal corrections to energy, enthalpy, Gibbs free energy, entropy, zero-point vibrational energy, and heat capacity. Electronic properties such as HOMO and LUMO energies, as well as the HOMO-LUMO energy gap an indicator of chemical reactivity and stability are also reported; a larger gap generally correlates with higher stability. Solvation energies indicate the energetic cost of transferring each molecule from the gas phase to an aqueous environment. Additionally, total energies (in atomic units) including thermal corrections are provided, with the Total Gibbs Free Energy serving as a comparative measure of overall thermodynamic stability. All calculations were performed using Gaussian 16 with the B3LYP functional and 6-311G(d,p) basis set, with thermodynamic properties evaluated at 298.15 K and 1.00 atm. Geometry optimizations converged successfully for all ligands. Analysis of the frontier molecular orbitals (Fig. 3 ) revealed that Bisphosphonate-8 possessed a HOMO-LUMO energy gap of 4.707 electron volts (eV). This was larger than that of Fenoldopam 3.347 eV, indicating greater chemical stability and lower reactivity for Bisphosphonate-8, and comparable to Paclitaxel 4.665 eV. Binding energy calculations for the DFT-optimized ligand-protein complexes (previously detailed in Table 6 ) demonstrated that Bisphosphonate-8 formed the most energetically favourable complex with the tubulin dimer (-0.3156 au). This was followed by Fenoldopam (-0.2202 au), while Paclitaxel showed a slightly positive binding energy (0.1673 au) with the dimer under these quantum-optimized conditions, suggesting a less stable interaction. Overall, the DFT results, encompassing both intrinsic ligand properties (Table 6 ) and complexation energies (Table 6 ), consistently support the enhanced stability and favourable electronic profile of Bisphosphonate-8 (Table 6 , Fig. 5 ). Table 6 DFT-Calculated Binding Energies of Ligand-Tubulin Dimer Complexes After Ligand Geometry Optimization S. No Title Bisphosphonates-8 Fenoldopam Paclitaxel 1 Energy of Complex (Ligand, Active Site) (au) -5915.617549 -3965.591135 -6877.925538 2 Energy of Protein (active site) (au) -3858.645253 -2605.630541 -3948.582412 3 Energy of Ligand (au) -2056.656667 -1359.740377 -2929.510404 4 Binding energy (au) -0.315629 -0.220217 0.167278 This table summarizes the binding energies (au) for each ligand-tubulin dimer complex, calculated after geometry optimization of the isolated ligands using DFT (B3LYP/6-311G(d,p)). The binding energy is calculated as: E binding = E complex - (E protein (active site) + E ligand) . More negative binding energies indicate more favorable complex formation. 4.4 Re - docking of DFT - Optimized Ligands with Tubulin Following DFT optimization, the ligands were re-docked into the tubulin binding site using AutoDock4 (Table 7 ). Bisphosphonate-8 maintained a strong binding affinity (-9.01 kcal/mol and Ki of 249.43 nM), forming six hydrogen bonds with residues Cys12, Asn101, Gly144, Thr145 (twice), and Gly146 (Fig. 6 ). Fenoldopam exhibited a moderate binding affinity (-7.96 kcal/mol and Ki of 1.47 µM), forming four hydrogen bonds (with residues Thr145, Gly146 (2), and Asn206) and two halogen bonds formed (with residues Gly100, Asn101) with Chlorine (Cl) (Fig. 7 ). Paclitaxel showed a binding affinity comparable to Bisphosphonate-8 (-8.65 kcal/mol and Ki of 455.15 nM) but formed only three hydrogen bonds (Fig. 8 ). RMSD values for Bisphosphonate-8, Fenoldopam and Paclitaxel upon redocking were 1.989 Å, 3.842 Å, and 3.843 Å respectively. The torsional free energies upon re-docking were 2.39 kcal/mol, 0.60 kcal/mol and 5.07 kcal/mol respectively, for Bisphosphonate-8, Fenoldopam and Paclitaxel (Fig. 4 ). Table 7 Molecular Docking Parameters of DFT-Optimized Ligands with the Tubulin α-β Dimer S. No Parameter Bisphosphonate-8 Fenoldopam Paclitaxel 1 Estimated Free Energy of Binding (kcal/mol) -9.01 -7.96 -8.65 2 Estimated Inhibition Constant, Ki (µM) 249.43 nM 1.47 µM 455.15 nM 3 Final Intermolecular Energy (kcal/mol) -11.39 -8.56 -13.72 4 vdW + Hbond + Desolvation Energy (kcal/mol) -10.65 -7.77 -13.72 5 Electrostatic Energy (kcal/mol) -0.74 -0.79 0.00 6 Torsional Free Energy (kcal/mol) 2.39 0.60 5.07 7 Internal Energy After Binding (kcal/mol) -1.59 -0.32 -8.71 8 Internal Energy brfore Binding (kcal/mol) -1.59 -0.32 -8.71 9 RMSD from Reference Structure (Å) 1.989 3.842 3.843 10 Interactions Hydrogen bonds (6) Cys 12, Asn 101, Gly 144, Thr 145 (2), Gly 146 Hydrogen bonds (4) Thr 145, Gly 146 (2), Asn 206 Halogen bonds (2) Gly 100, Asn 101 Hydrogen bonds (3) Asn 101, Ser 140, Thr 180 This table presents the outcomes of redocking the DFT-optimized ligand geometries into the tubulin α-β dimer (PDB ID: 7PJF) using AutoDock4. It includes the estimated binding free energy (kcal/mol), estimated inhibition constant (Ki), final intermolecular energy components, torsional free energy, and RMSD from the initial reference docking structure. Internal energy before and after binding was identical for each ligand. Interactions list predominant hydrogen and halogen bonds formed with β-tubulin residues. 4.5 MD Simulations and trajectories Analysis throughout 1µs 4.5.1 System Stability and Fluctuations Analysis Throughout 1µs MD simulations MD simulations (1µs) were performed to analyse the stability and dynamic behaviour of the tubulin-ligand complexes. Bisphosphonate-8 consistently showed the lowest average total and potential energies throughout the simulations (Table 8 ), indicating a more stable complex compared to Fenoldopam and Paclitaxel (Fig. 5 ). The RMSD analysis (Figs. 6 , 7 ) showed that the Bisphosphonate-8 complex remained stable, with an average RMSD of 2.854 Å. Fenoldopam showed moderate fluctuations (average RMSD 6.785 Å), whereas Paclitaxel exhibited significantly larger deviations (average RMSD 25.491 Å), indicating instability. RMSF analysis (Figs. 8 , 9 ) revealed similar trends, with Bisphosphonate-8 exhibiting the lowest fluctuations, particularly for the ligand itself (0.426 Å), confirming stable binding within the pocket. Paclitaxel, in contrast, showed considerably higher RMSF values, both for the protein and the ligand, indicating greater flexibility and a less stable binding mode. These findings are further supported by Table 9 (Figs. 5 – 9 , Tables 8 – 9 ). Table 8 System Energetics of Tubulin-Ligand Complexes During 1 µs MD Simulations. S. No. Parameters/Properties during 1,000 trajectories of 1 µs MDS Bisphosphonates-8 Fenoldopam Paclitaxel 1 Average total energy (kcal/mol) -2,11,019.967 -2,10,831.967 -2,08,231.1758 2 Average potential energy (kcal/mol) -2,57,755.936 -2,57,563.275 -2,54,785.447 3 Degrees of freedom 1,57,464 1,57,449 1,56,851 4 Number of particles 75,307 75,305 75,004 This table summarizes key energetic parameters for the Bisphosphonate-8, Fenoldopam, and Paclitaxel complexes with tubulin, averaged over 1,000 trajectories from 1 µs MD simulations. "Average total energy" reflects the overall stability of the system, while "Average potential energy" provides insight into the stability of the protein-ligand interactions. "Degrees of freedom" and "Number of particles" describe the simulated system size and complexity. All energy values are reported in kcal/mol. Table 9 RMSD and RMSF Metrics for Tubulin-Ligand Complexes During MD Simulations (MDS). S. No. Parameters/Properties during 1,000 trajectories of 1 µs MDS Bisphosphonates-8 Fenoldopam Paclitaxel 1 Average protein-Ligand RMSD (Å): Cα, backbone, sidechain, protein hetero atoms, ligand with respect to protein, ligand with respect to ligand 2.525, 2.543, 3.533, 2.951, 2.854, 0.772 2.6, 2.612, 3.602, 3.023, 6.785, 0.537 2.617, 2.635, 3.621, 3.0531, 28.491, 2.529 2 Average protein RMSF (Å) : Cα, backbone, sidechain, protein hetero atoms 1.051, 1.0759, 1.522, 1.303 1.243, 1.263, 1.689, 1.475 1.045, 1.066, 1.519, 1.296, 3 Average ligand RMSF (Å) : ligand with respect to protein, ligand with respect to ligand 1.1458, 0.426 2.458, 0.312 30.344, 1.537 Legend/Footnote: This table presents RMSD and RMSF values characterizing the structural stability and flexibility of the Bisphosphonate-8, Fenoldopam, and Paclitaxel complexes with tubulin during 1 µs MD simulations. • Average protein-Ligand RMSD (Å): Values represent the average deviation of specific protein components (Cα, backbone, sidechain, protein hetero atoms) and the ligand (calculated with respect to the protein and with respect to its initial docked pose) from the initial structure. Lower RMSD values indicate greater structural stability. • Average protein RMSF (Å): Values represent the average fluctuation of specific protein components (Cα, backbone, sidechain, protein hetero atoms), indicating residue-level flexibility. • Average ligand RMSF (Å): Values represent the average fluctuation of the ligand (calculated with respect to the protein and with respect to its initial docked pose), indicating its flexibility within the binding site. All RMSD and RMSF values are reported in Angstroms (Å). 4.5.2 Interaction Analysis Throughout 1µs MD simulations Analysis of intermolecular interactions during MD simulations (Table 10 ) revealed that the Bisphosphonate-8 complex formed the highest number of total interactions (12,830), including a substantial number of hydrogen bonds (2,709), metallic interactions (5,095), and water bridges (4,543), indicative of a stable and well-defined binding mode. Paclitaxel, despite having some hydrophobic and pi-pi stacking interactions, formed fewer total interactions (3,908) and significantly fewer hydrogen bonds (1,172), which may explain its observed instability. Fenoldopam formed a moderate number of interactions (8,143), but these were not as extensive or persistent as those observed for Bisphosphonate-8. Figure 10 demonstrated that Bisphosphonate-8 and fenoldopam interactions maintained consistently over approximately 30% of the simulation duration. But Paclitaxel maintained interactions consistently over approximately 10% of the simulation duration (Fig. 10 ). Figures 11 and 12 says that Paclitaxel showing lowest number of interactions throughout 1 µs MD simulations (Figs. 10 – 12 ). Figure 14 illustrates the interaction profiles of each ligand with tubulin over the course of the simulation, highlighting the persistent interactions of Bisphosphonate-8 with Asp69 and Thr145 (Figs. 10 – 12 , Table 10 ). Table 10 Summary of Specific Intermolecular Interactions Observed in Tubulin-Ligand Complexes During 1 µs MD Simulations. S. No. Parameters/Properties during 1,000 trajectories of 1 µs MDS Bisphosphonates-8 Fenoldopam Paclitaxel 1 Hydrogen bonds 2,709 1,056 1,172 2 Hydrophobic interactions 456 7 974 3 Ionic interactions 0 29 0 4 Metallic interactions 5,095 4,765 21 5 Pi-cation interactions 0 0 9 6 Pi-pi stacking interactions 27 1 150 7 Water bridge interactions 4,543 2,285 1,582 8 Total number of Interactions 12,830 8,143 3,908 This table quantifies the various types of intermolecular interactions (hydrogen bonds, hydrophobic interactions, ionic interactions, metallic interactions, pi-cation interactions, pi-pi stacking interactions, and water bridge interactions) identified between Bisphosphonate-8, Fenoldopam, and Paclitaxel and the tubulin protein throughout the 1 µs MD simulations. The "Total number of Interactions" represents the sum of all listed interaction types. These values provide insights into the nature and strength of the ligand-protein binding. 4.5.3 Structural Properties and Surface Area Analysis of MD Simulations Analysis of structural properties (Table 11 ) revealed that Bisphosphonate-8 exhibited a more compact structure (radius of gyration, rGyr = 4.75 Å) compared to Paclitaxel (rGyr = 5.55 Å), while Fenoldopam was the most compact (rGyr = 3.58 Å) (Fig. 13 ). Bisphosphonate-8 also showed the highest number of intramolecular hydrogen bonds (intraHB = 0.43), indicative of greater structural rigidity. Paclitaxel possessed the largest molecular surface area (MolSA) and solvent-accessible surface area (SASA), whereas Fenoldopam had the smallest. Bisphosphonate-8 had the largest polar surface area (PSA), suggesting a greater potential for hydrogen bonding interactions (Figs. 13 and 14 , Table 11 ). Table 11 Ligand Structural Properties and Surface Interaction Metrics Derived from 1 µs MDS S. No Ligands rGyr intraHB) MolSA SASA PSA 1 Bisphosphonate-8 4.747053 0.433566 357.3182 127.1727 239.9978 2 Fenoldopam 3.580194 0 265.5735 233.4635 159.4556 3 Paclitaxel 5.547752 0.263736 633.3919 773.1203 219.9332 This table presents key structural and surface interaction properties for Bisphosphonate-8, Fenoldopam, and Paclitaxel, averaged over the 1 µs MD simulations. • rGyr (Å): Radius of Gyration, a measure of molecular compactness. • intraHB: Number of intramolecular hydrogen bonds, indicating ligand rigidity. • MolSA (Ų): Molecular Surface Area, reflecting the overall surface exposure of the ligand. • SASA (Ų): Solvent Accessible Surface Area, indicating the portion of the ligand exposed to the solvent. • PSA (Ų): Polar Surface Area, a descriptor related to drug permeability and bioavailability. These properties provide insights into the ligand's flexibility, compactness, and potential for interaction with the protein and solvent. 4.5.4 RDF Analysis of MD Simulations Trajectories RDF analysis provided further insight into the binding stability of the ligand-protein complexes. Bisphosphonate-8 (Graph A, Fig. 15 ) showed sharp peaks at distances between 4–6 Å, indicating strong and stable interactions with a high degree of occupancy within the binding pocket. Fenoldopam (Graph B, Fig. 15 ) also showed distinct peaks in this region, but with some broadening, indicating moderate stability and some flexibility within the binding site. Paclitaxel (Graph C, Fig. 15 ) displayed broader and less intense peaks, suggesting weaker and more fluctuating interactions. The radial density distribution curves further confirmed these observations, with Bisphosphonate-8 showing the highest density near the protein, indicating tight binding (Figs. 15 ). 4.5.5 Clustering Analysis of MD Trajectories Clustering analysis of the MD trajectories (Table 12 ) identified 12, 15 and 10 distinct clusters for Bisphosphonate-8, Fenoldopam, and Paclitaxel, respectively. Bisphosphonate-8 showed the most populated cluster (Cluster 1) containing 19 frames, with Cα RMSD and ligand RMSD of 2.57 and 3.56 Å, respectively. The Cα RMSD values remained within a narrow range (2.1 to 2.7 Å) and ligand RMSD values also were moderate, indicating relatively stable conformation for Bisphosphonate-8. The Fenoldopam-tubulin complex showed higher conformational variability, forming 16 distinct clusters. The ligand RMSD values across clusters were generally higher than those of Bisphosphonate-8, reaching values up to 9.4 Å (e.g., in a cluster containing 10 frames). Despite some clusters exhibiting low ligand RMSD values, this wider range suggests moderate binding instability and increased conformational flexibility for Fenoldopam, which may affect its binding persistence (Table 12 ). Table 12 Clustering Analysis of Tubulin-Ligand Complex Trajectories from 1 µs MDS Based on Cα and Ligand RMSD. Bisphosphonates-8 Fenoldopam Paclitaxel Cluster No. No. of Frames RMSD No. of Frames RMSD No. of Frames RMSD C Alpha Ligand C Alpha Ligand C Alpha Ligand Cluster 0 / Reference 0.0 0.0 0.0 0.0 0.0 0.0 Cluster 1 19 2.57 3.559 14 2.445 5.774 13 2.872 6.56 Cluster 2 14 2.401 3.157 10 2.522 9.447 12 2.524 36.502 Cluster 3 13 2.684 3.322 10 2.615 6.478 11 2.844 56.645 Cluster 4 11 2.339 3.048 9 2.848 6.868 11 2.353 4.911 Cluster 5 10 2.766 2.528 9 2.505 6.345 10 2.81 45.13 Cluster 6 9 2.325 2.528 8 2.587 6.497 9 2.69 10.488 Cluster 7 5 2.529 2.968 8 2.323 6.301 9 2.805 8.388 Cluster 8 5 2.41 3.438 7 2.455 7.93 9 2.775 9.039 Cluster 9 5 2.689 3.462 7 2.566 7.635 8 2.518 39.584 Cluster 10 3 2.66 3.795 6 2.683 6.36 8 2.545 11.756 Cluster 11 3 2.584 3.099 4 2.411 7.589 Cluster 12 3 2.134 1.87 3 2.581 5.786 Cluster 13 2 2.384 7.012 Cluster 14 2 1.605 5.862 Cluster 15 1 0.87 1.182 Total Frames 100 100 100 This table presents the results of clustering analysis performed on the 1 µs MD simulation trajectories of Bisphosphonate-8, Fenoldopam, and Paclitaxel complexed with tubulin. Clustering was based on the Root Mean Square Deviation (RMSD) of protein Cα atoms and the ligand. "No. of Frames" indicates the population of each cluster. "RMSD C Alpha" and "RMSD Ligand" represent the average RMSD values (in Å) for the protein Cα atoms and the ligand within each cluster, respectively, relative to the reference structure (Cluster 0). This analysis reveals the conformational diversity and dominant binding poses adopted by each ligand during the simulation. In stark contrast, the Paclitaxel-tubulin complex displayed the greatest conformational divergence. While 10 clusters were observed, several exhibited extremely high ligand RMSD values. Notably, Cluster 3 (11 frames) reached a ligand RMSD of over 56 Å, and other significantly populated clusters (Cluster 2 with 12 frames, Cluster 5 with 10 frames, and Cluster 9 with 8 frames) consistently showed ligand RMSD values above 30 Å (36.5 Å, 45.1 Å, and 39.6 Å respectively). Collectively, these highly divergent clusters (2, 3, 5, and 9) accounted for approximately 41% of the simulation frames, indicating that Paclitaxel spent a substantial portion of the simulation time in conformations significantly displaced from its initial binding pose. This strongly suggests significant displacement and instability of Paclitaxel within the tubulin dimer binding site. Throughout these changes, the Cα RMSD values for the protein remained relatively consistent across Paclitaxel's clusters (ranging from 2.3 to 2.9 Å), indicating that the protein itself maintained structural stability while the ligand exhibited substantial instability (Table 12 ). 4.5.6 PCA for Throughout 1µs MD simulations PCA of the MD trajectories (Table 13 ) was performed to identify the principal motions of the tubulin-ligand complexes. Fenoldopam showed the largest variance along PC1 (29.56%), indicating more extensive global conformational changes, whereas Bisphosphonate-8 (19.49%) and Paclitaxel (21.23%) showed lower variance. Bisphosphonate-8 displayed moderate localized fluctuations (PC2 = 12.47%) (Fig. 16 ). Per-residue eigenvector displacement analysis (Fig. 18 ) confirmed that Bisphosphonate-8 induced localized flexibility primarily in a specific region (residue 588). Paclitaxel exhibited substantial residue-specific instability, as indicated by large displacements at residue 846. Free energy landscape (FEL) plots generated from PCA (Fig. 17 ) indicate that approximately 90% of the frames for the Bisphosphonate-8, Fenoldopam, and Paclitaxel complexes with tubulin exhibit free energy values lower than 1, 2, and 5 kcal/mol, respectively. These PCA results align with the findings from RMSD, RMSF, and clustering analyses, further supporting the conclusion that Bisphosphonate-8 displays enhanced stability and a more well-defined binding mode compared to Fenoldopam and Paclitaxel (Figs. 16 – 18 , Table 13 ). Table 13 Principal Component Analysis (PCA) of Tubulin-Ligand Complex Dynamics from 1 µs MDS. S. No PCA Type Bisphosphonates-8 Fenoldopam Paclitaxel 1. PC1 (Global conformational changes) 19.49% 29.56% 21.23% 2. PC2 (Localized fluctuations) 12.47% 11.46% 9.94% 3. PC3 (Cumulative variance) 8.25% 7.2% 8.23% Legend/Footnote: This table summarizes the results of Principal Component Analysis (PCA) applied to the 1 µs MD simulation trajectories of Bisphosphonate-8, Fenoldopam, and Paclitaxel complexed with tubulin. The values represent the percentage of total variance in protein dynamics captured by the first three principal components (PC1, PC2, and PC3). • PC1 (Global conformational changes): Reflects large-scale, collective motions of the protein-ligand complex. • PC2 (Localized fluctuations): Captures more localized, smaller-scale motions. • PC3 (Cumulative variance): Represents the cumulative contribution of the third principal component to the overall variance. This analysis provides insights into the dominant dynamic modes and conformational flexibility of the complexes. Discussion This study employed a comprehensive in silico approach to identify and characterize potential inhibitors of the tubulin α-β dimer, a novel and underexplored target for pancreatic cancer therapy. Our findings consistently highlight Bisphosphonate-8 as a lead candidate, demonstrating superior binding affinity, structural stability, and dynamic behaviour compared to Fenoldopam and the clinically used microtubule-stabilizing agent, Paclitaxel, when interacting with the tubulin dimer. 5.1 Bisphosphonate-8: A Structurally and Dynamically Superior Tubulin Dimer Ligand The initial molecular docking studies revealed Bisphosphonate-8's significantly lower binding free energy and picomolar inhibition constant (Ki) for the tubulin dimer, surpassing both Fenoldopam and Paclitaxel (Table 3 ). This superior affinity is attributed to a robust network of interactions, including key hydrogen bonds with Ser140, Glu183, and Tyr224 within the β-tubulin binding pocket, and a favourable orientation that allows deep penetration into the active site (Fig. 2 ). While Paclitaxel exhibited a reasonable intermolecular energy, its high torsional free energy penalty indicated significant conformational strain upon binding, likely diminishing its effective interaction with the isolated dimer, a finding consistent with its primary mechanism of stabilizing polymerized microtubules [ 65 , 66 ]. The structural stability of Bisphosphonate-8 within the binding site, evidenced by its low RMSD in docking, further supports its potential as a specific dimer-targeting agent. DFT calculations reinforced these findings, demonstrating Bisphosphonate-8's superior electronic and thermodynamic stability (Table 5 , 6 ). The absence of imaginary frequencies confirmed an optimized, stable geometry for Bisphosphonate-8, unlike Fenoldopam and Paclitaxel. Furthermore, its favourable solvation energy suggests better aqueous compatibility, a crucial factor for drug development. The HOMO-LUMO gap analysis (Fig. 3 ) indicated a good balance of chemical stability and reactivity for Bisphosphonate-8, crucial for effective biological interactions. The DFT-calculated binding energies further corroborated the docking results, showing a more energetically favourable complex formation for Bisphosphonate-8 with the tubulin dimer compared to the other compounds. Notably, for Bisphosphonate-8, DFT optimization of the ligand geometry led to a decrease in the estimated binding energy (from − 13.63 kcal/mol to -9.01 kcal/mol), alongside a shift in the predicted inhibition constant from the picomolar to the nanomolar range (Table 7 ). This reduction suggests that the original docking may have overestimated binding affinity due to idealized ligand conformations. The DFT-refined structure, which reflects a more realistic electronic and geometric profile, binds with a slightly reduced but still highly favourable affinity (Fig. 4 ), supporting the ligand’s potential while accounting for conformational stability and the rigor of the computational approach. Even with this refinement, Bisphosphonate-8 maintained a superior interaction profile compared to Paclitaxel. The 1 µs MD simulations provided critical insights into the dynamic behaviour of these complexes (Tables 8 – 11 , Figs. 5 – 12 ). Bisphosphonate-8 consistently formed the most energetically stable complex, exhibiting the lowest RMSD and RMSF values for both the protein and the ligand. This indicates a tightly bound and conformationally stable complex over the simulation period, essential for sustained inhibitory activity (Figs. 6 – 9 ). The extensive network of persistent interactions, including a high number of hydrogen bonds, metallic interactions (likely with the Mg2 + ion), and water bridges, underpins this dynamic stability (Figs. 10 – 12 ). In stark contrast, Paclitaxel displayed significant instability and high ligand RMSD/RMSF values (Figs. 10 – 12 ), suggesting it does not maintain a stable interaction with the unpolymerized tubulin dimer. This observation is pivotal, as it supports the hypothesis that Paclitaxel's efficacy is largely restricted to polymerized microtubules, and it may be ineffective against the dimer, a potentially important target in specific cancer contexts [ 39 ]. Fenoldopam showed intermediate stability but did not match the robust dynamic profile of Bisphosphonate-8. Further dynamic analyses, including RDF (Fig. 15 ), clustering (Table 12 ), and PCA (Table 13 , Figs. 16 – 18 ), consistently favoured Bisphosphonate-8. The sharp RDF peaks for Bisphosphonate-8 indicated a well-defined and persistent binding mode. Clustering analysis revealed that Bisphosphonate-8 predominantly occupied a stable conformational state, while Paclitaxel explored a wide range of unstable conformations. PCA demonstrated that Bisphosphonate-8 induced more restrained global motions and moderate localized fluctuations compared to the extensive and destabilizing movements observed with Paclitaxel, suggesting a more controlled and effective interaction. The pharmacokinetic predictions (Table 4 ) also favoured Bisphosphonate-8 in terms of drug-likeness and fewer predicted liabilities compared to Paclitaxel, although Fenoldopam showed some favourable absorption properties. However, for a targeted therapy, strong and specific binding, as demonstrated by Bisphosphonate-8, is paramount. 5.2 Favourable Physicochemical and Pharmacokinetic Profile of Bisphosphonate-8: Enhancing its Therapeutic Potential Beyond its robust target engagement, the in silico predicted physicochemical and pharmacokinetic properties of Bisphosphonate-8 (Table 4 ) further distinguish it as a promising drug candidate, especially when contrasted with Paclitaxel. Bisphosphonate-8's moderate molecular weight (448.3 g/mol) and fewer rotatable bonds (5) are advantageous compared to Paclitaxel's larger size (853.91 g/mol) and greater flexibility (15 rotatable bonds), generally correlating with better oral bioavailability and membrane permeability [ 67 ]. A key predicted advantage for Bisphosphonate-8 is its good water solubility (e.g., ESOL LogS − 3.57), contrasting sharply with Paclitaxel’s very poor predicted solubility (ESOL LogS − 6.66). This is highly significant, as Paclitaxel's low solubility necessitates complex intravenous formulations often associated with hypersensitivity reactions [ 68 ]. Furthermore, Bisphosphonate-8 is not predicted to be a P-glycoprotein (P-gp) substrate. Paclitaxel, however, is a known P-gp substrate (Table 4 ), and P-gp mediated efflux is a major mechanism of multidrug resistance in cancer [ 69 , 70 ]. Evading P-gp could allow Bisphosphonate-8 to achieve higher effective intracellular concentrations. In terms of drug-likeness, Bisphosphonate-8 adheres to Lipinski's Rule of Five (0 violations) and has a good predicted bioavailability score (0.55). Paclitaxel, conversely, shows 2 Lipinski violations and a much lower bioavailability score (0.17) (Table 4 ), reflecting its established limitations as an oral agent. The absence of PAINS alerts for Bisphosphonate-8 also reduces concerns about non-specific assay interference. While a Brenk alert was noted, this is a flag for future medicinal chemistry optimization [ 71 ]. The moderate synthetic accessibility score for Bisphosphonate-8 (4.0) also suggests greater tractability for synthesis and derivatization compared to the complex natural product structure of Paclitaxel (synthetic accessibility 8.34). Collectively, these ADMET predictions highlight Bisphosphonate-8 as possessing a more favourable drug-like profile than Paclitaxel, enhancing its potential for successful development. 5.3 The Significance of Targeting the Tubulin Dimer in Pancreatic Cancer Pancreatic cancer remains a formidable therapeutic challenge, largely due to late diagnosis and profound chemoresistance [ 72 ]. Existing tubulin-targeting agents, like Paclitaxel, primarily act on polymerized microtubules. However, resistance to these agents can arise through various mechanisms, including mutations in tubulin, altered expression of tubulin isotypes, or changes in microtubule dynamics that reduce drug efficacy [ 73 , 74 ]. Targeting the tubulin α-β dimer represents a potentially novel strategy to circumvent some of these resistance mechanisms and offers a different point of intervention in microtubule regulation. The dimer is the fundamental building block, and its availability and dynamics are crucial for proper microtubule assembly. There is emerging, albeit limited, evidence suggesting that the pool of free tubulin dimers might be dysregulated in certain cancer cells or under conditions of stress and resistance [ 75 , 76 ]. By identifying potent dimer-specific inhibitors like Bisphosphonate-8, it may be possible to disrupt microtubule formation at an earlier stage or target cancer cells that have developed resistance to polymer-targeting drugs. While research on direct tubulin dimer inhibitors is less extensive than on microtubule-stabilizing/destabilizing agents [ 77 ], our in silico findings suggest that Bisphosphonate-8 has the characteristics to effectively engage this form of tubulin. The poor interaction of Paclitaxel with the dimer in our study further highlights the need for and potential of developing dedicated dimer inhibitors. The class of bisphosphonates themselves has been primarily studied for their effects on bone resorption, but some have shown anticancer activities through various mechanisms, including interference with protein prenylation and induction of apoptosis [ 78 , 79 ]. Our study is among the first, to our knowledge, to computationally explore a specific bisphosphonate derivative for direct interaction with the tubulin dimer, suggesting a novel mechanism of action for this chemical scaffold in an oncological context. 5.4 Limitations of the Study This study, while comprehensive in its computational scope, has inherent limitations. The findings are based entirely on in silico predictions, and experimental validation is crucial to confirm the binding affinities, inhibitory activities, and cellular effects of Bisphosphonate-8. Molecular docking and MD simulations rely on force fields and scoring functions that are approximations of complex biological reality. While the OPLS4 force field and extended simulation times (1µs) enhance reliability, discrepancies between computational predictions and experimental outcomes can occur. The solvent model, though explicit, is also a simplification. Furthermore, the ADMET predictions are based on computational models and require experimental verification. Finally, this study focused on the interaction with a single PDB structure of tubulin; variations in tubulin isotypes or post-translational modifications present in pancreatic cancer cells were not accounted for. Conclusion In conclusion, this rigorous multi-scale computational investigation has elucidated the superior binding characteristics and dynamic behaviour of Bisphosphonate-8 upon interaction with the α-β tubulin dimer, establishing it as a highly promising lead candidate. Our findings consistently demonstrate that, compared to Fenoldopam and the conventional microtubule agent Paclitaxel, Bisphosphonate-8 exhibits significantly enhanced binding affinity, a more stable docked pose validated by DFT calculations, and, critically, maintains persistent and stable interactions throughout extensive 1µs MD simulations. Paclitaxel, in stark contrast, displayed marked instability and an inability to effectively engage the tubulin dimer, highlighting a key distinction in their potential mechanisms and target preferences. These structural and dynamic insights, combined with Bisphosphonate-8’s more favourable in silico pharmacokinetic predictions, strongly suggest its potential as a novel, dimer-specific tubulin inhibitor. While this study provides compelling in silico evidence, certain limitations must be acknowledged. The findings are inherently predictive and await experimental validation. The computational models, including force fields and ADMET predictions, are approximations of complex biological systems, and the study focused on a single tubulin crystal structure without considering potential isotype variations or post-translational modifications pertinent to pancreatic cancer. Despite these limitations, this work provides a solid foundation and a compelling rationale for future research. Crucially, future efforts should focus on the experimental validation of Bisphosphonate-8’s predicted activity, encompassing biochemical assays to confirm dimer interaction and cell-based studies to assess its efficacy against pancreatic cancer cell lines. Confirmation of these in silico findings could then pave the way for structural elucidation of the complex and further preclinical development. Ultimately, the successful experimental pursuit of Bisphosphonate-8, guided by these computational insights, holds the potential to yield a novel therapeutic intervention targeting a distinct mechanistic vulnerability in tubulin for the treatment of pancreatic cancer. Declarations Author Contribution Declaration Hemantha Mani Kumar Chakravarthi Chanda a : Executed in silico analyses including molecular docking and molecular dynamics simulations, and contributed to the writing and editing of the manuscript. Sudheer Kumar Katari a : Contributed to the study's conceptualization and design, and conducted in silico analysis, and including target prediction. Affiliations a Department of Bioinformatics, Vignan's Foundation for Science, Technology and Research (Deemed to be University), Vadlamudi, Guntur-522213, India. Corresponding Author Sudheer Kumar Katari Vignan's Foundation for Science, Technology and Research (Deemed to be University), Vadlamudi, Guntur-522213, India. [email protected] Subject Area : Bioinformatics, Computational Biology, Drug Discovery, Structural Biology Funding Declaration The authors declare that no financial support was received for the research, authorship, and / or publication of this article. Conflict of interest The authors declare that there is no conflict of interest. Acknowledgement Authors are highly thankful to VFSTR (Deemed to be University) for providing faculty seed grant (F.No. VFSTR/REG/A6/30/2023-24/01 dated 16-05-2023) facility. The authors acknowledge the use of AI tools, including ChatGPT (OpenAI) and Gemini (Google), for language editing and sentence refinement. These tools were employed solely to enhance the clarity and grammar of the manuscript, and were not used for generating scientific content, analysis, technical writing, or interpretation. Data availability The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request. References Muñoz AR, Chakravarthy D, Gong J et al (2017) Pancreatic Cancer: Current Status and Challenges. Curr Pharmacol Rep 3 Leiphrakpam PD, Chowdhury S, Zhang M et al (2025) Trends in the Global Incidence of Pancreatic Cancer and a Brief Review of its Histologic and Molecular Subtypes. J Gastrointest Cancer 56:71 Yu W, Zhou D, Meng F et al (2025) The global, regional burden of pancreatic cancer and its attributable risk factors from 1990 to 2021. 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Med Res Rev 39 Rogers MJ, Mönkkönen J, Munoz MA (2020) Molecular mechanisms of action of bisphosphonates and new insights into their effects outside the skeleton. Bone 139. https://doi.org/10.1016/j.bone.2020.115493 Santini D, Gentilucci UV, Vincenzi B et al (2003) The antineoplastic role of bisphosphonates: From basic research to clinical evidence. Ann Oncol 14 Additional Declarations No competing interests reported. Supplementary Files GraphicalAbstract.jpg Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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2","display":"","copyAsset":false,"role":"figure","size":328274,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking results (prior to DFT analysis) showing the interaction of selected ligands with tubulin. (A) Bisphosphonate-8, (B) Fenoldopam, and (C) Paclitaxel. Each includes the binding pocket representation and the corresponding 2D interaction diagram illustrating key interactions between the ligand and the tubulin protein.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8016981/v1/e6366a1474f69066c67cdcd4.png"},{"id":95189459,"identity":"e8596357-3bf7-4350-9594-da6e5f5bacb4","added_by":"auto","created_at":"2025-11-05 09:56:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":125845,"visible":true,"origin":"","legend":"\u003cp\u003eFrontier molecular orbitals (HOMO and LUMO) of three ligands obtained from geometry optimization. (A) HOMO of bisphosphonate-8. (B) LUMO of bisphosphonate-8. (C) HOMO of fenoldopam. (D) LUMO of fenoldopam. (E) LUMO of paclitaxel. The energy gaps between the HOMO and LUMO for each ligand are indicated.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8016981/v1/ef6d9aaa39a7d571ca5bdff2.png"},{"id":95227134,"identity":"a2cd6146-6ad2-42c8-99c6-b1edff216c36","added_by":"auto","created_at":"2025-11-05 16:32:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":309256,"visible":true,"origin":"","legend":"\u003cp\u003eMolecular docking results following DFT optimization of the ligands: (A) Bisphosphonate-8, (B) Fenoldopam, and (C) Paclitaxel. Each panel illustrates the optimized ligand bound within the tubulin binding pocket and the corresponding 2D interaction diagram, highlighting changes in binding orientation and molecular interactions post-optimization.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8016981/v1/0c9bfdf9476f68a944b55325.png"},{"id":95228077,"identity":"5130e8d4-3bcd-4696-82f1-53a5010d48a4","added_by":"auto","created_at":"2025-11-05 16:33:21","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":196337,"visible":true,"origin":"","legend":"\u003cp\u003eEnergy plots for three Complexes of ligands with Tubulin throughout 1 µs MD simulations. (A) Plots for Total and Potential energies of Tubulin-Bisphosphonate-8 complex. (B) Plots for Total and Potential energies of Tubulin-Fenoldopam complex. (C) Plots for Total and Potential energies of Tubulin-Paclitaxel complex.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8016981/v1/b9817462844ff3607a9b1c73.png"},{"id":95189463,"identity":"c237a8fb-752d-4748-a515-e2cc243efdd6","added_by":"auto","created_at":"2025-11-05 09:56:04","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":81488,"visible":true,"origin":"","legend":"\u003cp\u003eRMSD plot showing the structural stability of tubulin-ligand complexes over 1 µs molecular dynamics (MD) simulations. The plot represents the RMSD of Cα atoms for tubulin in complex with Bisphosphonate-8, Fenoldopam, and Paclitaxel.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8016981/v1/89adb188c5397ce6d401e101.png"},{"id":95229140,"identity":"233010d0-569e-4a82-92d5-5cf5ff093c2b","added_by":"auto","created_at":"2025-11-05 16:34:29","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":72529,"visible":true,"origin":"","legend":"\u003cp\u003eRMSD of ligands with respect to their corresponding tubulin complexes during 1 µs MD simulations, showing the stability and conformational changes of different tubulin inhibitors (Bisphosphonate-8, Fenoldopam, and Paclitaxel).\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-8016981/v1/db4bec96e85c8b1bb483ec04.png"},{"id":95189476,"identity":"c905d237-0bd7-499c-a62d-aced1d3a6b25","added_by":"auto","created_at":"2025-11-05 09:56:04","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":83684,"visible":true,"origin":"","legend":"\u003cp\u003eRMSF of Cα atoms in tubulin for complexes with different ligands (Bisphosphonate-8, Fenoldopam, and Paclitaxel), indicating residue-level flexibility during the 1 µs MD simulations.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-8016981/v1/f12758e3d83d5f8e78204fff.png"},{"id":95228465,"identity":"95b0a41e-5868-47eb-86e0-55541ef3db1b","added_by":"auto","created_at":"2025-11-05 16:33:47","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":122112,"visible":true,"origin":"","legend":"\u003cp\u003eLigand RMSF plots for three complexes of ligands with Tubulin throughout 1 µs MD simulations. (A) Bisphosphonate-8 RMSF Plot of Tubulin-Bisphosphonate-8 complex. (B) Fenoldopam RMSF Plot of Tubulin-Fenoldopam complex. (C) Paclitaxel RMSF Plot for complex of Tubulin-Paclitaxel.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-8016981/v1/0bc6718ed8fed06f407dd8e8.png"},{"id":95227948,"identity":"7643f548-31ca-4bd2-abaa-4f026229e849","added_by":"auto","created_at":"2025-11-05 16:33:13","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":85811,"visible":true,"origin":"","legend":"\u003cp\u003eTwo-dimensional interaction plots for the three ligand-tubulin complexes at selected frames from the 1 µs MD simulations.\u003c/p\u003e\n\u003cp\u003e(A) 2D interaction plot of the Tubulin-Bisphosphonate-8 complex, with interactions maintained consistently over approximately 30% of the simulation duration.\u003c/p\u003e\n\u003cp\u003e(B) 2D interaction plot of the Tubulin-Fenoldopam complex, showing interactions consistent over approximately 30% of the MD\u003cem\u003e \u003c/em\u003esimulations.\u003c/p\u003e\n\u003cp\u003e(C) 2D interaction plot of the Tubulin-Paclitaxel complex, with interactions persistent for about 10% of the total simulation time.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-8016981/v1/22fadf8069c3caf426cf6410.png"},{"id":95189472,"identity":"04916ef0-1cf8-43d7-93da-78db159b3ab8","added_by":"auto","created_at":"2025-11-05 09:56:04","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":178250,"visible":true,"origin":"","legend":"\u003cp\u003eHistogram of contacts for three complexes of ligands with Tubulin throughout 1 µs MD simulations. This represents all residues of protein which are involved in interactions with ligands (A) Histogram of contacts for Tubulin-Bisphosphonate-8 complex. (B) Histogram of contacts for Tubulin-Fenoldopam complex. (C) Histogram of contacts for Tubulin-Paclitaxel complex.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-8016981/v1/28ed4f55fbf8dc4faa64da23.png"},{"id":95227009,"identity":"3a76a37a-c191-49db-8ac3-e0b74b531484","added_by":"auto","created_at":"2025-11-05 16:32:00","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":112421,"visible":true,"origin":"","legend":"\u003cp\u003eTotal number of contacts between tubulin and each ligand over a 1 µs MD simulation. The plots illustrate all interactions formed between the ligands and tubulin throughout the simulation timeframe. (A) Tubulin-Bisphosphonate-8 complex. (B) Tubulin-Fenoldopam complex. (C) Tubulin-Paclitaxel complex.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-8016981/v1/d8b1861552f9ecc59fb49748.png"},{"id":95226767,"identity":"ea7246c0-4c4a-422d-aa8f-d661f35e25cd","added_by":"auto","created_at":"2025-11-05 16:31:43","extension":"png","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":112329,"visible":true,"origin":"","legend":"\u003cp\u003erGyr of ligands in complexes with tubulin, showing the compactness and structural stability of different tubulin inhibitors (Bisphosphonate-8, Fenoldopam, and Paclitaxel) during 1 µs MD simulations.\u003c/p\u003e","description":"","filename":"13.png","url":"https://assets-eu.researchsquare.com/files/rs-8016981/v1/66fb8b556c111720569fe8b2.png"},{"id":95189481,"identity":"473c331e-b69d-4f26-8177-28f722c16d45","added_by":"auto","created_at":"2025-11-05 09:56:04","extension":"png","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":280062,"visible":true,"origin":"","legend":"\u003cp\u003eThis figure represents Intramolecular Hydrogen Bonds (intraHB), Molecular Surface Area (MolSA), Solvent Accessible Surface Area (SASA), Polar Surface Area (PSA) plots for selected three complexes of ligands with Tubulin throughout 1 µs MD simulations. (A) intraHB, MolSA, SASA, PSA plots for Tubulin-Bisphosphonate-8 complex. (B) intraHB, MolSA, SASA, PSA plots for Tubulin-Fenoldopam complex. (C) intraHB, MolSA, SASA, PSA plots for Tubulin-Paclitaxel complex.\u003c/p\u003e","description":"","filename":"14.png","url":"https://assets-eu.researchsquare.com/files/rs-8016981/v1/34253ca24309ecfaa06ea62a.png"},{"id":95189477,"identity":"8ee161d6-63da-43ab-8bfb-77feb2db7fbe","added_by":"auto","created_at":"2025-11-05 09:56:04","extension":"png","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":86697,"visible":true,"origin":"","legend":"\u003cp\u003eRadial distribution function (RDF) plots for selected three complexes of ligands with Tubulin from 1 µs MD simulations. (A) RDF plot for Tubulin-Bisphosphonate-8 complex from 1 µs MD simulations. (B) RDF plot for Tubulin-Fenoldopam complex from 1 µs MD simulations. (C) RDF plot for Tubulin-Paclitaxel complex from 1 µs MD simulations.\u003c/p\u003e","description":"","filename":"15.png","url":"https://assets-eu.researchsquare.com/files/rs-8016981/v1/8af5ff25c54384baf540d2a5.png"},{"id":95228539,"identity":"ebfa4850-45dc-4391-9dbd-c7e26a4e1c56","added_by":"auto","created_at":"2025-11-05 16:33:54","extension":"png","order_by":16,"title":"Figure 16","display":"","copyAsset":false,"role":"figure","size":170483,"visible":true,"origin":"","legend":"\u003cp\u003ePCA plot derived from 1 µs molecular dynamics simulation of the tubulin-ligand complexes. The plot illustrates the conformational sampling and major collective motions of the protein-ligand system across the principal components. (A) PCA plot for Tubulin-Bisphosphonate-8 complex of 1 µs MD simulations. (B) PCA plot for Tubulin-Fenoldopam complex of 1 µs MD simulations. (C) PCA plot for Tubulin-Paclitaxel complex of 1 µs MD simulations.\u003c/p\u003e","description":"","filename":"16.png","url":"https://assets-eu.researchsquare.com/files/rs-8016981/v1/66b5686f8bb4405ba08081f5.png"},{"id":95189502,"identity":"88227300-b00a-43c5-9911-97bd300611a0","added_by":"auto","created_at":"2025-11-05 09:56:05","extension":"png","order_by":17,"title":"Figure 17","display":"","copyAsset":false,"role":"figure","size":417449,"visible":true,"origin":"","legend":"\u003cp\u003e3D and 2D FEL plots derived from 1 µs MD simulations of tubulin-ligand complexes. The plot illustrates the conformational energy states of the system, highlighting the most stable and frequently sampled conformations during the simulation. (A) FEL plot for Tubulin-Bisphosphonate-8 complex of 1 µs MD simulations. (B) FEL plot for Tubulin-Fenoldopam complex of 1 µs MD simulations. (C) FEL plot for Tubulin-Paclitaxel complex of 1 µs MD simulations.\u003c/p\u003e","description":"","filename":"17.png","url":"https://assets-eu.researchsquare.com/files/rs-8016981/v1/31dff65cccf9b964e8d0f52d.png"},{"id":95229192,"identity":"994f3597-468d-47ee-9218-e26d54648076","added_by":"auto","created_at":"2025-11-05 16:34:35","extension":"png","order_by":18,"title":"Figure 18","display":"","copyAsset":false,"role":"figure","size":153932,"visible":true,"origin":"","legend":"\u003cp\u003eEigenvector displacement per residue for principal components PC1 and PC2 of the tubulin–ligand complexes. The plot shows the displacement of each residue in the protein complex along the first two principal components, providing insight into the conformational dynamics and flexibility of the system during the 1 µs molecular dynamics simulation. (A) Tubulin-Bisphosphonate-8 complex of 1 µs MD simulations. (B) Tubulin-Fenoldopam complex of 1 µs MD simulations. (C) Tubulin-Paclitaxel complex of 1 µs MD simulations.\u003c/p\u003e","description":"","filename":"18.png","url":"https://assets-eu.researchsquare.com/files/rs-8016981/v1/d660f90537895c604f33fa6f.png"},{"id":95312346,"identity":"9c80b735-cf68-47e7-a52f-d9989a042c46","added_by":"auto","created_at":"2025-11-06 15:48:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5956147,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8016981/v1/b3f8445e-e7f8-4725-8e71-4f4c919db2dd.pdf"},{"id":95189467,"identity":"102105ae-09b7-4f9b-b3cc-f5f26c25df09","added_by":"auto","created_at":"2025-11-05 09:56:04","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2065007,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8016981/v1/db15252ed081191200084525.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of Novel Tubulin Dimer Inhibitors for Pancreatic Cancer: An Integrated Computational Approach","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePancreatic cancer remains one of the most challenging malignancies in oncology, characterized by aggressive progression, poor prognosis, and a dismal five-year survival rate. The urgent need for new therapeutic strategies is underscored by the alarming statistics: in 2021 alone, over half a million new cases were diagnosed globally, with a similar number of deaths attributed to the disease [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These figures highlight the increasing incidence and exceptionally high mortality rates, often exceeding 90% in both males and females [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This grim reality positions pancreatic cancer as a leading cause of cancer-related deaths worldwide, emphasizing the critical need for more effective therapeutic interventions [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The consistently high mortality across different reporting periods and geographical locations further underscores the limitations of current therapies. The rising incidence further amplifies the urgency for ground-breaking approaches to combat this devastating disease [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e outlines worldwide data for pancreatic cancer from 2017 to 2022, focusing on new cases, mortality, and age-standardized incidence and death rates. The data reveals an upward trend in both incidence and deaths over the years, solidifying pancreatic cancer as one of the deadliest cancers globally. The age-standardized incidence and death rates reflect the substantial impact of this disease across diverse populations, with the highest numbers observed in 2021. This data underscores the urgent need for effective treatments and interventions for pancreatic cancer, which continues to pose a significant challenge to global healthcare systems. Notably, while data for 2022 shows some improvements in mortality rates, the overall impact remains devastating [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eGlobal Incidence and Mortality Statistics for Pancreatic Cancer (2017\u0026ndash;2022)\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=\"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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNew Cases (Global)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDeaths (Global)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAge-Standardized Incidence Rate (per 100,000)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAge-Standardized Mortality Rate (per 100,000)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e447,665\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e441,083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e58.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e57.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e489,862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e486,869\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e6.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e495,773\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e466,003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e508,532\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e505,752\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2022\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e510,992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eN/A\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eThis table presents the global data on new cases, deaths, age-standardized incidence, and mortality rates for pancreatic cancer over recent years. The data highlights the increasing trend of pancreatic cancer cases and deaths, emphasizing the severity of the disease and its growing global burden. The age-standardized incidence and mortality rates provide insights into the impact of pancreatic cancer, adjusted for population age structures across different regions.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eDespite progress in cancer research and treatment, options for pancreatic cancer remain severely limited, primarily due to late-stage diagnosis and the development of chemotherapy resistance [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Surgical resection, often followed by adjuvant chemotherapy, is currently the only treatment offering a potential cure [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, this option is available to fewer than 20% of patients, as the majority are diagnosed with advanced-stage disease, precluding surgical intervention [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Even for those who undergo surgery, the five-year survival rate remains below 10%, indicating a high probability of recurrence. Moreover, surgical procedures like the Whipple procedure carry significant morbidity risks [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The overall survival rate for pancreatic cancer stands at a meager 5%, even among patients with initially resectable tumours [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A considerable percentage of patients receive no active treatment, highlighting the inadequacy of current approaches for managing this aggressive disease [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Tumour size, invasion into blood vessels, and lymph node metastasis are associated with poorer outcomes, underscoring the complex and aggressive nature of pancreatic cancer [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This challenging therapeutic landscape necessitates the urgent exploration of novel and targeted therapies to improve patient outcomes.\u003c/p\u003e\u003cp\u003eA promising avenue for therapeutic intervention lies in disrupting the function of the tubulin α-β dimer, a critical component of microtubules [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Microtubules, dynamic structures composed of α- and β-tubulin heterodimers, play essential roles in various cellular processes, including cell movement, intracellular transport, and mitosis. Given the rapid proliferation of cancer cells, the microtubule network, and specifically the tubulin dimer, is a compelling therapeutic target [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Tubulin-binding chemotherapeutic agents have long been recognized for their ability to disrupt the dynamics of the mitotic spindle, leading to mitotic arrest and subsequent cell death in rapidly dividing cancer cells [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Alterations in tubulin dimer dynamics, including changes in stability, isotype expression, and post-translational modifications, have been observed in various cancers, often correlating with poor prognosis and chemoresistance [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Recent studies suggest that these alterations in dimer dynamics may contribute to drug resistance by modifying the drug binding affinity at the tubulin interface, promoting microtubule destabilization that reduces drug efficacy, and altering tubulin post-translational modifications that interfere with chemotherapeutic targeting [\u003cspan additionalcitationids=\"CR48 CR49\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Targeting the α-β tubulin dimer specifically offers the potential to circumvent resistance mechanisms associated with targeting polymerized microtubules, as the dimer may be more abundant or accessible in cancer cells exhibiting microtubule destabilization or resistance phenotypes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This rationale strongly supports the investigation of molecules that directly stabilize or inhibit the tubulin dimer, potentially offering improved therapeutic outcomes in drug-resistant pancreatic cancer.\u003c/p\u003e\u003cp\u003eAmong these, Paclitaxel remains one of the most widely used microtubule-stabilizing agents (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e); however, it often exhibits suboptimal stability and binding affinity when interacting with the isolated tubulin heterodimer, potentially limiting its efficacy in cases of microtubule destabilization or drug resistance [\u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33 CR34 CR35 CR36 CR37\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. This highlights a critical unmet need for agents that effectively target the unassembled tubulin dimer with enhanced stability and interaction dynamics [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Furthermore, the potential of non-traditional tubulin-targeting compounds, such as Bisphosphonate-8 and Fenoldopam, has not been thoroughly explored in this context (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eOverview of Selected Clinically Used Microtubule-Targeting Agents (MTAs), Their Mechanisms, and Target Cancers\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\u003eDrug Name\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMechanism of Action\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTarget Binding Site\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eExamples of Cancers Treated\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePaclitaxel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMicrotubule stabilizer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTaxane\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOvarian, breast, lung, pancreatic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDocetaxel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMicrotubule stabilizer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTaxane\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBreast, prostate, lung, gastric, head and neck\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVincristine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMicrotubule destabilizer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVinca\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLeukemia, lymphoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVinblastine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMicrotubule destabilizer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVinca\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLymphoma, Hodgkin's disease, testicular cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCabazitaxel\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMicrotubule stabilizer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTaxane\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMetastatic castration-resistant prostate cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEribulin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMicrotubule destabilizer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-taxane\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMetastatic breast cancer, metastatic or unresectable liposarcoma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIxabepilone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMicrotubule stabilizer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEpothilone\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMetastatic or locally advanced breast cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrastuzumab Emtansine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAntibody-drug conjugate (MTI)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTubulin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHER2-overexpressing breast cancer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eThis table provides a summary of prominent microtubule-targeting agents (MTAs) used in oncology. It details their primary mechanism of action (microtubule stabilizer or destabilizer), their general target binding site on tubulin (e.g., Taxane site, Vinca site), and examples of cancers for which they are clinically indicated. This provides context for the established therapeutic strategy of targeting tubulin.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThis research addresses these gaps by employing an integrated computational approach to investigate the stability, binding affinity, and molecular dynamics of novel and repurposed compounds, identified through DrugBank screening, with the tubulin α-β dimer. Paclitaxel serves as a clinically relevant control due to its widespread use in pancreatic cancer therapy and its well-characterized mechanism of (primarily polymerized) microtubule stabilization, providing a benchmark for comparing the novel dimer-targeting agents. Bisphosphonate-8 and Fenoldopam were selected based on preliminary screening data suggesting potential tubulin-binding activity and their unique chemical structures, offering potential for repurposing or development as alternative tubulin inhibitors. Our computational investigation utilizes molecular docking, density functional theory (DFT), molecular dynamics (MD) simulations, and principal component analysis (PCA) to thoroughly evaluate the interactions of these compounds with the tubulin dimer. The primary objective is to provide \u003cem\u003ein silico\u003c/em\u003e evidence supporting the potential of Bisphosphonate-8 as a promising tubulin dimer inhibitor for pancreatic cancer, warranting further experimental validation and preclinical evaluation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis study investigated the potential of Bisphosphonate-8 as a novel tubulin dimer inhibitor for pancreatic cancer therapy using a comprehensive \u003cem\u003ein silico\u003c/em\u003e approach. The methodology integrated target identification, molecular docking, DFT calculations, optimized docking, MD simulations, and PCA. Paclitaxel, a clinically established microtubule stabilizer used in cancer therapy, served as a control due to its well-characterized mechanism of action and clinical relevance in pancreatic cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Target Identification and Control Selection for Pancreatic Cancer\u003c/h2\u003e\u003cp\u003eTubulin, a critical protein involved in cell division, was selected as the primary therapeutic target due to its essential role in cellular proliferation and established relevance in cancer treatment [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Specifically, the α-β tubulin dimer, the building block of microtubules, was targeted in this study. Microtubules, polymers of α- and β-tubulin, form the mitotic spindle, crucial for chromosome segregation during cell division [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Disrupting microtubule dynamics through dimer stabilization or inhibition can lead to mitotic arrest and apoptosis in rapidly dividing cancer cells. Paclitaxel, a member of the taxane family and a clinically used chemotherapeutic agent for various solid tumours, including pancreatic cancer [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], served as the control. Paclitaxel stabilizes microtubules by binding to β-tubulin, preventing depolymerization, which leads to cell cycle arrest at the G2/M phase and subsequent apoptosis [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. While Paclitaxel's primary interaction is with polymerized microtubules, its clinical significance and well-understood mechanism make it a relevant benchmark for comparing novel tubulin dimer inhibitors. Bisphosphonate-8 and Fenoldopam were selected as potential dimer inhibitors based on preliminary \u003cem\u003ein silico\u003c/em\u003e screening and their distinct chemical structures, suggesting potential for novel mechanisms of action.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Screening With Databases Based on Molecular Docking Using AutoDock Vina\u003c/h2\u003e\u003cp\u003eThe high-resolution crystal structure of the tubulin α-β heterodimer (PDB ID: 7PJF) was retrieved from the Protein Data Bank (PDB). Both the protein and ligands (Bisphosphonate-8, Fenoldopam, and Paclitaxel) obtained from the DrugBank database were prepared for docking using Python Molecular Viewer (PMV) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Water molecules were removed, hydrogen atoms were added, and Kollman and Gasteiger charges were assigned to ensure accurate representation of electrostatic interactions [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. A docking grid encompassing the known Paclitaxel binding site on β-tubulin [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] was defined with dimensions of 60 \u0026Aring; x 60 \u0026Aring; x 60 \u0026Aring;, centred at coordinates X\u0026thinsp;=\u0026thinsp;148.46, Y\u0026thinsp;=\u0026thinsp;7.17, and Z\u0026thinsp;=\u0026thinsp;46.73. Initial docking and virtual screening were performed using AutoDock Vina (version 1.1.2) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] with an exhaustiveness parameter of 32 for independent runs. The top two scoring compounds from the DrugBank screen, along with Paclitaxel, were then subjected to more precise docking using AutoDock4 (version 4.2.6) [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. AutoDock4, with its more sophisticated search algorithm and energy function, was employed with an exhaustiveness of 32 and 100 of runs to refine the binding poses and calculate more accurate binding energies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e3.3. DFT Optimization of Ligands and Binding Affinity Calculations\u003c/h2\u003e\u003cp\u003eTo gain deeper insights into the nature of ligand-tubulin interactions at the quantum mechanical level DFT calculations were performed using Gaussian version 16 [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. The B3LYP hybrid functional, known for its good performance in describing non-covalent interactions, was employed in conjunction with the 6-311G(d,p) basis set, which includes polarization functions for both hydrogen and heavy atoms, enhancing accuracy [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Geometry optimizations and frequency calculations were performed for the individual ligands, the tubulin residues within 2.5 \u0026Aring; of the predicted binding site, and the ligand-protein complexes. The binding energy (EB) for each complex was calculated as: EB\u0026thinsp;=\u0026thinsp;EC - (EP\u0026thinsp;+\u0026thinsp;EL), where EC, EP, and EL represent the energies of the complex, protein, and ligand, respectively. This analysis provided a detailed assessment of the energetic contributions, including electrostatic interactions, hydrogen bonding, and van der Waals forces, to the ligand-protein binding.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Optimized Ligands Docking Through AutoDock4\u003c/h2\u003e\u003cp\u003eFollowing DFT optimization of ligand geometries, redocking was performed using AutoDock4 (parameters as described in 3.2) to evaluate the impact of geometry refinement on binding affinity and pose. This step ensured that the docking results accurately reflected the ligands' preferred conformations [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.5. MD Simulations for Complexes of Tubulin with Optimized Ligands Using Desmond\u003c/h2\u003e\u003cp\u003eMD simulations were performed using Desmond (version 2021-4, Schr\u0026ouml;dinger, 2023.2) [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] to assess the stability and dynamic behaviour of the tubulin-ligand complexes in a simulated physiological environment. The OPLS4 force field [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] was used to parameterize the systems. The optimized ligand structures and Paclitaxel were prepared using the System Builder tool in Maestro (Schr\u0026ouml;dinger). The protein was prepared using the Protein Preparation Wizard, removing unnecessary components and performing energy minimization [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Each complex was solvated in an orthorhombic box with the SPC, extending 10 \u0026Aring; beyond the protein in all directions. Sodium (Na⁺) and chloride (Cl⁻) ions were added to neutralize the system and mimic a physiological salt concentration of 0.15 M. The systems were energy minimized using the steepest descent algorithm and equilibrated in two phases: NVT (constant number of particles, volume, and temperature) for 500 picoseconds (ps) followed by NPT (constant number of particles, pressure, and temperature) for 1 nanosecond (ns) at 300 K and 1 bar pressure to ensure system stability. A 2 femtoseconds (fs) time step was used, and trajectories were saved every 1 ps. Periodic boundary conditions and the Particle Mesh Ewald (PME) method were employed to handle long-range electrostatic interactions [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Trajectories were analyzed for root mean square deviation (RMSD) and root mean square fluctuation (RMSF) of the protein backbone and ligand atoms, as well as for persistent intermolecular interactions (hydrogen bonds, van der Waals, metallic, electrostatic) [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.6. MD Simulations Trajectory Analysis\u003c/h2\u003e\u003cp\u003e\u003cstrong\u003eClustering\u003c/strong\u003e\u003cp\u003eThe MD simulations trajectories were clustered using the Desmond Trajectory Clustering algorithm implemented in Desmond based on the protein C-α RMSD matrix, using a cut-off of 10 to identify the dominant conformational states of the tubulin-ligand complexes. Clustering analysis Opens the RMSD Based Clustering of Frames from Desmond Trajectory panel.[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eRadial Distribution Function (RDF) Analysis\u003c/strong\u003e\u003cp\u003eRDF analysis was performed to characterize the distribution of ligand atoms and residues in chain B of tubulin and identify persistent interactions during the simulation using RDF tool in Desmond software. RDFs were calculated for ligand atoms and residues in chain B of tubulin to determine the average distances and fluctuations of these interactions [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e].\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePCA for MD Simulations Trajectories\u003c/strong\u003e\u003cp\u003ePCA was performed using the Bio3D package in R programming language [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] to identify the dominant conformational changes and dynamic flexibility of the tubulin α-β dimer upon ligand binding (Bisphosphonate-8, Fenoldopam, and Paclitaxel). MD simulations trajectories from 1 microsecond (\u0026micro;s) simulations, with frames saved every 10 ps, were used for this analysis. The trajectories were first aligned to the initial reference structure, and a covariance matrix of the positional fluctuations of all Cα atoms was constructed to capture correlated and anticorrelated motions. Eigenvectors and eigenvalues were then computed, with eigenvectors representing the direction of atomic motion and eigenvalues quantifying the magnitude of those motions. PCA was also extended to include ligand atoms within the binding pocket to assess their flexibility and interaction stability. The first three principal components (PC1-PC3), which captured the most significant collective motions, were analyzed, with particular focus on the first two for projecting conformational space and comparing the apo and holo forms. This approach enabled visualization of ligand-induced shifts in protein dynamics and provided insights into the structural adaptability of the tubulin-ligand complexes [\u003cspan additionalcitationids=\"CR62 CR63\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e].\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e4.1 Molecular Docking, Interactions and Binding Site Analysis\u003c/h2\u003e\n \u003cp\u003eMolecular docking was performed to assess the binding affinity and characterize the interactions of Bisphosphonate-8, Fenoldopam, and Paclitaxel with the tubulin \u0026alpha;-\u0026beta; dimer. Initial screening using AutoDock Vina identified Bisphosphonate-8 and Fenoldopam as potential tubulin inhibitors, exhibiting docking scores of -13.224 and \u0026minus;\u0026thinsp;12.339 kcal/mol, respectively, compared to -4.718 kcal/mol for Paclitaxel. Subsequent docking with AutoDock4 (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) provided a more refined assessment of binding energies. Bisphosphonate-8 displayed the most favourable binding free energy (-13.63 kcal/mol) and a remarkably low inhibition constant (Ki\u0026thinsp;=\u0026thinsp;102.26 picomolar [pM]), indicating high binding potency. Fenoldopam exhibited a less favourable binding free energy (-8.63 kcal/mol) and a Ki of 468.90 nanomolar (nM), while Paclitaxel showed the weakest binding (-6.19 kcal/mol and a Ki of 29.05 micromolar [\u0026micro;M]). Decomposition of the binding free energy revealed that van der Waals interactions and desolvation energy were the primary driving forces for binding in all three complexes, with Bisphosphonate-8 demonstrating the strongest contributions. Electrostatic interactions were negligible for Bisphosphonate-8 and Paclitaxel, while Fenoldopam exhibited a slight negative electrostatic contribution (-1.28 kcal/mol). Bisphosphonate-8 and Fenoldopam maintained low RMSD values (1.409 \u0026Aring; and 1.594 \u0026Aring;, respectively) relative to the initial docked pose, indicating conformational stability within the binding site. In contrast, Paclitaxel displayed a higher RMSD (4.664 \u0026Aring;), suggesting a less stable binding conformation (Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparative Molecular Docking Parameters of Bisphosphonate-8, Fenoldopam, and Paclitaxel with the Tubulin \u0026alpha;-\u0026beta; Dimer\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS. No\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBisphosphonates-8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFenoldopam\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePaclitaxel\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimated Free Energy of Binding (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimated Inhibition Constant, Ki\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e102.26 pM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e468.90 nM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29.05 \u0026micro;M\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinal Intermolecular Energy (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-16.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003evdW\u0026thinsp;+\u0026thinsp;Hbond\u0026thinsp;+\u0026thinsp;Desolvation Energy (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-16.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eElectrostatic Energy (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTorsional Free Energy (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternal Energy After Binding (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternal Energy brfore Binding (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRMSD from Reference Structure (\u0026Aring;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.664\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteractions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrogen bonds (3)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSer 140, Glu 183, Tyr 224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrogen bonds (2)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eGln 11, Glu 183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrogen bonds (3)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAsn 101, Glu 183, Asn 206\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMetal bonds (1)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMg 502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eThis table summarizes the predicted binding affinities and interaction parameters from AutoDock4 calculations for the studied ligands with the tubulin \u0026alpha;-\u0026beta; dimer (PDB ID: 7PJF). Lower estimated binding free energy (kcal/mol) and estimated inhibition constant (Ki) values indicate stronger predicted binding. Energy components (intermolecular, vdW\u0026thinsp;+\u0026thinsp;Hbond\u0026thinsp;+\u0026thinsp;Desolvation, electrostatic, torsional) provide insights into the forces driving complex formation and ligand strain.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u0026bull; Internal energy before and after binding was identical for each ligand, indicating no significant induced conformational strain upon binding.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u0026bull; RMSD (Root Mean Square Deviation) of ligand heavy atoms from the reference docked pose, indicating conformational stability.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u0026bull; Predominant hydrogen bond interactions are listed; \u0026quot;Metal bonds\u0026quot; refers to coordination with Mg-502.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eAnalysis of the docking poses (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e) revealed key interactions between the ligands and the tubulin dimer. Bisphosphonate-8 formed three hydrogen bonds with Ser140, Glu183, and Tyr224 within the \u0026beta;-tubulin binding pocket. Fenoldopam formed two hydrogen bonds with Gln11 and Glu183 (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Paclitaxel interacted with Asn101, Glu183, and Asn206 via hydrogen bonds and formed a metal coordination bond with Mg502 (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). The more extensive hydrogen bonding network observed for Bisphosphonate-8 likely contributes to its higher binding affinity compared to Fenoldopam and Paclitaxel. All three ligands interacted exclusively with the \u0026beta;-tubulin monomer. Bisphosphonate-8 penetrated deeply into the binding pocket, exhibiting a close interaction with the magnesium ion present in the active site. Fenoldopam also exhibited a good fit within the pocket, whereas Paclitaxel showed a less optimal fit, consistent with its higher RMSD and weaker binding energy (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e4.2 \u003cstrong\u003eIn Silico\u003c/strong\u003e \u003cstrong\u003ePhysicochemical and Pharmacokinetic Profiling Highlights Bisphosphonate\u003c/strong\u003e-\u003cstrong\u003e8\u0026apos;s Favourable Drug-Like Properties\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eTo evaluate the drug development potential of the identified compounds, key physicochemical characteristics, lipophilicity, water solubility, pharmacokinetic properties (ADMET), drug-likeness, and medicinal chemistry attributes were computationally predicted, with detailed parameters presented in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePredicted Physicochemical, ADMET, and Drug-Likeness Properties of Bisphosphonate-8, Fenoldopam, and Paclitaxel\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS. No\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProperty\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBisphosphonate-8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFenoldopam\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePaclitaxel\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003ePhysicochemical Properties\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMolecular Formula\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003csub\u003e20\u003c/sub\u003eH\u003csub\u003e18\u003c/sub\u003eO\u003csub\u003e8\u003c/sub\u003eP\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003csub\u003e16\u003c/sub\u003eH\u003csub\u003e16\u003c/sub\u003eClNO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eC\u003csub\u003e47\u003c/sub\u003eH\u003csub\u003e51\u003c/sub\u003eNO\u003csub\u003e14\u003c/sub\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMolecular Weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e448.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e305.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e853.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeavy atoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAromatic heavy atoms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFraction of sp3 hybridized carbons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRotatable bonds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eH-bond acceptors/donors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026thinsp;\u0026minus;\u0026thinsp;5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u0026ndash;4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u0026thinsp;\u0026minus;\u0026thinsp;4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMolar Refractivity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e218.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTPSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e168.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e72.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e221.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eLipophilicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eiLOGP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXLOGP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWLOGP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMLOGP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSilicos-IT Log P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConsensus Log P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eWater Solubility\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eESOL Log S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.66\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eESOL Solubility (mg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.20E-01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.71E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.85E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eESOL Solubility (mol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.69E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.52E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.16E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSolubility class (ESOL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoluble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoluble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoorly soluble\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAli Log S\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAli Solubility (mg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.71E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.45E-02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.61E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAli Solubility (mol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.81E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.76E-04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAli Class\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerately soluble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSoluble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoorly soluble\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSilicos-IT LogSw\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-4.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSilicos-IT Solubility (mg/ml)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.79E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.80E-03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSilicos-IT Solubility (mol/l)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.90E-05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.57E-09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSolubility class (Silicos-IT)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerately soluble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eModerately soluble\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoorly soluble\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003ePharmacokinetics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGastrointestinal absorption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBBB permeant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-glycoprotein substrate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCYP1A2 inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCYP2C19 inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCYP2C9 inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCYP2D6 inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCYP3A4 inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elog Kp (cm/s)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eDruglikeness\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLipinski violations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGhose/Veber/Egan/Muegge Violations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0-1-1-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0-0-0-0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3-2-1-3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBioavailability Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedicinal Chemistry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePains/Brenk alerts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u0026ndash;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u0026ndash;1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeadlikeness violations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSynthetic Accessibility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eThis table presents computationally predicted properties relevant to the drug development potential of the studied compounds. Parameters include: Physicochemical Properties, Lipophilicity, Water Solubility, Pharmacokinetics, Drug-Likeness, and Medicinal Chemistry Predictions obtained using SwissADME server.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eBisphosphonate-8 (Molecular Weight [MW]\u0026thinsp;=\u0026thinsp;448.3 g/mol) exhibited a favourable physicochemical profile, including 5 rotatable bonds, and 8 H-bond acceptors with 5 donors. It demonstrated good predicted water solubility across multiple models (e.g., ESOL LogS = -3.57, classified as \u0026quot;soluble\u0026quot;) and a balanced lipophilicity (e.g., Consensus LogP\u0026thinsp;=\u0026thinsp;1.46). In pharmacokinetic predictions, Bisphosphonate-8 showed low predicted gastrointestinal (GI) absorption, was not predicted to be a P-glycoprotein (P-gp) substrate, and was not predicted to permeate the blood-brain barrier (BBB). Crucially, Bisphosphonate-8 had no Lipinski violations and a good bioavailability score of 0.55. It also showed no PAINS (Pan Assay Interference Compounds) alerts, though one Brenk alert and one lead-likeness violation were noted. Its synthetic accessibility score was 4.0.\u003c/p\u003e\n \u003cp\u003eFenoldopam (MW\u0026thinsp;=\u0026thinsp;305.76 g/mol) also showed good predicted solubility (ESOL LogS = -3.60) and a bioavailability score of 0.55. It was predicted to have high GI absorption and to be BBB permeant, but it was also flagged as a P-gp substrate and with one PAINS alert.\u003c/p\u003e\n \u003cp\u003eIn stark contrast, Paclitaxel (MW\u0026thinsp;=\u0026thinsp;853.91 g/mol), the reference drug, displayed several characteristics less favourable for typical oral drug development. It had poor predicted water solubility (ESOL LogS = -6.66), a higher number of rotatable bonds (15), and exhibited 2 Lipinski violations with a significantly lower bioavailability score of 0.17 (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Paclitaxel was also predicted to be a P-gp substrate, though not BBB permeant, and its synthetic accessibility score (8.34) indicated greater synthetic complexity.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e4.3 DFT Calculations Reveal Enhanced Stability and Favourable Electronic Properties for Bisphosphonate-8\u003c/h2\u003e\n \u003cp\u003eTo further investigate the intrinsic stability and electronic characteristics of the selected ligands, DFT calculations were performed using Gaussian 16 with the B3LYP functional and the 6-311G(d,p) basis set. Key quantum chemical properties, including vibrational frequencies and thermodynamic parameters, are presented in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Vibrational frequency analysis confirmed that Bisphosphonate-8 achieved a true energy minimum, as indicated by the absence of imaginary frequencies and a positive lowest frequency of 41.838 cm (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). In contrast, Fenoldopam and Paclitaxel exhibited one (-77.31 cm) and three (lowest at -39.177 cm) imaginary frequencies, respectively, suggesting that their DFT-optimized geometries may represent transition states or less stable conformations. Thermodynamic properties calculated at 298.15 K and 1 atm (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e) further differentiated the ligands. Bisphosphonate-8 exhibited a total Gibbs free energy of -2056.376507 atomic units (au), which was the most favourable among the three isolated ligands, indicating greater intrinsic stability. For instance, its Gibbs free energy correction was \u0026minus;\u0026thinsp;32.014 kcal/mol. The zero-point energy, enthalpy, and internal energy values also reflected these stability trends (Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab5\" border=\"1\" style=\"margin-left: calc(1%); width: 99%;\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDFT-Calculated Physicochemical Properties of Isolated Ligands After Geometry Optimization\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProperty\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBisphosphonate-8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFenoldopam\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePaclitaxel\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eVibrational Analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLowest Frequency (cm\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41.838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-77.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-39.177\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of Imaginary Frequencies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eThermodynamic Properties\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZero-Point Energy (kcal/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e207.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187.632\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e575.654\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnthalpy (kcal/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34.183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Gibbs Free Energy (au)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2056.376507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1359.484274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2928.670783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGibbs Free Energy (kcal/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-32.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-26.924\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-48.784\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternal Energy (kcal/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.591\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeat Capacity (kcal/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e67.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e219.336\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEntropy (kcal/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161.448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e126.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e278.272\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eElectronic Properties\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOMO Energy (eV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-5.894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-6.926\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLUMO Energy (eV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2.263\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHOMO-LUMO Gap (eV)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.665\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSolvation Energy (kcal/mol)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-201.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-91.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-46.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Energies (au)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Internal Energy (au)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2056.300743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1359.425015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2928.539511\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Enthalpy (au)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2056.299798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1359.42407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2928.538567\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal Free Energy (au)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2056.376507\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1359.484274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2928.670783\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThis table summarizes key quantum chemical and thermodynamic properties for the isolated ligands Bisphosphonate-8, Fenoldopam, and Paclitaxel, following geometry optimization using Density Functional Theory (DFT). These properties offer insights into the intrinsic stability, reactivity, and behaviour of the ligands. The vibrational analysis includes the lowest vibrational frequency and the number of imaginary frequencies, where zero imaginary frequencies confirm that the optimized structures represent true minima on the potential energy surface. Thermodynamic data include thermal corrections to energy, enthalpy, Gibbs free energy, entropy, zero-point vibrational energy, and heat capacity. Electronic properties such as HOMO and LUMO energies, as well as the HOMO-LUMO energy gap an indicator of chemical reactivity and stability are also reported; a larger gap generally correlates with higher stability. Solvation energies indicate the energetic cost of transferring each molecule from the gas phase to an aqueous environment. Additionally, total energies (in atomic units) including thermal corrections are provided, with the Total Gibbs Free Energy serving as a comparative measure of overall thermodynamic stability. All calculations were performed using Gaussian 16 with the B3LYP functional and 6-311G(d,p) basis set, with thermodynamic properties evaluated at 298.15 K and 1.00 atm. Geometry optimizations converged successfully for all ligands.\u003c/p\u003e\n \u003cp\u003eAnalysis of the frontier molecular orbitals (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) revealed that Bisphosphonate-8 possessed a HOMO-LUMO energy gap of 4.707 electron volts (eV). This was larger than that of Fenoldopam 3.347 eV, indicating greater chemical stability and lower reactivity for Bisphosphonate-8, and comparable to Paclitaxel 4.665 eV.\u003c/p\u003e\n \u003cp\u003eBinding energy calculations for the DFT-optimized ligand-protein complexes (previously detailed in Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e) demonstrated that Bisphosphonate-8 formed the most energetically favourable complex with the tubulin dimer (-0.3156 au). This was followed by Fenoldopam (-0.2202 au), while Paclitaxel showed a slightly positive binding energy (0.1673 au) with the dimer under these quantum-optimized conditions, suggesting a less stable interaction. Overall, the DFT results, encompassing both intrinsic ligand properties (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e) and complexation energies (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e), consistently support the enhanced stability and favourable electronic profile of Bisphosphonate-8 (Table \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDFT-Calculated Binding Energies of Ligand-Tubulin Dimer Complexes After Ligand Geometry Optimization\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS. No\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTitle\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBisphosphonates-8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFenoldopam\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePaclitaxel\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnergy of Complex (Ligand, Active Site) (au)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5915.617549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3965.591135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-6877.925538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnergy of Protein (active site) (au)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3858.645253\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2605.630541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3948.582412\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnergy of Ligand (au)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2056.656667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1359.740377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2929.510404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBinding energy (au)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.315629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.220217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.167278\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eThis table summarizes the binding energies (au) for each ligand-tubulin dimer complex, calculated after geometry optimization of the isolated ligands using DFT (B3LYP/6-311G(d,p)). The binding energy is calculated as: E \u003csub\u003ebinding\u003c/sub\u003e = E \u003csub\u003ecomplex\u003c/sub\u003e - (E \u003csub\u003eprotein (active site) +\u003c/sub\u003e E \u003csub\u003eligand)\u003c/sub\u003e. More negative binding energies indicate more favorable complex formation.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e4.4 \u003cstrong\u003eRe\u003c/strong\u003e-\u003cstrong\u003edocking of DFT\u003c/strong\u003e-\u003cstrong\u003eOptimized Ligands with Tubulin\u003c/strong\u003e\u003c/h2\u003e\n \u003cp\u003eFollowing DFT optimization, the ligands were re-docked into the tubulin binding site using AutoDock4 (Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). Bisphosphonate-8 maintained a strong binding affinity (-9.01 kcal/mol and Ki of 249.43 nM), forming six hydrogen bonds with residues Cys12, Asn101, Gly144, Thr145 (twice), and Gly146 (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). Fenoldopam exhibited a moderate binding affinity (-7.96 kcal/mol and Ki of 1.47 \u0026micro;M), forming four hydrogen bonds (with residues Thr145, Gly146 (2), and Asn206) and two halogen bonds formed (with residues Gly100, Asn101) with Chlorine (Cl) (Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e). Paclitaxel showed a binding affinity comparable to Bisphosphonate-8 (-8.65 kcal/mol and Ki of 455.15 nM) but formed only three hydrogen bonds (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). RMSD values for Bisphosphonate-8, Fenoldopam and Paclitaxel upon redocking were 1.989 \u0026Aring;, 3.842 \u0026Aring;, and 3.843 \u0026Aring; respectively. The torsional free energies upon re-docking were 2.39 kcal/mol, 0.60 kcal/mol and 5.07 kcal/mol respectively, for Bisphosphonate-8, Fenoldopam and Paclitaxel (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMolecular Docking Parameters of DFT-Optimized Ligands with the Tubulin \u0026alpha;-\u0026beta; Dimer\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS. No\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBisphosphonate-8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFenoldopam\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePaclitaxel\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimated Free Energy of Binding (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-9.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEstimated Inhibition Constant, Ki (\u0026micro;M)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249.43 nM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.47 \u0026micro;M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e455.15 nM\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFinal Intermolecular Energy (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-11.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003evdW\u0026thinsp;+\u0026thinsp;Hbond\u0026thinsp;+\u0026thinsp;Desolvation Energy (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-10.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-7.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-13.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eElectrostatic Energy (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTorsional Free Energy (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternal Energy After Binding (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInternal Energy brfore Binding (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-8.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRMSD from Reference Structure (\u0026Aring;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eInteractions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrogen bonds (6)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eCys 12, Asn 101, Gly 144, Thr 145 (2), Gly 146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrogen bonds (4)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eThr 145, Gly 146 (2), Asn 206\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHalogen bonds (2)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eGly 100, Asn 101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrogen bonds (3)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAsn 101, Ser 140, Thr 180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eThis table presents the outcomes of redocking the DFT-optimized ligand geometries into the tubulin \u0026alpha;-\u0026beta; dimer (PDB ID: 7PJF) using AutoDock4. It includes the estimated binding free energy (kcal/mol), estimated inhibition constant (Ki), final intermolecular energy components, torsional free energy, and RMSD from the initial reference docking structure.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eInternal energy before and after binding was identical for each ligand.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eInteractions list predominant hydrogen and halogen bonds formed with \u0026beta;-tubulin residues.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e4.5 MD Simulations and trajectories Analysis throughout 1\u0026micro;s\u003c/h2\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e4.5.1 System Stability and Fluctuations Analysis Throughout 1\u0026micro;s MD simulations\u003c/h2\u003e\n \u003cp\u003eMD simulations (1\u0026micro;s) were performed to analyse the stability and dynamic behaviour of the tubulin-ligand complexes. Bisphosphonate-8 consistently showed the lowest average total and potential energies throughout the simulations (Table \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e), indicating a more stable complex compared to Fenoldopam and Paclitaxel (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e). The RMSD analysis (Figs. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e,\u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e) showed that the Bisphosphonate-8 complex remained stable, with an average RMSD of 2.854 \u0026Aring;. Fenoldopam showed moderate fluctuations (average RMSD 6.785 \u0026Aring;), whereas Paclitaxel exhibited significantly larger deviations (average RMSD 25.491 \u0026Aring;), indicating instability. RMSF analysis (Figs. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e) revealed similar trends, with Bisphosphonate-8 exhibiting the lowest fluctuations, particularly for the ligand itself (0.426 \u0026Aring;), confirming stable binding within the pocket. Paclitaxel, in contrast, showed considerably higher RMSF values, both for the protein and the ligand, indicating greater flexibility and a less stable binding mode. These findings are further supported by Table \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e (Figs. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e, Tables \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSystem Energetics of Tubulin-Ligand Complexes During 1 \u0026micro;s MD Simulations.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS. No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters/Properties during 1,000 trajectories of 1 \u0026micro;s MDS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBisphosphonates-8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFenoldopam\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePaclitaxel\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage total energy (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2,11,019.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2,10,831.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2,08,231.1758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage potential energy (kcal/mol)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2,57,755.936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2,57,563.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-2,54,785.447\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDegrees of freedom\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,57,464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,57,449\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1,56,851\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eNumber of particles\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75,307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75,305\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75,004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eThis table summarizes key energetic parameters for the Bisphosphonate-8, Fenoldopam, and Paclitaxel complexes with tubulin, averaged over 1,000 trajectories from 1 \u0026micro;s MD simulations. \u0026quot;Average total energy\u0026quot; reflects the overall stability of the system, while \u0026quot;Average potential energy\u0026quot; provides insight into the stability of the protein-ligand interactions. \u0026quot;Degrees of freedom\u0026quot; and \u0026quot;Number of particles\u0026quot; describe the simulated system size and complexity. All energy values are reported in kcal/mol.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eRMSD and RMSF Metrics for Tubulin-Ligand Complexes During MD Simulations (MDS).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS. No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters/Properties during 1,000 trajectories of 1 \u0026micro;s MDS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBisphosphonates-8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFenoldopam\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePaclitaxel\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage protein-Ligand RMSD (\u0026Aring;): C\u0026alpha;, backbone, sidechain, protein hetero atoms, ligand with respect to protein, ligand with respect to ligand\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.525, 2.543, 3.533, 2.951, 2.854, 0.772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.6, 2.612, 3.602, 3.023, 6.785, 0.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.617, 2.635, 3.621, 3.0531, 28.491, 2.529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage protein RMSF (\u0026Aring;)\u003c/strong\u003e:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eC\u0026alpha;, backbone, sidechain, protein hetero atoms\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.051, 1.0759, 1.522, 1.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.243, 1.263, 1.689, 1.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.045, 1.066, 1.519, 1.296,\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAverage ligand RMSF (\u0026Aring;)\u003c/strong\u003e:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eligand with respect to protein, ligand with respect to ligand\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.1458, 0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.458, 0.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.344, 1.537\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eLegend/Footnote: This table presents RMSD and RMSF values characterizing the structural stability and flexibility of the Bisphosphonate-8, Fenoldopam, and Paclitaxel complexes with tubulin during 1 \u0026micro;s MD simulations.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u0026bull; Average protein-Ligand RMSD (\u0026Aring;): Values represent the average deviation of specific protein components (C\u0026alpha;, backbone, sidechain, protein hetero atoms) and the ligand (calculated with respect to the protein and with respect to its initial docked pose) from the initial structure. Lower RMSD values indicate greater structural stability.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u0026bull; Average protein RMSF (\u0026Aring;): Values represent the average fluctuation of specific protein components (C\u0026alpha;, backbone, sidechain, protein hetero atoms), indicating residue-level flexibility.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u0026bull; Average ligand RMSF (\u0026Aring;): Values represent the average fluctuation of the ligand (calculated with respect to the protein and with respect to its initial docked pose), indicating its flexibility within the binding site. All RMSD and RMSF values are reported in Angstroms (\u0026Aring;).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\n \u003ch2\u003e4.5.2 Interaction Analysis Throughout 1\u0026micro;s MD simulations\u003c/h2\u003e\n \u003cp\u003eAnalysis of intermolecular interactions during MD simulations (Table \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e) revealed that the Bisphosphonate-8 complex formed the highest number of total interactions (12,830), including a substantial number of hydrogen bonds (2,709), metallic interactions (5,095), and water bridges (4,543), indicative of a stable and well-defined binding mode. Paclitaxel, despite having some hydrophobic and pi-pi stacking interactions, formed fewer total interactions (3,908) and significantly fewer hydrogen bonds (1,172), which may explain its observed instability. Fenoldopam formed a moderate number of interactions (8,143), but these were not as extensive or persistent as those observed for Bisphosphonate-8. Figure \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e demonstrated that Bisphosphonate-8 and fenoldopam interactions maintained consistently over approximately 30% of the simulation duration. But Paclitaxel maintained interactions consistently over approximately 10% of the simulation duration (Fig. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e). Figures \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e says that Paclitaxel showing lowest number of interactions throughout 1 \u0026micro;s MD simulations (Figs. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e). Figure \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e illustrates the interaction profiles of each ligand with tubulin over the course of the simulation, highlighting the persistent interactions of Bisphosphonate-8 with Asp69 and Thr145 (Figs. \u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab10\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSummary of Specific Intermolecular Interactions Observed in Tubulin-Ligand Complexes During 1 \u0026micro;s MD Simulations.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS. No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eParameters/Properties during 1,000 trajectories of 1 \u0026micro;s MDS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBisphosphonates-8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFenoldopam\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePaclitaxel\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrogen bonds\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,172\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHydrophobic interactions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e974\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eIonic interactions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMetallic interactions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5,095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePi-cation interactions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePi-pi stacking interactions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWater bridge interactions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4,543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal number of Interactions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12,830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8,143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3,908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eThis table quantifies the various types of intermolecular interactions (hydrogen bonds, hydrophobic interactions, ionic interactions, metallic interactions, pi-cation interactions, pi-pi stacking interactions, and water bridge interactions) identified between Bisphosphonate-8, Fenoldopam, and Paclitaxel and the tubulin protein throughout the 1 \u0026micro;s MD simulations. The \u0026quot;Total number of Interactions\u0026quot; represents the sum of all listed interaction types. These values provide insights into the nature and strength of the ligand-protein binding.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\n \u003ch2\u003e4.5.3 Structural Properties and Surface Area Analysis of MD Simulations\u003c/h2\u003e\n \u003cp\u003eAnalysis of structural properties (Table \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e) revealed that Bisphosphonate-8 exhibited a more compact structure (radius of gyration, rGyr\u0026thinsp;=\u0026thinsp;4.75 \u0026Aring;) compared to Paclitaxel (rGyr\u0026thinsp;=\u0026thinsp;5.55 \u0026Aring;), while Fenoldopam was the most compact (rGyr\u0026thinsp;=\u0026thinsp;3.58 \u0026Aring;) (Fig. \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e). Bisphosphonate-8 also showed the highest number of intramolecular hydrogen bonds (intraHB\u0026thinsp;=\u0026thinsp;0.43), indicative of greater structural rigidity. Paclitaxel possessed the largest molecular surface area (MolSA) and solvent-accessible surface area (SASA), whereas Fenoldopam had the smallest. Bisphosphonate-8 had the largest polar surface area (PSA), suggesting a greater potential for hydrogen bonding interactions (Figs. \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab11\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLigand Structural Properties and Surface Interaction Metrics Derived from 1 \u0026micro;s MDS\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS. No\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLigands\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003erGyr\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eintraHB)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMolSA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSASA\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePSA\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eBisphosphonate-8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.747053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.433566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e357.3182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e127.1727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e239.9978\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFenoldopam\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.580194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e265.5735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e233.4635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e159.4556\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePaclitaxel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.547752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.263736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e633.3919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e773.1203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e219.9332\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eThis table presents key structural and surface interaction properties for Bisphosphonate-8, Fenoldopam, and Paclitaxel, averaged over the 1 \u0026micro;s MD simulations.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u0026bull; rGyr (\u0026Aring;): Radius of Gyration, a measure of molecular compactness.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u0026bull; intraHB: Number of intramolecular hydrogen bonds, indicating ligand rigidity.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u0026bull; MolSA (\u0026Aring;\u0026sup2;): Molecular Surface Area, reflecting the overall surface exposure of the ligand.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u0026bull; SASA (\u0026Aring;\u0026sup2;): Solvent Accessible Surface Area, indicating the portion of the ligand exposed to the solvent.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003e\u0026bull; PSA (\u0026Aring;\u0026sup2;): Polar Surface Area, a descriptor related to drug permeability and bioavailability. These properties provide insights into the ligand\u0026apos;s flexibility, compactness, and potential for interaction with the protein and solvent.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n \u003ch2\u003e4.5.4 RDF Analysis of MD Simulations Trajectories\u003c/h2\u003e\n \u003cp\u003eRDF analysis provided further insight into the binding stability of the ligand-protein complexes. Bisphosphonate-8 (Graph A, Fig. \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003e) showed sharp peaks at distances between 4\u0026ndash;6 \u0026Aring;, indicating strong and stable interactions with a high degree of occupancy within the binding pocket. Fenoldopam (Graph B, Fig. \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003e) also showed distinct peaks in this region, but with some broadening, indicating moderate stability and some flexibility within the binding site. Paclitaxel (Graph C, Fig. \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003e) displayed broader and less intense peaks, suggesting weaker and more fluctuating interactions. The radial density distribution curves further confirmed these observations, with Bisphosphonate-8 showing the highest density near the protein, indicating tight binding (Figs. \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\n \u003ch2\u003e4.5.5 Clustering Analysis of MD Trajectories\u003c/h2\u003e\n \u003cp\u003eClustering analysis of the MD trajectories (Table \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e) identified 12, 15 and 10 distinct clusters for Bisphosphonate-8, Fenoldopam, and Paclitaxel, respectively. Bisphosphonate-8 showed the most populated cluster (Cluster 1) containing 19 frames, with C\u0026alpha; RMSD and ligand RMSD of 2.57 and 3.56 \u0026Aring;, respectively. The C\u0026alpha; RMSD values remained within a narrow range (2.1 to 2.7 \u0026Aring;) and ligand RMSD values also were moderate, indicating relatively stable conformation for Bisphosphonate-8. The Fenoldopam-tubulin complex showed higher conformational variability, forming 16 distinct clusters. The ligand RMSD values across clusters were generally higher than those of Bisphosphonate-8, reaching values up to 9.4 \u0026Aring; (e.g., in a cluster containing 10 frames). Despite some clusters exhibiting low ligand RMSD values, this wider range suggests moderate binding instability and increased conformational flexibility for Fenoldopam, which may affect its binding persistence (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab12\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 12\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eClustering Analysis of Tubulin-Ligand Complex Trajectories from 1 \u0026micro;s MDS Based on C\u0026alpha; and Ligand RMSD.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eBisphosphonates-8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eFenoldopam\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePaclitaxel\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCluster No.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo. of\u003c/p\u003e\n \u003cp\u003eFrames\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRMSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo. of\u003c/p\u003e\n \u003cp\u003eFrames\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRMSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNo. of\u003c/p\u003e\n \u003cp\u003eFrames\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eRMSD\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC Alpha\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLigand\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC Alpha\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLigand\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC Alpha\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLigand\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 0 /\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.445\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.502\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.615\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.645\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.911\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.766\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.325\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.488\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.566\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.584\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.795\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 11\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.584\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 12\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.786\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 13\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 14\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.605\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCluster 15\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal Frames\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"10\"\u003eThis table presents the results of clustering analysis performed on the 1 \u0026micro;s MD simulation trajectories of Bisphosphonate-8, Fenoldopam, and Paclitaxel complexed with tubulin. Clustering was based on the Root Mean Square Deviation (RMSD) of protein C\u0026alpha; atoms and the ligand. \u0026quot;No. of Frames\u0026quot; indicates the population of each cluster. \u0026quot;RMSD C Alpha\u0026quot; and \u0026quot;RMSD Ligand\u0026quot; represent the average RMSD values (in \u0026Aring;) for the protein C\u0026alpha; atoms and the ligand within each cluster, respectively, relative to the reference structure (Cluster 0). This analysis reveals the conformational diversity and dominant binding poses adopted by each ligand during the simulation.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn stark contrast, the Paclitaxel-tubulin complex displayed the greatest conformational divergence. While 10 clusters were observed, several exhibited extremely high ligand RMSD values. Notably, Cluster 3 (11 frames) reached a ligand RMSD of over 56 \u0026Aring;, and other significantly populated clusters (Cluster 2 with 12 frames, Cluster 5 with 10 frames, and Cluster 9 with 8 frames) consistently showed ligand RMSD values above 30 \u0026Aring; (36.5 \u0026Aring;, 45.1 \u0026Aring;, and 39.6 \u0026Aring; respectively). Collectively, these highly divergent clusters (2, 3, 5, and 9) accounted for approximately 41% of the simulation frames, indicating that Paclitaxel spent a substantial portion of the simulation time in conformations significantly displaced from its initial binding pose. This strongly suggests significant displacement and instability of Paclitaxel within the tubulin dimer binding site. Throughout these changes, the C\u0026alpha; RMSD values for the protein remained relatively consistent across Paclitaxel\u0026apos;s clusters (ranging from 2.3 to 2.9 \u0026Aring;), indicating that the protein itself maintained structural stability while the ligand exhibited substantial instability (Table \u003cspan class=\"InternalRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\n \u003ch2\u003e4.5.6 PCA for Throughout 1\u0026micro;s MD simulations\u003c/h2\u003e\n \u003cp\u003ePCA of the MD trajectories (Table \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e) was performed to identify the principal motions of the tubulin-ligand complexes. Fenoldopam showed the largest variance along PC1 (29.56%), indicating more extensive global conformational changes, whereas Bisphosphonate-8 (19.49%) and Paclitaxel (21.23%) showed lower variance. Bisphosphonate-8 displayed moderate localized fluctuations (PC2\u0026thinsp;=\u0026thinsp;12.47%) (Fig. \u003cspan class=\"InternalRef\"\u003e16\u003c/span\u003e). Per-residue eigenvector displacement analysis (Fig. \u003cspan class=\"InternalRef\"\u003e18\u003c/span\u003e) confirmed that Bisphosphonate-8 induced localized flexibility primarily in a specific region (residue 588). Paclitaxel exhibited substantial residue-specific instability, as indicated by large displacements at residue 846. Free energy landscape (FEL) plots generated from PCA (Fig. \u003cspan class=\"InternalRef\"\u003e17\u003c/span\u003e) indicate that approximately 90% of the frames for the Bisphosphonate-8, Fenoldopam, and Paclitaxel complexes with tubulin exhibit free energy values lower than 1, 2, and 5 kcal/mol, respectively. These PCA results align with the findings from RMSD, RMSF, and clustering analyses, further supporting the conclusion that Bisphosphonate-8 displays enhanced stability and a more well-defined binding mode compared to Fenoldopam and Paclitaxel (Figs. \u003cspan class=\"InternalRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e18\u003c/span\u003e, Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab13\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 13\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003ePrincipal Component Analysis (PCA) of Tubulin-Ligand Complex Dynamics from 1 \u0026micro;s MDS.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS. No\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePCA Type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBisphosphonates-8\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFenoldopam\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePaclitaxel\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePC1\u003c/strong\u003e (Global conformational changes)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.49%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29.56%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21.23%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePC2\u003c/strong\u003e (Localized fluctuations)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12.47%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.46%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.94%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePC3\u003c/strong\u003e (Cumulative variance)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.23%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eLegend/Footnote: This table summarizes the results of Principal Component Analysis (PCA) applied to the 1 \u0026micro;s MD simulation trajectories of Bisphosphonate-8, Fenoldopam, and Paclitaxel complexed with tubulin. The values represent the percentage of total variance in protein dynamics captured by the first three principal components (PC1, PC2, and PC3).\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u0026bull; PC1 (Global conformational changes): Reflects large-scale, collective motions of the protein-ligand complex.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u0026bull; PC2 (Localized fluctuations): Captures more localized, smaller-scale motions.\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u0026bull; PC3 (Cumulative variance): Represents the cumulative contribution of the third principal component to the overall variance. This analysis provides insights into the dominant dynamic modes and conformational flexibility of the complexes.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study employed a comprehensive in silico approach to identify and characterize potential inhibitors of the tubulin α-β dimer, a novel and underexplored target for pancreatic cancer therapy. Our findings consistently highlight Bisphosphonate-8 as a lead candidate, demonstrating superior binding affinity, structural stability, and dynamic behaviour compared to Fenoldopam and the clinically used microtubule-stabilizing agent, Paclitaxel, when interacting with the tubulin dimer.\u003c/p\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Bisphosphonate-8: A Structurally and Dynamically Superior Tubulin Dimer Ligand\u003c/h2\u003e\u003cp\u003eThe initial molecular docking studies revealed Bisphosphonate-8's significantly lower binding free energy and picomolar inhibition constant (Ki) for the tubulin dimer, surpassing both Fenoldopam and Paclitaxel (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This superior affinity is attributed to a robust network of interactions, including key hydrogen bonds with Ser140, Glu183, and Tyr224 within the β-tubulin binding pocket, and a favourable orientation that allows deep penetration into the active site (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). While Paclitaxel exhibited a reasonable intermolecular energy, its high torsional free energy penalty indicated significant conformational strain upon binding, likely diminishing its effective interaction with the isolated dimer, a finding consistent with its primary mechanism of stabilizing polymerized microtubules [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. The structural stability of Bisphosphonate-8 within the binding site, evidenced by its low RMSD in docking, further supports its potential as a specific dimer-targeting agent.\u003c/p\u003e\u003cp\u003eDFT calculations reinforced these findings, demonstrating Bisphosphonate-8's superior electronic and thermodynamic stability (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The absence of imaginary frequencies confirmed an optimized, stable geometry for Bisphosphonate-8, unlike Fenoldopam and Paclitaxel. Furthermore, its favourable solvation energy suggests better aqueous compatibility, a crucial factor for drug development. The HOMO-LUMO gap analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) indicated a good balance of chemical stability and reactivity for Bisphosphonate-8, crucial for effective biological interactions. The DFT-calculated binding energies further corroborated the docking results, showing a more energetically favourable complex formation for Bisphosphonate-8 with the tubulin dimer compared to the other compounds. Notably, for Bisphosphonate-8, DFT optimization of the ligand geometry led to a decrease in the estimated binding energy (from \u0026minus;\u0026thinsp;13.63 kcal/mol to -9.01 kcal/mol), alongside a shift in the predicted inhibition constant from the picomolar to the nanomolar range (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). This reduction suggests that the original docking may have overestimated binding affinity due to idealized ligand conformations. The DFT-refined structure, which reflects a more realistic electronic and geometric profile, binds with a slightly reduced but still highly favourable affinity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), supporting the ligand\u0026rsquo;s potential while accounting for conformational stability and the rigor of the computational approach. Even with this refinement, Bisphosphonate-8 maintained a superior interaction profile compared to Paclitaxel.\u003c/p\u003e\u003cp\u003eThe 1 \u0026micro;s MD simulations provided critical insights into the dynamic behaviour of these complexes (Tables\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). Bisphosphonate-8 consistently formed the most energetically stable complex, exhibiting the lowest RMSD and RMSF values for both the protein and the ligand. This indicates a tightly bound and conformationally stable complex over the simulation period, essential for sustained inhibitory activity (Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). The extensive network of persistent interactions, including a high number of hydrogen bonds, metallic interactions (likely with the Mg2\u0026thinsp;+\u0026thinsp;ion), and water bridges, underpins this dynamic stability (Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). In stark contrast, Paclitaxel displayed significant instability and high ligand RMSD/RMSF values (Figs.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e), suggesting it does not maintain a stable interaction with the unpolymerized tubulin dimer. This observation is pivotal, as it supports the hypothesis that Paclitaxel's efficacy is largely restricted to polymerized microtubules, and it may be ineffective against the dimer, a potentially important target in specific cancer contexts [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Fenoldopam showed intermediate stability but did not match the robust dynamic profile of Bisphosphonate-8.\u003c/p\u003e\u003cp\u003eFurther dynamic analyses, including RDF (Fig.\u0026nbsp;\u003cspan refid=\"Fig15\" class=\"InternalRef\"\u003e15\u003c/span\u003e), clustering (Table\u0026nbsp;\u003cspan refid=\"Tab12\" class=\"InternalRef\"\u003e12\u003c/span\u003e), and PCA (Table\u0026nbsp;\u003cspan refid=\"Tab13\" class=\"InternalRef\"\u003e13\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig16\" class=\"InternalRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e18\u003c/span\u003e), consistently favoured Bisphosphonate-8. The sharp RDF peaks for Bisphosphonate-8 indicated a well-defined and persistent binding mode. Clustering analysis revealed that Bisphosphonate-8 predominantly occupied a stable conformational state, while Paclitaxel explored a wide range of unstable conformations. PCA demonstrated that Bisphosphonate-8 induced more restrained global motions and moderate localized fluctuations compared to the extensive and destabilizing movements observed with Paclitaxel, suggesting a more controlled and effective interaction. The pharmacokinetic predictions (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) also favoured Bisphosphonate-8 in terms of drug-likeness and fewer predicted liabilities compared to Paclitaxel, although Fenoldopam showed some favourable absorption properties. However, for a targeted therapy, strong and specific binding, as demonstrated by Bisphosphonate-8, is paramount.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Favourable Physicochemical and Pharmacokinetic Profile of Bisphosphonate-8: Enhancing its Therapeutic Potential\u003c/h2\u003e\u003cp\u003eBeyond its robust target engagement, the in silico predicted physicochemical and pharmacokinetic properties of Bisphosphonate-8 (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) further distinguish it as a promising drug candidate, especially when contrasted with Paclitaxel. Bisphosphonate-8's moderate molecular weight (448.3 g/mol) and fewer rotatable bonds (5) are advantageous compared to Paclitaxel's larger size (853.91 g/mol) and greater flexibility (15 rotatable bonds), generally correlating with better oral bioavailability and membrane permeability [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eA key predicted advantage for Bisphosphonate-8 is its good water solubility (e.g., ESOL LogS \u0026minus;\u0026thinsp;3.57), contrasting sharply with Paclitaxel\u0026rsquo;s very poor predicted solubility (ESOL LogS \u0026minus;\u0026thinsp;6.66). This is highly significant, as Paclitaxel's low solubility necessitates complex intravenous formulations often associated with hypersensitivity reactions [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Furthermore, Bisphosphonate-8 is not predicted to be a P-glycoprotein (P-gp) substrate. Paclitaxel, however, is a known P-gp substrate (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), and P-gp mediated efflux is a major mechanism of multidrug resistance in cancer [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Evading P-gp could allow Bisphosphonate-8 to achieve higher effective intracellular concentrations.\u003c/p\u003e\u003cp\u003eIn terms of drug-likeness, Bisphosphonate-8 adheres to Lipinski's Rule of Five (0 violations) and has a good predicted bioavailability score (0.55). Paclitaxel, conversely, shows 2 Lipinski violations and a much lower bioavailability score (0.17) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), reflecting its established limitations as an oral agent. The absence of PAINS alerts for Bisphosphonate-8 also reduces concerns about non-specific assay interference. While a Brenk alert was noted, this is a flag for future medicinal chemistry optimization [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. The moderate synthetic accessibility score for Bisphosphonate-8 (4.0) also suggests greater tractability for synthesis and derivatization compared to the complex natural product structure of Paclitaxel (synthetic accessibility 8.34). Collectively, these ADMET predictions highlight Bisphosphonate-8 as possessing a more favourable drug-like profile than Paclitaxel, enhancing its potential for successful development.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e5.3 The Significance of Targeting the Tubulin Dimer in Pancreatic Cancer\u003c/h2\u003e\u003cp\u003ePancreatic cancer remains a formidable therapeutic challenge, largely due to late diagnosis and profound chemoresistance [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Existing tubulin-targeting agents, like Paclitaxel, primarily act on polymerized microtubules. However, resistance to these agents can arise through various mechanisms, including mutations in tubulin, altered expression of tubulin isotypes, or changes in microtubule dynamics that reduce drug efficacy [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTargeting the tubulin α-β dimer represents a potentially novel strategy to circumvent some of these resistance mechanisms and offers a different point of intervention in microtubule regulation. The dimer is the fundamental building block, and its availability and dynamics are crucial for proper microtubule assembly. There is emerging, albeit limited, evidence suggesting that the pool of free tubulin dimers might be dysregulated in certain cancer cells or under conditions of stress and resistance [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. By identifying potent dimer-specific inhibitors like Bisphosphonate-8, it may be possible to disrupt microtubule formation at an earlier stage or target cancer cells that have developed resistance to polymer-targeting drugs. While research on direct tubulin dimer inhibitors is less extensive than on microtubule-stabilizing/destabilizing agents [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e], our \u003cem\u003ein silico\u003c/em\u003e findings suggest that Bisphosphonate-8 has the characteristics to effectively engage this form of tubulin. The poor interaction of Paclitaxel with the dimer in our study further highlights the need for and potential of developing dedicated dimer inhibitors.\u003c/p\u003e\u003cp\u003eThe class of bisphosphonates themselves has been primarily studied for their effects on bone resorption, but some have shown anticancer activities through various mechanisms, including interference with protein prenylation and induction of apoptosis [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Our study is among the first, to our knowledge, to computationally explore a specific bisphosphonate derivative for direct interaction with the tubulin dimer, suggesting a novel mechanism of action for this chemical scaffold in an oncological context.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e5.4 Limitations of the Study\u003c/h2\u003e\u003cp\u003eThis study, while comprehensive in its computational scope, has inherent limitations. The findings are based entirely on \u003cem\u003ein silico\u003c/em\u003e predictions, and experimental validation is crucial to confirm the binding affinities, inhibitory activities, and cellular effects of Bisphosphonate-8. Molecular docking and MD simulations rely on force fields and scoring functions that are approximations of complex biological reality. While the OPLS4 force field and extended simulation times (1\u0026micro;s) enhance reliability, discrepancies between computational predictions and experimental outcomes can occur. The solvent model, though explicit, is also a simplification. Furthermore, the ADMET predictions are based on computational models and require experimental verification. Finally, this study focused on the interaction with a single PDB structure of tubulin; variations in tubulin isotypes or post-translational modifications present in pancreatic cancer cells were not accounted for.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this rigorous multi-scale computational investigation has elucidated the superior binding characteristics and dynamic behaviour of Bisphosphonate-8 upon interaction with the α-β tubulin dimer, establishing it as a highly promising lead candidate. Our findings consistently demonstrate that, compared to Fenoldopam and the conventional microtubule agent Paclitaxel, Bisphosphonate-8 exhibits significantly enhanced binding affinity, a more stable docked pose validated by DFT calculations, and, critically, maintains persistent and stable interactions throughout extensive 1µs MD simulations. Paclitaxel, in stark contrast, displayed marked instability and an inability to effectively engage the tubulin dimer, highlighting a key distinction in their potential mechanisms and target preferences. These structural and dynamic insights, combined with Bisphosphonate-8’s more favourable\u0026nbsp;in silico\u0026nbsp;pharmacokinetic predictions, strongly suggest its potential as a novel, dimer-specific tubulin inhibitor.\u003c/p\u003e\n\u003cp\u003eWhile this study provides compelling\u0026nbsp;in silico\u0026nbsp;evidence, certain limitations must be acknowledged. The findings are inherently predictive and await experimental validation. The computational models, including force fields and ADMET predictions, are approximations of complex biological systems, and the study focused on a single tubulin crystal structure without considering potential isotype variations or post-translational modifications pertinent to pancreatic cancer.\u003c/p\u003e\n\u003cp\u003eDespite these limitations, this work provides a solid foundation and a compelling rationale for future research.\u0026nbsp;Crucially, future efforts should focus on the experimental validation of Bisphosphonate-8’s predicted activity, encompassing biochemical assays to confirm dimer interaction and cell-based studies to assess its efficacy against pancreatic cancer cell lines. Confirmation of these\u0026nbsp;in silico\u0026nbsp;findings could then pave the way for structural elucidation of the complex and further preclinical development.\u0026nbsp;Ultimately, the successful experimental pursuit of Bisphosphonate-8, guided by these computational insights, holds the potential to yield a novel therapeutic intervention targeting a distinct mechanistic vulnerability in tubulin for the treatment of pancreatic cancer.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contribution Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHemantha Mani Kumar Chakravarthi Chanda\u003c/strong\u003e\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003cstrong\u003e:\u003c/strong\u003e Executed \u003cem\u003ein silico\u0026nbsp;\u003c/em\u003eanalyses including molecular docking and molecular dynamics simulations, and contributed to the writing and editing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSudheer Kumar Katari\u003c/strong\u003e\u003csup\u003e\u0026nbsp;a\u003c/sup\u003e\u003cstrong\u003e:\u003c/strong\u003e Contributed to the study\u0026apos;s conceptualization and design, and conducted \u003cem\u003ein silico\u0026nbsp;\u003c/em\u003eanalysis, and including target prediction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAffiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Department of Bioinformatics, Vignan\u0026apos;s Foundation for Science, Technology and Research (Deemed to be University), Vadlamudi, Guntur-522213, India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Author\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSudheer Kumar Katari\u003c/strong\u003e Vignan\u0026apos;s Foundation for Science, Technology and Research (Deemed to be University), Vadlamudi, Guntur-522213, India.\u003c/p\u003e\n\u003cp\[email protected]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubject Area\u003c/strong\u003e: Bioinformatics, Computational Biology, Drug Discovery, Structural Biology\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no financial support was received for the research, authorship, and / or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors are highly thankful to VFSTR (Deemed to be University) for providing faculty seed grant (F.No. VFSTR/REG/A6/30/2023-24/01 dated 16-05-2023) facility.\u003c/p\u003e\n\u003cp\u003eThe authors acknowledge the use of AI tools, including ChatGPT (OpenAI) and Gemini (Google), for language editing and sentence refinement. These tools were employed solely to enhance the clarity and grammar of the manuscript, and were not used for generating scientific content, analysis, technical writing, or interpretation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMu\u0026ntilde;oz AR, Chakravarthy D, Gong J et al (2017) Pancreatic Cancer: Current Status and Challenges. Curr Pharmacol Rep 3\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLeiphrakpam PD, Chowdhury S, Zhang M et al (2025) Trends in the Global Incidence of Pancreatic Cancer and a Brief Review of its Histologic and Molecular Subtypes. 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Ann Oncol 14\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Pancreatic cancer, alpha-beta tubulin dimer, Bisphosphonate-8, tubulin inhibitors, binding free energy, conformational adaptability, molecular dynamics simulations","lastPublishedDoi":"10.21203/rs.3.rs-8016981/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8016981/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePancreatic cancer remains one of the most lethal malignancies, with mortality rates exceeding 90% in males and 89% in females, largely due to late-stage diagnosis and chemotherapy resistance. Despite advances in cancer research, effective treatment options for pancreatic cancer remain limited, highlighting the urgent need for novel therapeutic strategies. Identification of potent tubulin dimer inhibitors, disrupting the Alpha-Beta dimer complex essential for microtubule dynamics and cancer cell proliferation, offers a promising therapeutic avenue. Computational drug discovery provides an effective strategy to find novel inhibitors with enhanced binding efficiency, stability, and adaptability for personalized treatments. We implemented a multi-faceted computational approach combining molecular docking, density functional theory (DFT), molecular dynamics (MD) simulations, and principal component analysis (PCA). DrugBank screening identified Bisphosphonate-8 and Fenoldopam as potential inhibitors, with Paclitaxel as a reference drug. DFT calculations provided quantum mechanical insights into ligand-tubulin interactions, while binding energy analysis revealed Bisphosphonate-8 exhibiting a nearly 2-fold higher binding affinity than Paclitaxel. Microsecond-scale MD simulations assessed the stability and flexibility of ligand-protein complexes, and PCA analysis of MD simulations trajectories demonstrated significant conformational adaptability, reinforcing the potential of Bisphosphonate-8 and Fenoldopam as effective tubulin dimer inhibitors. Bisphosphonate-8, demonstrating superior binding properties in silico, emerges as a promising candidate for preclinical evaluation in pancreatic cancer, offering translational potential for improved targeted therapy.\u003c/p\u003e","manuscriptTitle":"Identification of Novel Tubulin Dimer Inhibitors for Pancreatic Cancer: An Integrated Computational Approach","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-05 09:55:59","doi":"10.21203/rs.3.rs-8016981/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e2e06639-d93b-4730-bd62-d7ee7108c186","owner":[],"postedDate":"November 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-05T09:56:01+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-05 09:55:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8016981","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8016981","identity":"rs-8016981","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}