Rational design and comparative docking and simulation of modified FLT3 inhibitors: A study on enhanced binding stability and inhibition potency

Rational design and comparative docking and simulation of modified FLT3 inhibitors: A study on enhanced binding stability and inhibition potency | 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 Rational design and comparative docking and simulation of modified FLT3 inhibitors: A study on enhanced binding stability and inhibition potency Uddalak Das, Dheemanth Regati, R. Sowdhamini, Jitendra Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7508862/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The FLT3 protein is a well-established therapeutic target for treating Acute Myeloid Leukemia (AML), with its inhibition playing a crucial role in disease management. In this study, we identify and propose a novel FLT3 inhibitor that demonstrates superior binding affinity, stability, and pharmacokinetic properties compared to currently available inhibitors. We initially characterized the binding interactions of known FLT3 inhibitors through molecular docking and then strategically modified functional groups to enhance binding affinity, optimize drug-likeness, and minimize toxicity. The resulting analogue exhibits improved metabolic stability, lower toxicity, higher intestinal absorption, and superior permeability. Molecular dynamics simulations further confirm that the novel inhibitor forms stable and persistent interactions with FLT3, as evidenced by reduced conformational fluctuations and compact structural integrity. Free energy calculations reveal stronger ligand stabilization, while dynamic correlation analysis suggests enhanced engagement with critical residues, reinforcing its potential as an effective therapeutic agent. These findings highlight a promising candidate for further experimental validation and potential development in AML treatment. Acute myeloid leukemia FLT3 pharmacophore molecular docking ADMET molecular dynamic simulation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 1. INTRODUCTION Acute myeloid leukemia (AML) stands as a aggressive hematologic malignancy making up 80% of all adult leukemia cases [ 1 ]. According to recent statistics, there were approximately 20,380 new cases of AML and 11,310 associated deaths in the United States in the year 2023 [ 2 , 3 ]. These figures underscore the continued significance of addressing and advancing research, diagnosis, and treatment options for AML to improve patient outcomes and reduce mortality rates. Originating from cytogenetic aberrations within bone marrow hematopoietic cells, AML manifests as a clonal transformation of immature myeloblasts, impacting blood cell formation and differentiation [ 4 ]. The primary focus of therapeutic investigations has honed in on the intricate molecular landscape of AML, with specific attention directed towards the family of receptor tyrosine kinase FMS-like tyrosine kinase 3 (FLT3). FLT3, a class III protein tyrosine kinase, orchestrates essential signalling cascades critical for hematopoietic regulation [ 5 , 6 ]. The receptor, composed of five immunoglobulin-like extracellular domains, a transmembrane domain, and two intracellular tyrosine kinase domains, plays a crucial role in immunological responses and hematopoietic stem cell proliferation [ 7 ]. Mutations within the FLT3 gene manifest in around 30% of AML instances, with the internal tandem duplication (ITD) constituting the predominant subtype (FLT3-ITD; approximately 25% of AML cases). Additionally, mutations within the tyrosine kinase domain (FLT3-TKD) are observed in a subset of AML cases, accounting for approximately 7–10% of occurrences. These mutations impart constitutive activation to FLT3, resulting in unregulated expansion of malignant white blood cells [ 8 ]. The emergence of FLT3 mutations is identified as a critical driver in the pathogenesis of AML, elucidating the importance of FLT3 as a therapeutic target [ 9 ]. The classification of FLT3 inhibitors into first and next generations, based on potency and specificity, signifies a strategic evolution in drug design [ 10 ]. The landscape of FLT3 inhibitors encompasses both type I and type II inhibitors, with the latter displaying distinct advantages in targeting the hydrophobic pocket adjacent to the ATP-binding site in the inactive DFG-out conformation [ 11 ]. Clinical trials involving the first generation FLT3 inhibitors demonstrated limited efficacy due to issues of kinase selectivity [ 12 , 13 ]. The second generation, containing potent inhibitors like Gilteritinib and Quizartinib , offered heightened specificity and selectivity [ 14 ]. Despite advancements in the field, the rapid emergence of resistance, coupled with the inadequate binding affinity, instability within the active site, and insufficient potency of several experimentally validated FLT3 ligands identified as potential FLT3 inhibitors, has hindered their clinical success [ 15 ]. In silico drug discovery has revolutionized therapeutics by overcoming the limitations of traditional experimental methods [ 16 ]. Promising lead identification conventionally involves costly and time-consuming high-throughput screening (HTS) [ 17 , 18 ]. The drug discovery process, taking up to 14 years and costing around $ 800 million, faces challenges due to failures in clinical trials [ 19 – 21 ]. Rational drug design, or reverse pharmacology, is a cost-effective approach that begins with identifying target proteins and screening small-molecule libraries [ 22 ]. Structure-Based Drug Design optimizes lead discovery by utilizing target protein 3D structures and disease insights, employing techniques such as structure-based virtual screening (SBVS), molecular docking, and molecular dynamics (MD) simulations [ 23 ]. Due to the complexities of target identification, drug design, and screening in AML drug development, computational methods are essential [ 24 ]. In this study, we focuses on the rational design and optimization of the existing inhibitors. By modulating the top-performing compounds, we aim to enhance their binding affinity to key active-site residues, improve their stability, and ultimately bridge the gap between experimental validation and clinical efficacy, paving the way for more effective FLT3-targeted therapies in AML. 2. MATERIALS AND METHODS This study aims to optimize existing FLT3 inhibitors for AML by enhancing binding affinity, stability, and clinical efficacy, with the overall workflow illustrated in Fig. 1 . 2.1. Computational systems A CentOS Linux 7 workstation with 31.1 GiB memory, Intel ® Xeon ® E-2234 CPU with 3.60 GHz x 8 processor, and Quadro p1000/PCle/SSE2 graphics was used. Schrödinger release 2023-1 was used for all computational purposes. 2.2. Dataset generation and screening For the screening, all compounds exhibiting inhibitory activity against FLT3 were selected from the ZINC [ 25 ], BindingDB [ 26 ], and PubChem databases, comprising 1,050, 6,449, and 5,513 compounds respectively. These compounds were prepared using LigPrep [ 27 ], resulting in a total of 33,946 molecules. The ligands were then screened based on Lipinski’s Rule of Five (Ro5) [ 28 ] using the QikProp module of Schrodinger. 26,423 compounds showing zero Ro5 violations were kept for further studies. 2.3. Molecular Interactions 2.3.1. Protein preparation The X-ray crystallographic structure of the FLT3 target protein [PDB ID: 6JQR], in complex with Gilteritinib [ 29 ] was used in the study. Using the protein setup tool in Schrödinger Maestro, the structure underwent initial processing via the PROPKA component to refine hydrogen bond orientations.[ 30 ] The structures underwent refinement to ensure the alignment of heavy atoms within an RMSD threshold of 0.3Å, employing the OPLS4 force field. Following this, water molecules situated beyond a 5Å distance from the ligands were eliminated. [ 31 ]. 2.3.2. Receptor grid generation The receptor grid was constructed by centering it on both the hydrophobic domain and the binding site of Gilteritinib within the complex. The specified spatial coordinates for the grid were X=-30.02, Y=-11.86, Z=-26.27, with a ligand dimension extending to 18Å. 2.3.3. Molecular docking Molecular docking was constrained to compounds containing a maximum of 100 freely rotating bonds and under 500 atomic constituents. The scaling coefficient for Van der Waals radii was adjusted to 0.80, while the threshold for partial charges was maintained at 0.15. Conformational adjustments included enabling nitrogen inversion sampling and structural flexibility within cyclic systems. The ligand conformation exploration was conducted under a flexible mode, ensuring prioritized torsional sampling for all anticipated functional moieties. The system was optimized to enhance internal hydrogen bonding and maintain the coplanarity of conjugated π-systems. Extra precision (XP) docking [ 32 ] utilized the Glide component within the Schrödinger suite. Top 5 compounds showing the highest docking score were considered for enumerations. 2.3.4. Ligand enumerations All the functional groups in the ligand that showed proximity to the protein were altered to 2383 other functional groups using the ligand designer [ 33 ] and R-group enumeration [ 34 ] modules of the Schrodinger Suite. 2.4. ADME-Tox analysis A comparative evaluation of drug kinetics, with a particular focus on assessing the ADMET characteristics of the canonical ligand and its modification, identified from the ligand R-group enumeration, utilized multiple software platforms, including the QikProp module of Schrödinger and SwissADME [ 35 ], ProTox-3.0 [ 36 ], ADMETlab 3.0 [ 37 ], and the pkCSM server [ 38 ]. SwissADME, pkCSM, and ProTox-3.0 are online platforms that facilitate the estimation of pharmacokinetic attributes, determine drug-likeness, and analyze the appropriateness of small compounds for medicinal applications. 2.5. Molecular dynamics simulation studies The molecular dynamics simulations for the FLT3 protein complex, along with its canonical ligand and altered form, were executed utilizing the Desmond software suite. Every system was independently embedded within a 10 Å orthorhombic aqueous environment, employing the TIP3P solvation framework. [ 39 ]. The OPLS4 force field was employed to construct the ligand-protein complexes [ 40 ]. Sodium counterions were incorporated into the protein-ligand complexes to balance the overall system charge during molecular dynamics simulations. Additionally, energy minimization was performed for 2000 iterations to achieve the lowest possible energy state before commencing the simulation within an NPT ensemble framework. [ 41 ]. Once the system reached equilibrium, a production phase was carried out under conditions of 310.15 K temperature and 1 atm pressure for a duration of 500 ns to assess its stability in a water-based medium. Various assessments related to structure and stability were performed on the obtained molecular dynamics trajectories, including root mean square deviation (RMSD), root mean square fluctuation (RMSF), and gyration radius (Rg). To determine the extent of protein exposure to the surrounding solvent during the simulation, the solvent-accessible surface area (SASA) was computed. The graphical representations were generated using Matplotlib Library 3.10.0 [ 42 ] in using in-house python scripts. 2.6. Binding free energy distribution The trajectory files generated from the MD simulations were examined utilizing visualization tools scripted in VMD. To investigate principal component patterns and assess the free energy landscape (FEL), the covariance matrix underwent computation followed by diagonalization. FEL, along with MM-GBSA, facilitated the identification of interactions that remained both stable and unvarying. 2.6.1. Free energy landscape (FEL) The FEL was formulated as: \(\:F\left(X\right)={-k}_{B}TlnP\left(X\right)\) , where F(X) represents the free energy, k B is the Boltzmann constant, T is the absolute temperature, and P(X) corresponds to the probability density function of the molecular system along the primary coordinate. The 2D and 3D FEL were constructed to analyze conformational transitions using RMSD, radius of gyration (Rg), and other parameters. For FEL calculations, RMSD and Rg values were projected onto a plane, with population densities estimated via kernel density estimation, and visualized as contour maps in Python [ 42 ]. 2.6.2. Molecular Mechanics-Generalized Born Surface Area The prime module was employed to estimate the energetic factors derived from the MM-GBSA analysis, determining the extent of stabilization energy resulting from possible molecular interactions. The VSGB solvation model [ 43 ] was used and the force field was set as OPLS4. Binding free energy evaluations utilizing the MM-GBSA approach were performed on molecular dynamics simulation trajectories extending up to 500 ns. In order to identify protein-ligand complexes, this method proves to be more reliable for assessing binding affinities compared to glide score estimations. Important energetic factors, including hydrogen bonding contribution (ΔG Bind_Hbond ), solvation-associated electrostatic energy (ΔG Bind_Solv ), electrostatic or Coulombic interaction energy (ΔG Bind_Coul ), hydrophobic interaction contribution (ΔG Bind_Lipo ), and van der Waals interaction energy (ΔG Bind_vdW ), were collectively incorporated to estimate the relative binding affinity using the MM-GBSA framework. 3. RESULTS 3.1. The FLT3 protein The FLT3 protein consists of 993 amino acids and has a molecular weight of 112.903 Da. It is composed of three distinct regions: an extracellular segment containing 516 residues, a transmembrane portion made up of 19 amino acids, and a cytoplasmic region comprising 429 amino acids.[ 44 ]. To determine the structure of 6JQR which is in the cytoplasmic domain, the PDBsum web server was utilized [ 45 ]; this study revealed 15 α-helices, 6 β-hairpins, 4 β-bulges, and 12 interactions between helices. (see Fig. 2 ). The 6JQR structure underwent validation through the Ramachandran diagram, revealing that 93.5% of its amino acid residues occupied favored zones, 6.5% were found in permitted areas, none resided in permissible but less favorable sections, and 0.9% fell into restricted zones, with an overall G-Factor of 0.00. [ 46 ]. (see Supplementary Figure S1 ) 3.2. Molecular docking studies The docking assessments for the top 5 compounds, i.e., CHEMBL3403451 [ 47 ], UNC2124A (Patent ID: US10004755B2), CHEMBL3403450 [ 48 ], CHEMBL4577991 [ 47 ] and CHEMBL4444839 [ 47 ] yielded scores between − 13.675 kcal/mol and − 12.500 kcal/mol as a result of the XP docking procedure. These compounds were selected for modification for enhanced binding affinity and stability. The 2D and 3D interaction diagram of these ligands within the FLT3 binding site ( Supplementary Figure S2 ). 3.3. Ligand enumerations We performed ligand enumeration of all the top 5 docked compounds from the XP docking. We set a parameter of a Δ docking score of 2 kcal/mol from the original docking score of the canonical compounds. The first four compounds did not produce any analogue with enhanced binding affinity compared to their canonical versions. However, CHEMBL4444839 generated multiple analogues with a Δ docking score of 2 kcal/mol. The best-scoring analogue of CHEMBL4444839 exhibited a key modification—the incorporation of a fluorocyclobutane moiety ( Fig. 3 ) . See Supplementary Table S4 for 2D and 3D structures of CHEMBL4444839 and its analogue. Interestingly, the binding affinity of the best CHEMBL4444839 analogue exhibited an enhanced docking score of more than 1 kcal/mol compared to the best compound, CHEMBL3403451 (-13.675 kcal/mol). CHEMBL3403451 and its analogue were evaluated for their comparative pharmacokinetic and other properties. See Supplementary Figure S5 for the ligand torsions diagram encapsulating the structural transformation of each flexible bond within the ligands, providing insights into their flexibility and binding adaptability. 3.4. ADME-Tox predictions For this research, we employed the online platforms - SwissADME, ADMETlab 2.0, ProTox-II, and pkCMS to assess the absorption, distribution, metabolism, excretion, and toxicity characteristics of CHEMBL3403451 along with its structural variant. (Fig. 4 ). With a toxicity class of 6 compared to class 4 for CHEMBL4444839, the analogue exhibits lower acute toxicity, supported by a much higher predicted LD50 of 5500 mg/kg versus 420 mg/kg. Unlike CHEMBL4444839, which is AMES toxic, the analogue is not, reducing its mutagenicity risk. The analogue also shows better systemic tolerance, with a higher maximum tolerated dose (0.496 vs. 0.4). While both compounds exhibit similar absorption profiles, the analogue demonstrates slightly improved Caco-2 permeability (0.55 nm/sec vs. 0.539 nm/sec) and human intestinal absorption (78.041% vs. 77.745%). The analogue excels in distribution characteristics, with a higher volume of distribution (1.324 log L/kg vs. 1.053) and lower plasma protein binding (93% vs. 96.5%), enhancing its bioavailability and pharmacodynamic activity. The metabolic profiles differ, with the analogue being a CYP2D6 substrate, suggesting a broader metabolic pathway, while both compounds interact similarly with key CYP enzymes. The analogue has a marginally higher total clearance rate (1.277 log ml/min/kg vs. 1.223), indicating faster elimination. Importantly, CHEMBL4444839-Analogue has reduced toxicity risks, including the absence of AMES toxicity, lower Pyriformis toxicity (µg/L = 0.285 vs. 0.286), and lower minnow toxicity (log mM = -0.102 vs. 0.656), suggesting better environmental safety. Supplementary Tables S1, S2, and S3 offers comprehensive comparative pharmacokinetic and toxicity assessments of CHEMBL4444839 and its analogue. 3.5. Molecular Dynamics Simulation The MD trajectories were compared in terms of their RMSD, RMSF, Radius of Gyration, the protein ligand contacts, and the SASA values (Fig. 5 ). CHEMBL4444839 appears to allow greater fluctuations in RMSD of the protein compared to the analogue bound structure, suggesting the increased stability of the protein when complexed with the analogue (Panel A). The Residue Mean Square Fluctuation (RMSF) plot provides valuable insights into the dynamic flexibility of the FLT3 protein residues when complexed with two ligands: CHEMBL4444839 and its analogue. Certain regions which are responsible for the activity of FLT-3 such as Juxta Membrane (JM) loop (579–594), ATP binding site (616–664), the active site (807–819), and other conserved sites show reduction in the RMSF in the analogue bound structure, which appears to indicate that the analogue stabilizes the structure more efficiently compared to the original ligand (Panel D). Supplementary Figure S4 shows the ligand RMSF analysis indicating positional fluctuations of ligand atoms, providing insights into their entropic contributions and interactions with FLT3. The RMSF analysis of FLT3 regions reveals distinct ligand-induced stability patterns. In the JM loop (579–594), CHEMBL4444839-Analogue exhibits reduced fluctuations compared to CHEMBL4444839, indicating enhanced stabilization, crucial for FLT3 activation control [ 49 ]. At the ATP binding site (616–664), both ligands show low RMSF, but the analogue displays a slight reduction, suggesting stronger interactions with ATP-critical residues, enhancing inhibition. The conserved site (668–682) shows a fluctuation peak, but lower RMSF for the analogue implies stronger binding and structural integrity. In the active site (807–819), the analogue stabilizes the region more effectively, crucial for enzymatic inhibition. The DFG-out region (829–845) also shows reduced fluctuations with the analogue, suggesting enhanced stabilization of the inhibitory state (see Fig. 6 ). High fluctuation peaks (~ 45, 125, 240 residues) indicate flexible regions, with the analogue moderating these movements, reinforcing overall protein stability. Interaction analysis (Panel A) shows CHEMBL4444839 forms strong H-bonds (GLU 661, 82%; CYS 694, 72%) and hydrophobic interactions (PHE 621, 55%; LEU 818, 98%), with moderate water-bridging contributions. Panel B highlights dominant hydrophobic interactions, particularly at LEU 818 and PHE 621, with minimal ionic interactions. The analogue exhibits increased interaction duration and novel contacts (e.g., PHE 830), suggesting superior FLT3 inhibition. The timeline representation of interactions in Supplementary Figure S3 further illustrates the stability and duration of these contacts, revealing how the protein maintains ligand interactions across the simulation trajectory. Towards the end of the simulation, radius of gyration (Rg) marginally increases for the structure bund with CHEMBL4444839, suggesting the stability of the protein could potentially be better with the analogue bound. Initially, CHEMBL4444839 exhibits a higher Rg (~ 6.5 Å) than the analogue (~ 6 Å), indicating a less compact starting state. Both systems stabilize by 20–30 ns, with the analogue maintaining a lower Rg (~ 5 Å) compared to CHEMBL4444839 (~ 5.2 Å) throughout 50–400 ns, suggesting enhanced structural integrity. The analogue’s Rg trajectory is smoother, implying reduced instability. In the final phase (400–500 ns), CHEMBL4444839’s Rg rises beyond 6 Å, indicating destabilization, while the analogue remains stable (~ 5 Å), suggesting superior long-term stabilization [ 50 ] (Panel C). To further assess the dynamic behavior of the ligand-protein complexes, we analyzed the variations in RMSD, rGyr, MolSA, intraHB, SASA, and PSA over 500 ns of MD simulation time ( Supplementary Figure S6 ), which provide insights into the stability and compactness of the system. SASA analysis shows dynamic ligand-protein interactions. CHEMBL4444839 maintains lower SASA values (50–150 Ų), with minimal spikes (< 200 Ų), whereas the analogue exhibits higher SASA (100–200 Ų) with pronounced peaks, suggesting greater structural rearrangements. In 100–300 ns, the analogue shows significant fluctuations, reflecting dynamic binding, while CHEMBL4444839 remains more rigid. In 300–500 ns, the analogue sustains elevated SASA, indicating persistent interaction, whereas CHEMBL4444839 retains lower SASA, implying more restricted binding dynamics [ 51 ]. These findings highlight the analogue’s superior protein stabilization and dynamic binding properties, which may enhance efficacy (Panel E). 3.6. Binding free energy distributions 3.6.1. Free energy landscape (FEL) In Fig. 7 , Panels (A) and (C) represent CHEMBL4444839, while (B) and (D) depict its analogue. Each FEL plots free energy against RMSD and Rg. For CHEMBL4444839, the 2D FEL (A) shows a primary minimum at RMSD = 0.5-1.0 Å and Rg = 5.75-6.0 Å (~ 2.85 kcal/mol), stabilizing FLT3. A secondary basin at Rg ~ 6.2 Å suggests an alternative, less stable conformation. High-energy regions (RMSD > 2.0 Å) indicate destabilization. The 3D FEL (C) confirms a deep energy well at the primary minimum and sharp peaks for high RMSD, indicating conformational penalties. The energy range spans 2.85–5.28 kcal/mol, suggesting moderate flexibility. For the analogue, the 2D FEL (B) shows a minimum at RMSD = 0.5-1.0 Å and Rg = 6.0-6.25 Å (~ 2.61 kcal/mol), indicating better stabilization than the parent compound. A broader low-energy basin suggests higher conformational adaptability, with fewer high-energy states. The 3D FEL (D) reveals a deeper, smoother energy surface, supporting enhanced stability across a wider conformational space. The energy range (2.61–5.31 kcal/mol) highlights its superior ability to minimize unfavorable states. 3.6.2. Molecular Mechanics Generalized Born Surface Area (MM-GBSA) The binding free energy distributions of FLT3 in complex with CHEMBL4444839 and its analogue were analysed over a molecular dynamics simulation (Fig. 7 (A) ). The ΔG values (kcal/mol) over time (ns) reveal distinct binding behaviours ( Supplementary Table S5 & S6 ). CHEMBL4444839 (red line) exhibits greater fluctuations, with peaks exceeding − 60 kcal/mol, indicating transient weaker binding. In contrast, the analogue (green line) maintains a more stable profile, rarely exceeding − 70 kcal/mol, suggesting stronger, more consistent binding. CHEMBL4444839 shows a broader ΔG range (-100 to -60 kcal/mol), reflecting variability, whereas the analogue remains within − 100 to -70 kcal/mol, indicating stability. Temporal analysis reveals erratic fluctuations for CHEMBL4444839, while the analogue stabilizes over time, suggesting durable interactions. The analogue’s lower average ΔG and smoother fluctuations indicate superior binding affinity and stability, likely due to structural modifications enhancing hydrogen bonding, hydrophobic interactions, or π-π stacking. These properties suggest its potential as a more reliable FLT3 inhibitor. 3.7. Trajectory analysis 3.7.1. Principal component analysis (PCA) The PCA plots (Fig. 8 ) illustrate the conformational dynamics of FLT3 in complex with CHEMBL4444839 (Panel A) and its analogue (Panel B). Panel A exhibits greater dispersion, indicating higher conformational flexibility and structural variability, suggesting weaker and less stable binding. Conversely, Panel B shows tighter clustering, signifying a more stable complex with restricted conformational sampling. Temporal progression in Panel A is erratic, reflecting transient binding, while Panel B exhibits smoother transitions, indicating sustained stability. The broader distribution in Panel A suggests non-specific interactions, whereas Panel B’s confined range implies strong stabilization of FLT3 in a biologically relevant state. 3.7.2. Dynamic cross-correlation matrix (DCCM) In Fig. 8 , both CHEMBL4444839 (Panel C) and its analogue (Panel D) maintain self-correlation integrity, validating DCCM computation. However, Panel D exhibits a sharper diagonal, indicating more localized protein dynamics. Positive correlations in Panel C are weaker and dispersed (50–100, 150–200, ~ 250), while Panel D shows stronger, structured correlations (100–150, 200–250), enhancing FLT3 stability. Negative correlations are more extensive in Panel D, particularly between 50–100 and 150–200, suggesting enhanced dynamic adaptability. Panel C displays scattered correlations, whereas Panel D induces structured, functionally relevant allosteric effects. The analogue stabilizes FLT3 more effectively, promoting enhanced allosteric signaling and residue communication, particularly in the 50–100 and 150–200 regions, supporting its superior binding and dynamic impact over CHEMBL4444839. 4. DISCUSSION The comparative analysis between CHEMBL4444839 and its analogue strongly suggests that the latter exhibits superior stability and binding characteristics with the FLT3 protein. The smoother and more consistent RMSD trends of the analogue indicate more stable interactions, potentially due to improved binding affinity and reduced conformational fluctuations. The analogue’s lower RMSF values across critical regions further support its superior stabilization and potential for enhanced inhibition. Structurally, CHEMBL4444839 consists of a p-toluenesulfonyl (-SO₂-) group attached to a secondary sulfonamide (-SO₂NH-) functionality, which connects to a flexible alkyl linker (-NH-(CH₂)₄-NH₂). This linker bridges the sulfonamide moiety to the 1H-pyrrolo[2,3-b]pyridine core, an aromatic bicyclic system that likely contributes to binding through π-π stacking and hydrogen bonding interactions. In the best analogue, a significant structural modification is the incorporation of a fluorocyclobutane moiety at position 5 of the pyrrolopyrimidine ring. This modification introduces a four-membered cyclobutane ring substituted with a fluorine atom, which increases steric bulk and imposes conformational rigidity on the heterocycle. The presence of fluorine imparts an electron-withdrawing effect, potentially altering dipole moments and influencing interactions with nearby residues. Additionally, the p-toluenesulfonyl (-SO₂-) group and the flexible -(CH₂)₄-NH₂ alkyl linker remain unchanged, ensuring that key hydrogen bond donor-acceptor interactions in the parent compound are preserved. The interaction profile of the analogue reveals increased binding stability through additional interactions with key residues, such as PHE 830 [ 29 ], GLU 661 [ 52 ], and CYS 694 [ 53 ], and optimized hydrophobic interactions with LEU 818 [ 54 ]. The analogue also maintains consistent water-bridging interactions, ensuring hydration dynamics remain intact while enhancing overall binding efficacy [ 55 ]. The lower Rg values for the analogue indicate a more compact and tightly bound complex, further emphasizing its superior stability over the original compound. Fluorocyclobutane enhances metabolic stability, lipophilicity, and binding interactions through conformational rigidity. The fluorine atom increases resistance to enzymatic oxidation [ 56 ], while the four-membered ring reduces metabolic cleavage susceptibility. It modulates lipophilicity by fine-tuning the hydrophilic-hydrophobic balance, optimizing LogP for membrane permeability and bioavailability—though aliphatic fluorination often reduces overall lipophilicity [ 57 ]. Fluorine’s high electronegativity enables dipole interactions, strengthening target binding [ 58 ], while the rigid cyclobutane ring locks the molecule into a bioactive conformation, minimizing entropic penalties [ 59 ]. These combined effects might enhance stability, solubility, and binding affinity, contributing to improved docking performance and potential biological activity. Free energy calculations reveal a deeper free energy minimum (~ 2.61 kcal/mol) for the analogue compared to the parent compound (~ 2.85 kcal/mol), indicating stronger stabilization of the FLT3 protein-ligand complex. The analogue’s energy landscape is narrower, reducing entropic penalties and off-target interactions while improving binding affinity [ 60 ]. Cluster and dynamic stability analyses confirm that the analogue stabilizes the FLT3 structure more effectively than CHEMBL4444839, potentially improving therapeutic efficacy. The analogue’s interactions restrict protein motion, stabilizing it in a biologically favorable conformation [ 61 ]. DCCM analysis further demonstrates that the analogue better engages critical residues, promoting a more cohesive and functional dynamic state in FLT3. Overall, the analogue outperforms CHEMBL4444839 in stability, binding strength, and adaptability, making it a more promising candidate for further development as a potent FLT3 inhibitor. Further experimental validation and molecular studies are recommended to confirm these findings and optimize the analogue’s therapeutic potential. 5. CONCLUSION Our study presents a structurally optimized FLT3 inhibitor that exhibits superior binding affinity, stability, and pharmacokinetic properties compared to CHEMBL4444839, positioning it as a promising candidate for AML therapy. The incorporation of a fluorocyclobutane moiety significantly enhances ligand-protein interactions through increased hydrogen bonding, electrostatic stabilization, and hydrophobic engagement with key FLT3 residues. Molecular dynamics simulations and free energy calculations confirm the analogue’s reduced conformational fluctuations, lower entropic penalties, and stronger protein-ligand stabilization, while DCCM analysis reveals improved allosteric communication within FLT3. Furthermore, favorable ADME and toxicity assessments indicate enhanced metabolic stability, membrane permeability, and drug-likeness. These findings provide compelling computational evidence for the analogue’s therapeutic potential, warranting further in vitro and in vivo validation to assess its efficacy and translational applicability in AML treatment. Declarations Acknowledgement The authors thank NCBS, Bangalore for providing computational support. The authors would like to thank NCBS (TIFR) for infrastructural facilities. This work was supported by the Department of Atomic Energy, Government of India, Project Identification No. R TI 4006. RS is a J.C. Bose National Fellow (JBR/2021/000006) from the Science and Engineering Research Board, India. RS would also like to thank Bioinformatics Centre Grant funded by the Department of Biotechnology, India (BT/PR40187/BTIS/137/9/2021) and the Institute of Bioinformatics and Applied Biotechnology for the funding through her Mazumdar-Shaw Chair in Computational Biology (IBAB/MSCB/182/2022). A warm heartfelt thanks to the staff and administration at the NCBS, Bangalore for their support. We would also like to thank Dr. Abhijit Kayal, Senior Scientist II at Schrödinger, for his assistance with Free Energy Landscape (FEL) calculations and for his support with the Python scripts used in the analysis. Declaration of Competing Interest The authors report no conflict of interest. Ethics Statement This research did not involve any human participants or animal subjects, and therefore, no ethical approval was required. Funding Statement This research has received funding from the Department of Biotechnology (DBT) Human Resource Development (HRD), Ministry of Science and Technology, Government of India. ORCID Uddalak Das: 0009-0004-0007-5691 Author Contributions Uddalak Das : Conceptualization, Methodology, Formal Analysis, Writing—Original Draft, Writing—Review & Editing. Dheemanth Regati : Conceptualization, Methodology, Formal Analysis, Writing—Original Draft, Writing—Review & Editing. R. Sowdhamini : Supervision, Project Administration. Jitendra Kumar : Supervision. All the authors have read and agreed to the published version of the manuscript. Declaration of generative AI and AI-assisted technologies in the writing process The writing of this research paper involved the use of generative AI and AI-assisted technologies only to enhance the clarity, coherence, and overall quality of the manuscript. The authors acknowledges the contributions of AI in the writing process while ensuring that the final content reflects the author's own insights and interpretations of the literature. All interpretations and conclusions drawn in this manuscript are the sole responsibility of the author. References Vakiti A, Mewawalla P. Acute Myeloid Leukemia. StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 [cited 2024 Feb 20]. 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Lu C, Wu C, Ghoreishi D, et al . OPLS4: Improving Force Field Accuracy on Challenging Regimes of Chemical Space. J Chem Theory Comput. 2021;17(7):4291–4300. Al-Jumaili MHA, Siddique F, Abul Qais F, et al . Analysis and prediction pathways of natural products and their cytotoxicity against HeLa cell line protein using docking, molecular dynamics and ADMET. J Biomol Struct Dyn. 2023;41(3):765–777. Elhady SS, Abdelhameed RFA, Malatani RT, et al . Molecular Docking and Dynamics Simulation Study of Hyrtios erectus Isolated Scalarane Sesterterpenes as Potential SARS-CoV-2 Dual Target Inhibitors. Biology. 2021;10(5):389. Hess B, Kutzner C, Van Der Spoel D, et al . GROMACS 4: Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation. J Chem Theory Comput. 2008;4(3):435–447. Pan Y, Lu Z, Li C, et al . Molecular Dockings and Molecular Dynamics Simulations Reveal the Potency of Different Inhibitors against Xanthine Oxidase. ACS Omega. 2021;6(17):11639–11649. Hunter JD. Matplotlib: A 2D Graphics Environment. Comput Sci Eng. 2007;9(3):90–95. Li J, Abel R, Zhu K, et al . The VSGB 2.0 model: A next generation energy model for high resolution protein structure modeling. Proteins Struct Funct Bioinforma. 2011;79(10):2794–2812. Ouassaf M, Daoui O, Alam S, et al . Pharmacophore-based virtual screening, molecular docking, and molecular dynamics studies for the discovery of novel FLT3 inhibitors. J Biomol Struct Dyn. 2023;41(16):7712–7724. Laskowski RA, Jablonska J, Pravda L, et al . PDBsum: Structural summaries of PDB entries. Protein Sci. 2018;27(1):129–134. Ramachandran GN, Ramakrishnan C, Sasisekharan V. Stereochemistry of polypeptide chain configurations. J Mol Biol. 1963;7(1):95–99. Grimm SH, Gagestein B, Keijzer JF, et al . Comprehensive structure-activity-relationship of azaindoles as highly potent FLT3 inhibitors. Bioorg Med Chem. 2019;27(5):692–699. McElroy WT, Michael Seganish W, Jason Herr R, et al . Discovery and hit-to-lead optimization of 2,6-diaminopyrimidine inhibitors of interleukin-1 receptor-associated kinase 4. Bioorg Med Chem Lett. 2015;25(9):1836–1841. Griffith J, Black J, Faerman C, et al . The Structural Basis for Autoinhibition of FLT3 by the Juxtamembrane Domain. Mol Cell. 2004;13(2):169–178. Abdullah A, Biswas P, Sahabuddin Md, et al . Molecular Dynamics Simulation and Pharmacoinformatic Integrated Analysis of Bioactive Phytochemicals from Azadirachta indica (Neem) to Treat Diabetes Mellitus. Marc (Vlaic) RA, editor. J Chem. 2023;2023:1–19. Hassan AM, Gattan HS, Faizo AA, et al . Evaluating the Binding Potential and Stability of Drug-like Compounds with the Monkeypox Virus VP39 Protein Using Molecular Dynamics Simulations and Free Energy Analysis. Pharmaceuticals. 2024;17(12):1617. Kang Y, Tiziani S, Park G, et al . Cellular protection using Flt3 and PI3Kα inhibitors demonstrates multiple mechanisms of oxidative glutamate toxicity. Nat Commun. 2014;5(1):3672. Kesarwani M, Huber E, Azam M. Overcoming AC220 resistance of FLT3-ITD by SAR302503. Blood Cancer J. 2013;3(8):e138–e138. Shi K, Hong Y, Liu H, et al . Discovery of novel and highly potent small molecule inhibitors targeting FLT3-ITD for the treatment of acute myeloid leukemia using structure-based virtual screening and biological evaluation. Front Pharmacol. 2025;16:1511257. Ahmad M, Gu W, Geyer T, et al . Adhesive water networks facilitate binding of protein interfaces. Nat Commun. 2011;2(1):261. Richardson P. Applications of fluorine to the construction of bioisosteric elements for the purposes of novel drug discovery. Expert Opin Drug Discov. 2021;16(11):1261–1286. Jeffries B, Wang Z, Graton J, et al . Reducing the Lipophilicity of Perfluoroalkyl Groups by CF 2 –F/CF 2 –Me or CF 3 /CH 3 Exchange. J Med Chem. 2018;61(23):10602–10618. Henary E, Casa S, Dost TL, et al . The Role of Small Molecules Containing Fluorine Atoms in Medicine and Imaging Applications. Pharm Basel Switz. 2024;17(3):281. van der Kolk MR, Janssen MACH, Rutjes FPJT, et al . Cyclobutanes in Small-Molecule Drug Candidates. ChemMedChem. 2022;17(9):e202200020. Tse C, Wickstrom L, Kvaratskhelia M, et al . Exploring the Free-Energy Landscape and Thermodynamics of Protein-Protein Association. Biophys J. 2020;119(6):1226–1238. Takagi F, Koga N, Takada S. How protein thermodynamics and folding mechanisms are altered by the chaperonin cage: Molecular simulations. Proc Natl Acad Sci. 2003;100(20):11367–11372. Additional Declarations No competing interests reported. Supplementary Files SupplimentaryInformation.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Sep, 2025 Editor assigned by journal 09 Sep, 2025 Submission checks completed at journal 09 Sep, 2025 First submitted to journal 01 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7508862","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512898578,"identity":"81dd34f9-eb30-4b63-b02d-8d62051e4665","order_by":0,"name":"Uddalak Das","email":"","orcid":"","institution":"University of Agricultural Sciences, Bangalore (UAS-B)","correspondingAuthor":false,"prefix":"","firstName":"Uddalak","middleName":"","lastName":"Das","suffix":""},{"id":512898583,"identity":"32ebe95e-bc30-4c2f-acc0-1cd6ce588e02","order_by":1,"name":"Dheemanth Regati","email":"","orcid":"","institution":"Tata Institute of Fundamental Research (TIFR)","correspondingAuthor":false,"prefix":"","firstName":"Dheemanth","middleName":"","lastName":"Regati","suffix":""},{"id":512898586,"identity":"3190d0ac-5dd1-4472-82f6-953bec1a2f56","order_by":2,"name":"R. Sowdhamini","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYDACZgaGAx8YGBKgXAnitBycQZoWkC4ehBYigG4778HDtjvs8uQbeB8+/MFgkUdQi9lhvoTDuWeSiw0OsBsb8zBIFBOhhcfgcG7bgcQNDGxs0kC/JDYQpcUSqGV+Axv7zx9Ea2EEamk4wMbGwEOsloO9bUC/HGZjluYxIEbL+TPGH362AUOsvY3x44+KOsJaEIAZRBgQr34UjIJRMApGAR4AABh1NrPw5HosAAAAAElFTkSuQmCC","orcid":"","institution":"Tata Institute of Fundamental Research (TIFR)","correspondingAuthor":true,"prefix":"","firstName":"R.","middleName":"","lastName":"Sowdhamini","suffix":""},{"id":512898590,"identity":"652f15a4-02b2-436b-b61d-a368a19e6723","order_by":3,"name":"Jitendra Kumar","email":"","orcid":"","institution":"Ministry of Science and Technology, Government of India","correspondingAuthor":false,"prefix":"","firstName":"Jitendra","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2025-09-01 13:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7508862/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7508862/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93619019,"identity":"9a05c51d-154b-4584-b29a-ac75ad1eeb2c","added_by":"auto","created_at":"2025-10-15 17:29:17","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":710055,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eA flowchart illustrating the workflow employed in this study.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7508862/v1/ef53451a3c7c081f3790d8b2.png"},{"id":93619318,"identity":"3838605e-7dbc-4b44-90dc-0e57ee3c0dc0","added_by":"auto","created_at":"2025-10-15 17:37:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":428947,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe three-dimensional conformation of the FLT3 kinase domain, spanning amino acids 610–943, exhibits distinct functional regions. A preserved segment (depicted in green) marks an essential zone for enzymatic function and molecular associations. The nucleotide attachment site (illustrated in blue) represents the locus where ATP molecules engage to trigger kinase activation. The catalytic region (shown in red) serves as the hub for phosphorylation processes, propagating intracellular signals. The DFG-Out segment (colored purple) contributes to modulating kinase functionality. Additionally, the juxtamembrane (JM) loop (highlighted in magenta) plays a pivotal role in spatial positioning and molecular interactions.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7508862/v1/b9b473d3bb6fd8f2188e46fd.png"},{"id":93619017,"identity":"28853b63-6a61-4bdd-974a-d760a80157e6","added_by":"auto","created_at":"2025-10-15 17:29:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":433409,"visible":true,"origin":"","legend":"\u003cp\u003eThe top 5 enumerations of the ligand CHEMBL4444839. The panels A-E shows the 2D interactions and the panels F-J shows the 3D interactions with FLT3 protein active site, with docking scores of -14. 833, -14.832, -14.743, -14.629, and -14. 629 kcal/mol respectively. The compound in green dotted square shows the best enumeration with the highest docking score.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7508862/v1/6b1534e6c72667e49e411137.png"},{"id":93619319,"identity":"f7dcb707-f269-403e-8a96-fe277da442f1","added_by":"auto","created_at":"2025-10-15 17:37:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":933713,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe Bioavailability Radar Plot illustrating the comparative drug-likeness of the two compounds. The blue area indicates the ideal range for each property, while the yellow dots represent the properties of the compounds. A: CHEMBL4444839 and B: CHEMBL4444839-analogue.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7508862/v1/a2c77926ad5c1b62ab520fed.png"},{"id":93619022,"identity":"04450a4d-3c30-4b6e-a3e7-549f925a89c3","added_by":"auto","created_at":"2025-10-15 17:29:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":313262,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eThe comparative trajectory assessment of FLT3 backbone and ligand complex during a 500 ns trajectory. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(A)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e RMSD of protein, \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(B)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e RMSD of ligand, (\u003c/em\u003e\u003cem\u003e\u003cstrong\u003eC)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Rg, \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(D)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e RMSF, and \u003c/em\u003e\u003cem\u003e\u003cstrong\u003e(E)\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eSASA.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7508862/v1/be1a1dccdd11e148fc4eacd0.png"},{"id":93619024,"identity":"d0fdec57-09e5-4fcd-b28a-036cee607a6e","added_by":"auto","created_at":"2025-10-15 17:29:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":774744,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eMolecular interactions of CHEMBL4444839 and its analogue in FLT3 binding pocket. Panels A and B depict the interactions for CHEMBL4444839, while Panels C and D illustrate those for the analogue.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image6.png","url":"https://assets-eu.researchsquare.com/files/rs-7508862/v1/049955c37f6fce6d6c5d7184.png"},{"id":93619020,"identity":"0aa7ce3a-5bee-4874-8172-f294764ebfd5","added_by":"auto","created_at":"2025-10-15 17:29:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":287541,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eComparative Free energy landscapes and binding free energy distributions of CHEMBL4444839 and its analogue. Panels A and B display 2D free energy landscapes projected onto RMSD and radius of gyration, revealing distinct conformational preferences. Panels C and D show 3D free energy landscapes, highlighting the energy minima associated with stable conformations. Panel E illustrates the MM-GBSA based binding free energy distributions over 500ns, demonstrating differences in binding affinity between the two molecules.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7508862/v1/262795dc6b4b6270ee516e39.png"},{"id":93620063,"identity":"4e6e85ae-cfc6-43a3-930d-03347ea45e0c","added_by":"auto","created_at":"2025-10-15 17:45:17","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":920334,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eA-B: PCA projections of molecular dynamics trajectories, color-coded by frame number, showing conformational clustering along PC1 and PC2. C-D: Residue-residue contact maps highlighting dynamic correlations, with colour intensity indicating the strength of interactions over the simulation, revealing key structural changes.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image8.png","url":"https://assets-eu.researchsquare.com/files/rs-7508862/v1/0081723d39d48ba97da73fd0.png"},{"id":93620311,"identity":"a3c7f364-f9c4-45fa-bc9a-0eee0f3ed580","added_by":"auto","created_at":"2025-10-15 17:53:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5629703,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7508862/v1/99b7b8cf-6b6a-4235-b2ca-1f963d3afad9.pdf"},{"id":93619025,"identity":"14a10033-825d-4f86-98cf-f2b67b575915","added_by":"auto","created_at":"2025-10-15 17:29:17","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":4246249,"visible":true,"origin":"","legend":"","description":"","filename":"SupplimentaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7508862/v1/86330f27cbde8fde91735b8a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Rational design and comparative docking and simulation of modified FLT3 inhibitors: A study on enhanced binding stability and inhibition potency","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eAcute myeloid leukemia (AML) stands as a aggressive hematologic malignancy making up 80% of all adult leukemia cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to recent statistics, there were approximately 20,380 new cases of AML and 11,310 associated deaths in the United States in the year 2023 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These figures underscore the continued significance of addressing and advancing research, diagnosis, and treatment options for AML to improve patient outcomes and reduce mortality rates. Originating from cytogenetic aberrations within bone marrow hematopoietic cells, AML manifests as a clonal transformation of immature myeloblasts, impacting blood cell formation and differentiation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The primary focus of therapeutic investigations has honed in on the intricate molecular landscape of AML, with specific attention directed towards the family of receptor tyrosine kinase FMS-like tyrosine kinase 3 (FLT3).\u003c/p\u003e\u003cp\u003eFLT3, a class III protein tyrosine kinase, orchestrates essential signalling cascades critical for hematopoietic regulation [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. The receptor, composed of five immunoglobulin-like extracellular domains, a transmembrane domain, and two intracellular tyrosine kinase domains, plays a crucial role in immunological responses and hematopoietic stem cell proliferation [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Mutations within the FLT3 gene manifest in around 30% of AML instances, with the internal tandem duplication (ITD) constituting the predominant subtype (FLT3-ITD; approximately 25% of AML cases). Additionally, mutations within the tyrosine kinase domain (FLT3-TKD) are observed in a subset of AML cases, accounting for approximately 7\u0026ndash;10% of occurrences. These mutations impart constitutive activation to FLT3, resulting in unregulated expansion of malignant white blood cells [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe emergence of FLT3 mutations is identified as a critical driver in the pathogenesis of AML, elucidating the importance of FLT3 as a therapeutic target [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The classification of FLT3 inhibitors into first and next generations, based on potency and specificity, signifies a strategic evolution in drug design [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The landscape of FLT3 inhibitors encompasses both type I and type II inhibitors, with the latter displaying distinct advantages in targeting the hydrophobic pocket adjacent to the ATP-binding site in the inactive DFG-out conformation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eClinical trials involving the first generation FLT3 inhibitors demonstrated limited efficacy due to issues of kinase selectivity [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The second generation, containing potent inhibitors like \u003cem\u003eGilteritinib\u003c/em\u003e and \u003cem\u003eQuizartinib\u003c/em\u003e, offered heightened specificity and selectivity [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Despite advancements in the field, the rapid emergence of resistance, coupled with the inadequate binding affinity, instability within the active site, and insufficient potency of several experimentally validated FLT3 ligands identified as potential FLT3 inhibitors, has hindered their clinical success [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003cem\u003eIn silico\u003c/em\u003e drug discovery has revolutionized therapeutics by overcoming the limitations of traditional experimental methods [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Promising lead identification conventionally involves costly and time-consuming high-throughput screening (HTS) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The drug discovery process, taking up to 14 years and costing around \u003cspan\u003e$\u003c/span\u003e800\u0026nbsp;million, faces challenges due to failures in clinical trials [\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Rational drug design, or reverse pharmacology, is a cost-effective approach that begins with identifying target proteins and screening small-molecule libraries [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Structure-Based Drug Design optimizes lead discovery by utilizing target protein 3D structures and disease insights, employing techniques such as structure-based virtual screening (SBVS), molecular docking, and molecular dynamics (MD) simulations [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Due to the complexities of target identification, drug design, and screening in AML drug development, computational methods are essential [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn this study, we focuses on the rational design and optimization of the existing inhibitors. By modulating the top-performing compounds, we aim to enhance their binding affinity to key active-site residues, improve their stability, and ultimately bridge the gap between experimental validation and clinical efficacy, paving the way for more effective FLT3-targeted therapies in AML.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cp\u003eThis study aims to optimize existing FLT3 inhibitors for AML by enhancing binding affinity, stability, and clinical efficacy, with the overall workflow illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Computational systems\u003c/h2\u003e\u003cp\u003eA CentOS Linux 7 workstation with 31.1 GiB memory, Intel \u0026reg; Xeon \u0026reg; E-2234 CPU with 3.60 GHz x 8 processor, and Quadro p1000/PCle/SSE2 graphics was used. Schr\u0026ouml;dinger release 2023-1 was used for all computational purposes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Dataset generation and screening\u003c/h2\u003e\u003cp\u003eFor the screening, all compounds exhibiting inhibitory activity against FLT3 were selected from the ZINC [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], BindingDB [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and PubChem databases, comprising 1,050, 6,449, and 5,513 compounds respectively. These compounds were prepared using LigPrep [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], resulting in a total of 33,946 molecules. The ligands were then screened based on Lipinski\u0026rsquo;s Rule of Five (Ro5) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] using the QikProp module of Schrodinger. 26,423 compounds showing zero Ro5 violations were kept for further studies.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Molecular Interactions\u003c/h2\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1. Protein preparation\u003c/h2\u003e\u003cp\u003eThe X-ray crystallographic structure of the FLT3 target protein [PDB ID: 6JQR], in complex with \u003cem\u003eGilteritinib\u003c/em\u003e [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] was used in the study.\u003c/p\u003e\u003cp\u003eUsing the protein setup tool in Schr\u0026ouml;dinger Maestro, the structure underwent initial processing via the PROPKA component to refine hydrogen bond orientations.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] The structures underwent refinement to ensure the alignment of heavy atoms within an RMSD threshold of 0.3\u0026Aring;, employing the OPLS4 force field. Following this, water molecules situated beyond a 5\u0026Aring; distance from the ligands were eliminated. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2. Receptor grid generation\u003c/h2\u003e\u003cp\u003eThe receptor grid was constructed by centering it on both the hydrophobic domain and the binding site of \u003cem\u003eGilteritinib\u003c/em\u003e within the complex. The specified spatial coordinates for the grid were X=-30.02, Y=-11.86, Z=-26.27, with a ligand dimension extending to 18\u0026Aring;.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.3.3. Molecular docking\u003c/h2\u003e\u003cp\u003eMolecular docking was constrained to compounds containing a maximum of 100 freely rotating bonds and under 500 atomic constituents. The scaling coefficient for Van der Waals radii was adjusted to 0.80, while the threshold for partial charges was maintained at 0.15. Conformational adjustments included enabling nitrogen inversion sampling and structural flexibility within cyclic systems. The ligand conformation exploration was conducted under a flexible mode, ensuring prioritized torsional sampling for all anticipated functional moieties. The system was optimized to enhance internal hydrogen bonding and maintain the coplanarity of conjugated π-systems. Extra precision (XP) docking [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] utilized the Glide component within the Schr\u0026ouml;dinger suite. Top 5 compounds showing the highest docking score were considered for enumerations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.3.4. Ligand enumerations\u003c/h2\u003e\u003cp\u003eAll the functional groups in the ligand that showed proximity to the protein were altered to 2383 other functional groups using the ligand designer [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e] and R-group enumeration [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] modules of the Schrodinger Suite.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.4. ADME-Tox analysis\u003c/h2\u003e\u003cp\u003eA comparative evaluation of drug kinetics, with a particular focus on assessing the ADMET characteristics of the canonical ligand and its modification, identified from the ligand R-group enumeration, utilized multiple software platforms, including the QikProp module of Schr\u0026ouml;dinger and SwissADME [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], ProTox-3.0 [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], ADMETlab 3.0 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], and the pkCSM server [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. SwissADME, pkCSM, and ProTox-3.0 are online platforms that facilitate the estimation of pharmacokinetic attributes, determine drug-likeness, and analyze the appropriateness of small compounds for medicinal applications.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Molecular dynamics simulation studies\u003c/h2\u003e\u003cp\u003eThe molecular dynamics simulations for the FLT3 protein complex, along with its canonical ligand and altered form, were executed utilizing the Desmond software suite. Every system was independently embedded within a 10 \u0026Aring; orthorhombic aqueous environment, employing the TIP3P solvation framework. [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The OPLS4 force field was employed to construct the ligand-protein complexes [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Sodium counterions were incorporated into the protein-ligand complexes to balance the overall system charge during molecular dynamics simulations. Additionally, energy minimization was performed for 2000 iterations to achieve the lowest possible energy state before commencing the simulation within an NPT ensemble framework. [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOnce the system reached equilibrium, a production phase was carried out under conditions of 310.15 K temperature and 1 atm pressure for a duration of 500 ns to assess its stability in a water-based medium. Various assessments related to structure and stability were performed on the obtained molecular dynamics trajectories, including root mean square deviation (RMSD), root mean square fluctuation (RMSF), and gyration radius (Rg). To determine the extent of protein exposure to the surrounding solvent during the simulation, the solvent-accessible surface area (SASA) was computed. The graphical representations were generated using Matplotlib Library 3.10.0 [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] in using in-house python scripts.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Binding free energy distribution\u003c/h2\u003e\u003cp\u003eThe trajectory files generated from the MD simulations were examined utilizing visualization tools scripted in VMD. To investigate principal component patterns and assess the free energy landscape (FEL), the covariance matrix underwent computation followed by diagonalization. FEL, along with MM-GBSA, facilitated the identification of interactions that remained both stable and unvarying.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section3\"\u003e\u003ch2\u003e2.6.1. Free energy landscape (FEL)\u003c/h2\u003e\u003cp\u003eThe FEL was formulated as: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:F\\left(X\\right)={-k}_{B}TlnP\\left(X\\right)\\)\u003c/span\u003e\u003c/span\u003e, where \u003cem\u003eF(X)\u003c/em\u003e represents the free energy, \u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003eB\u003c/em\u003e\u003c/sub\u003e is the Boltzmann constant, \u003cem\u003eT\u003c/em\u003e is the absolute temperature, and \u003cem\u003eP(X)\u003c/em\u003e corresponds to the probability density function of the molecular system along the primary coordinate.\u003c/p\u003e\u003cp\u003eThe 2D and 3D FEL were constructed to analyze conformational transitions using RMSD, radius of gyration (Rg), and other parameters. For FEL calculations, RMSD and Rg values were projected onto a plane, with population densities estimated via kernel density estimation, and visualized as contour maps in Python [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e2.6.2. Molecular Mechanics-Generalized Born Surface Area\u003c/h2\u003e\u003cp\u003eThe prime module was employed to estimate the energetic factors derived from the MM-GBSA analysis, determining the extent of stabilization energy resulting from possible molecular interactions. The VSGB solvation model [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] was used and the force field was set as OPLS4. Binding free energy evaluations utilizing the MM-GBSA approach were performed on molecular dynamics simulation trajectories extending up to 500 ns. In order to identify protein-ligand complexes, this method proves to be more reliable for assessing binding affinities compared to glide score estimations. Important energetic factors, including hydrogen bonding contribution (ΔG\u003csub\u003eBind_Hbond\u003c/sub\u003e), solvation-associated electrostatic energy (ΔG\u003csub\u003eBind_Solv\u003c/sub\u003e), electrostatic or Coulombic interaction energy (ΔG\u003csub\u003eBind_Coul\u003c/sub\u003e), hydrophobic interaction contribution (ΔG\u003csub\u003eBind_Lipo\u003c/sub\u003e), and van der Waals interaction energy (ΔG\u003csub\u003eBind_vdW\u003c/sub\u003e), were collectively incorporated to estimate the relative binding affinity using the MM-GBSA framework.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.1. The FLT3 protein\u003c/h2\u003e\u003cp\u003eThe FLT3 protein consists of 993 amino acids and has a molecular weight of 112.903 Da. It is composed of three distinct regions: an extracellular segment containing 516 residues, a transmembrane portion made up of 19 amino acids, and a cytoplasmic region comprising 429 amino acids.[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. To determine the structure of 6JQR which is in the cytoplasmic domain, the PDBsum web server was utilized [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]; this study revealed 15 α-helices, 6 β-hairpins, 4 β-bulges, and 12 interactions between helices. (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe 6JQR structure underwent validation through the Ramachandran diagram, revealing that 93.5% of its amino acid residues occupied favored zones, 6.5% were found in permitted areas, none resided in permissible but less favorable sections, and 0.9% fell into restricted zones, with an overall G-Factor of 0.00. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. (see \u003cb\u003eSupplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Molecular docking studies\u003c/h2\u003e\u003cp\u003eThe docking assessments for the top 5 compounds, i.e., CHEMBL3403451 [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], UNC2124A (Patent ID: US10004755B2), CHEMBL3403450 [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], CHEMBL4577991 [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] and CHEMBL4444839 [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] yielded scores between \u0026minus;\u0026thinsp;13.675 kcal/mol and \u0026minus;\u0026thinsp;12.500 kcal/mol as a result of the XP docking procedure. These compounds were selected for modification for enhanced binding affinity and stability. The 2D and 3D interaction diagram of these ligands within the FLT3 binding site (\u003cb\u003eSupplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Ligand enumerations\u003c/h2\u003e\u003cp\u003eWe performed ligand enumeration of all the top 5 docked compounds from the XP docking. We set a parameter of a Δ docking score of 2 kcal/mol from the original docking score of the canonical compounds. The first four compounds did not produce any analogue with enhanced binding affinity compared to their canonical versions. However, CHEMBL4444839 generated multiple analogues with a Δ docking score of 2 kcal/mol. The \u003cb\u003ebest-scoring analogue\u003c/b\u003e of CHEMBL4444839 exhibited a key modification\u0026mdash;the incorporation of a fluorocyclobutane moiety \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. See \u003cb\u003eSupplementary Table S4\u003c/b\u003e for 2D and 3D structures of CHEMBL4444839 and its analogue. Interestingly, the binding affinity of the best CHEMBL4444839 analogue exhibited an enhanced docking score of more than 1 kcal/mol compared to the best compound, CHEMBL3403451 (-13.675 kcal/mol). CHEMBL3403451 and its analogue were evaluated for their comparative pharmacokinetic and other properties. See \u003cb\u003eSupplementary Figure S5\u003c/b\u003e for the ligand torsions diagram encapsulating the structural transformation of each flexible bond within the ligands, providing insights into their flexibility and binding adaptability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.4. ADME-Tox predictions\u003c/h2\u003e\u003cp\u003eFor this research, we employed the online platforms - SwissADME, ADMETlab 2.0, ProTox-II, and pkCMS to assess the absorption, distribution, metabolism, excretion, and toxicity characteristics of CHEMBL3403451 along with its structural variant. (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWith a toxicity class of 6 compared to class 4 for CHEMBL4444839, the analogue exhibits lower acute toxicity, supported by a much higher predicted LD50 of 5500 mg/kg versus 420 mg/kg. Unlike CHEMBL4444839, which is AMES toxic, the analogue is not, reducing its mutagenicity risk. The analogue also shows better systemic tolerance, with a higher maximum tolerated dose (0.496 vs. 0.4). While both compounds exhibit similar absorption profiles, the analogue demonstrates slightly improved Caco-2 permeability (0.55 nm/sec vs. 0.539 nm/sec) and human intestinal absorption (78.041% vs. 77.745%). The analogue excels in distribution characteristics, with a higher volume of distribution (1.324 log L/kg vs. 1.053) and lower plasma protein binding (93% vs. 96.5%), enhancing its bioavailability and pharmacodynamic activity. The metabolic profiles differ, with the analogue being a CYP2D6 substrate, suggesting a broader metabolic pathway, while both compounds interact similarly with key CYP enzymes. The analogue has a marginally higher total clearance rate (1.277 log ml/min/kg vs. 1.223), indicating faster elimination. Importantly, CHEMBL4444839-Analogue has reduced toxicity risks, including the absence of AMES toxicity, lower Pyriformis toxicity (\u0026micro;g/L\u0026thinsp;=\u0026thinsp;0.285 vs. 0.286), and lower minnow toxicity (log mM = -0.102 vs. 0.656), suggesting better environmental safety.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eSupplementary Tables S1, S2, and S3\u003c/b\u003e offers comprehensive comparative pharmacokinetic and toxicity assessments of CHEMBL4444839 and its analogue.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Molecular Dynamics Simulation\u003c/h2\u003e\u003cp\u003eThe MD trajectories were compared in terms of their RMSD, RMSF, Radius of Gyration, the protein ligand contacts, and the SASA values (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). CHEMBL4444839 appears to allow greater fluctuations in RMSD of the protein compared to the analogue bound structure, suggesting the increased stability of the protein when complexed with the analogue (Panel A).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Residue Mean Square Fluctuation (RMSF) plot provides valuable insights into the dynamic flexibility of the FLT3 protein residues when complexed with two ligands: CHEMBL4444839 and its analogue. Certain regions which are responsible for the activity of FLT-3 such as Juxta Membrane (JM) loop (579\u0026ndash;594), ATP binding site (616\u0026ndash;664), the active site (807\u0026ndash;819), and other conserved sites show reduction in the RMSF in the analogue bound structure, which appears to indicate that the analogue stabilizes the structure more efficiently compared to the original ligand (Panel D). \u003cb\u003eSupplementary Figure S4\u003c/b\u003e shows the ligand RMSF analysis indicating positional fluctuations of ligand atoms, providing insights into their entropic contributions and interactions with FLT3.\u003c/p\u003e\u003cp\u003eThe RMSF analysis of FLT3 regions reveals distinct ligand-induced stability patterns. In the JM loop (579\u0026ndash;594), CHEMBL4444839-Analogue exhibits reduced fluctuations compared to CHEMBL4444839, indicating enhanced stabilization, crucial for FLT3 activation control [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. At the ATP binding site (616\u0026ndash;664), both ligands show low RMSF, but the analogue displays a slight reduction, suggesting stronger interactions with ATP-critical residues, enhancing inhibition. The conserved site (668\u0026ndash;682) shows a fluctuation peak, but lower RMSF for the analogue implies stronger binding and structural integrity. In the active site (807\u0026ndash;819), the analogue stabilizes the region more effectively, crucial for enzymatic inhibition. The DFG-out region (829\u0026ndash;845) also shows reduced fluctuations with the analogue, suggesting enhanced stabilization of the inhibitory state (see Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eHigh fluctuation peaks (~\u0026thinsp;45, 125, 240 residues) indicate flexible regions, with the analogue moderating these movements, reinforcing overall protein stability. Interaction analysis (Panel A) shows CHEMBL4444839 forms strong H-bonds (GLU 661, 82%; CYS 694, 72%) and hydrophobic interactions (PHE 621, 55%; LEU 818, 98%), with moderate water-bridging contributions. Panel B highlights dominant hydrophobic interactions, particularly at LEU 818 and PHE 621, with minimal ionic interactions. The analogue exhibits increased interaction duration and novel contacts (e.g., PHE 830), suggesting superior FLT3 inhibition. The timeline representation of interactions in \u003cb\u003eSupplementary Figure S3\u003c/b\u003e further illustrates the stability and duration of these contacts, revealing how the protein maintains ligand interactions across the simulation trajectory.\u003c/p\u003e\u003cp\u003eTowards the end of the simulation, radius of gyration (Rg) marginally increases for the structure bund with CHEMBL4444839, suggesting the stability of the protein could potentially be better with the analogue bound. Initially, CHEMBL4444839 exhibits a higher Rg (~\u0026thinsp;6.5 \u0026Aring;) than the analogue (~\u0026thinsp;6 \u0026Aring;), indicating a less compact starting state. Both systems stabilize by 20\u0026ndash;30 ns, with the analogue maintaining a lower Rg (~\u0026thinsp;5 \u0026Aring;) compared to CHEMBL4444839 (~\u0026thinsp;5.2 \u0026Aring;) throughout 50\u0026ndash;400 ns, suggesting enhanced structural integrity. The analogue\u0026rsquo;s Rg trajectory is smoother, implying reduced instability. In the final phase (400\u0026ndash;500 ns), CHEMBL4444839\u0026rsquo;s Rg rises beyond 6 \u0026Aring;, indicating destabilization, while the analogue remains stable (~\u0026thinsp;5 \u0026Aring;), suggesting superior long-term stabilization [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] (Panel C). To further assess the dynamic behavior of the ligand-protein complexes, we analyzed the variations in RMSD, rGyr, MolSA, intraHB, SASA, and PSA over 500 ns of MD simulation time (\u003cb\u003eSupplementary Figure S6\u003c/b\u003e), which provide insights into the stability and compactness of the system.\u003c/p\u003e\u003cp\u003eSASA analysis shows dynamic ligand-protein interactions. CHEMBL4444839 maintains lower SASA values (50\u0026ndash;150 \u0026Aring;\u0026sup2;), with minimal spikes (\u0026lt;\u0026thinsp;200 \u0026Aring;\u0026sup2;), whereas the analogue exhibits higher SASA (100\u0026ndash;200 \u0026Aring;\u0026sup2;) with pronounced peaks, suggesting greater structural rearrangements. In 100\u0026ndash;300 ns, the analogue shows significant fluctuations, reflecting dynamic binding, while CHEMBL4444839 remains more rigid. In 300\u0026ndash;500 ns, the analogue sustains elevated SASA, indicating persistent interaction, whereas CHEMBL4444839 retains lower SASA, implying more restricted binding dynamics [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. These findings highlight the analogue\u0026rsquo;s superior protein stabilization and dynamic binding properties, which may enhance efficacy (Panel E).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.6. Binding free energy distributions\u003c/h2\u003e\u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\u003ch2\u003e3.6.1. Free energy landscape (FEL)\u003c/h2\u003e\u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, Panels (A) and (C) represent CHEMBL4444839, while (B) and (D) depict its analogue. Each FEL plots free energy against RMSD and Rg. For CHEMBL4444839, the 2D FEL (A) shows a primary minimum at RMSD\u0026thinsp;=\u0026thinsp;0.5-1.0 \u0026Aring; and Rg\u0026thinsp;=\u0026thinsp;5.75-6.0 \u0026Aring; (~\u0026thinsp;2.85 kcal/mol), stabilizing FLT3. A secondary basin at Rg\u0026thinsp;~\u0026thinsp;6.2 \u0026Aring; suggests an alternative, less stable conformation. High-energy regions (RMSD\u0026thinsp;\u0026gt;\u0026thinsp;2.0 \u0026Aring;) indicate destabilization. The 3D FEL (C) confirms a deep energy well at the primary minimum and sharp peaks for high RMSD, indicating conformational penalties. The energy range spans 2.85\u0026ndash;5.28 kcal/mol, suggesting moderate flexibility. For the analogue, the 2D FEL (B) shows a minimum at RMSD\u0026thinsp;=\u0026thinsp;0.5-1.0 \u0026Aring; and Rg\u0026thinsp;=\u0026thinsp;6.0-6.25 \u0026Aring; (~\u0026thinsp;2.61 kcal/mol), indicating better stabilization than the parent compound. A broader low-energy basin suggests higher conformational adaptability, with fewer high-energy states. The 3D FEL (D) reveals a deeper, smoother energy surface, supporting enhanced stability across a wider conformational space. The energy range (2.61\u0026ndash;5.31 kcal/mol) highlights its superior ability to minimize unfavorable states.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e3.6.2. Molecular Mechanics Generalized Born Surface Area (MM-GBSA)\u003c/h2\u003e\u003cp\u003eThe binding free energy distributions of FLT3 in complex with CHEMBL4444839 and its analogue were analysed over a molecular dynamics simulation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e \u003cb\u003e(A)\u003c/b\u003e). The ΔG values (kcal/mol) over time (ns) reveal distinct binding behaviours (\u003cb\u003eSupplementary Table S5 \u0026amp; S6\u003c/b\u003e). CHEMBL4444839 (red line) exhibits greater fluctuations, with peaks exceeding \u0026minus;\u0026thinsp;60 kcal/mol, indicating transient weaker binding. In contrast, the analogue (green line) maintains a more stable profile, rarely exceeding \u0026minus;\u0026thinsp;70 kcal/mol, suggesting stronger, more consistent binding. CHEMBL4444839 shows a broader ΔG range (-100 to -60 kcal/mol), reflecting variability, whereas the analogue remains within \u0026minus;\u0026thinsp;100 to -70 kcal/mol, indicating stability. Temporal analysis reveals erratic fluctuations for CHEMBL4444839, while the analogue stabilizes over time, suggesting durable interactions. The analogue\u0026rsquo;s lower average ΔG and smoother fluctuations indicate superior binding affinity and stability, likely due to structural modifications enhancing hydrogen bonding, hydrophobic interactions, or π-π stacking. These properties suggest its potential as a more reliable FLT3 inhibitor.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e3.7. Trajectory analysis\u003c/h2\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003e3.7.1. Principal component analysis (PCA)\u003c/h2\u003e\u003cp\u003eThe PCA plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e) illustrate the conformational dynamics of FLT3 in complex with CHEMBL4444839 (Panel A) and its analogue (Panel B). Panel A exhibits greater dispersion, indicating higher conformational flexibility and structural variability, suggesting weaker and less stable binding. Conversely, Panel B shows tighter clustering, signifying a more stable complex with restricted conformational sampling. Temporal progression in Panel A is erratic, reflecting transient binding, while Panel B exhibits smoother transitions, indicating sustained stability. The broader distribution in Panel A suggests non-specific interactions, whereas Panel B\u0026rsquo;s confined range implies strong stabilization of FLT3 in a biologically relevant state.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003e3.7.2. Dynamic cross-correlation matrix (DCCM)\u003c/h2\u003e\u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, both CHEMBL4444839 (Panel C) and its analogue (Panel D) maintain self-correlation integrity, validating DCCM computation. However, Panel D exhibits a sharper diagonal, indicating more localized protein dynamics. Positive correlations in Panel C are weaker and dispersed (50\u0026ndash;100, 150\u0026ndash;200, ~\u0026thinsp;250), while Panel D shows stronger, structured correlations (100\u0026ndash;150, 200\u0026ndash;250), enhancing FLT3 stability. Negative correlations are more extensive in Panel D, particularly between 50\u0026ndash;100 and 150\u0026ndash;200, suggesting enhanced dynamic adaptability. Panel C displays scattered correlations, whereas Panel D induces structured, functionally relevant allosteric effects. The analogue stabilizes FLT3 more effectively, promoting enhanced allosteric signaling and residue communication, particularly in the 50\u0026ndash;100 and 150\u0026ndash;200 regions, supporting its superior binding and dynamic impact over CHEMBL4444839.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThe comparative analysis between CHEMBL4444839 and its analogue strongly suggests that the latter exhibits superior stability and binding characteristics with the FLT3 protein. The smoother and more consistent RMSD trends of the analogue indicate more stable interactions, potentially due to improved binding affinity and reduced conformational fluctuations. The analogue\u0026rsquo;s lower RMSF values across critical regions further support its superior stabilization and potential for enhanced inhibition.\u003c/p\u003e\u003cp\u003eStructurally, CHEMBL4444839 consists of a p-toluenesulfonyl (-SO₂-) group attached to a secondary sulfonamide (-SO₂NH-) functionality, which connects to a flexible alkyl linker (-NH-(CH₂)₄-NH₂). This linker bridges the sulfonamide moiety to the 1H-pyrrolo[2,3-b]pyridine core, an aromatic bicyclic system that likely contributes to binding through π-π stacking and hydrogen bonding interactions. In the best analogue, a significant structural modification is the incorporation of a fluorocyclobutane moiety at position 5 of the pyrrolopyrimidine ring. This modification introduces a four-membered cyclobutane ring substituted with a fluorine atom, which increases steric bulk and imposes conformational rigidity on the heterocycle. The presence of fluorine imparts an electron-withdrawing effect, potentially altering dipole moments and influencing interactions with nearby residues. Additionally, the p-toluenesulfonyl (-SO₂-) group and the flexible -(CH₂)₄-NH₂ alkyl linker remain unchanged, ensuring that key hydrogen bond donor-acceptor interactions in the parent compound are preserved.\u003c/p\u003e\u003cp\u003eThe interaction profile of the analogue reveals increased binding stability through additional interactions with key residues, such as PHE 830 [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], GLU 661 [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], and CYS 694 [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], and optimized hydrophobic interactions with LEU 818 [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. The analogue also maintains consistent water-bridging interactions, ensuring hydration dynamics remain intact while enhancing overall binding efficacy [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. The lower Rg values for the analogue indicate a more compact and tightly bound complex, further emphasizing its superior stability over the original compound.\u003c/p\u003e\u003cp\u003eFluorocyclobutane enhances metabolic stability, lipophilicity, and binding interactions through conformational rigidity. The fluorine atom increases resistance to enzymatic oxidation [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], while the four-membered ring reduces metabolic cleavage susceptibility. It modulates lipophilicity by fine-tuning the hydrophilic-hydrophobic balance, optimizing LogP for membrane permeability and bioavailability\u0026mdash;though aliphatic fluorination often reduces overall lipophilicity [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Fluorine\u0026rsquo;s high electronegativity enables dipole interactions, strengthening target binding [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], while the rigid cyclobutane ring locks the molecule into a bioactive conformation, minimizing entropic penalties [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. These combined effects might enhance stability, solubility, and binding affinity, contributing to improved docking performance and potential biological activity.\u003c/p\u003e\u003cp\u003eFree energy calculations reveal a deeper free energy minimum (~\u0026thinsp;2.61 kcal/mol) for the analogue compared to the parent compound (~\u0026thinsp;2.85 kcal/mol), indicating stronger stabilization of the FLT3 protein-ligand complex. The analogue\u0026rsquo;s energy landscape is narrower, reducing entropic penalties and off-target interactions while improving binding affinity [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCluster and dynamic stability analyses confirm that the analogue stabilizes the FLT3 structure more effectively than CHEMBL4444839, potentially improving therapeutic efficacy. The analogue\u0026rsquo;s interactions restrict protein motion, stabilizing it in a biologically favorable conformation [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. DCCM analysis further demonstrates that the analogue better engages critical residues, promoting a more cohesive and functional dynamic state in FLT3.\u003c/p\u003e\u003cp\u003eOverall, the analogue outperforms CHEMBL4444839 in stability, binding strength, and adaptability, making it a more promising candidate for further development as a potent FLT3 inhibitor. Further experimental validation and molecular studies are recommended to confirm these findings and optimize the analogue\u0026rsquo;s therapeutic potential.\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eOur study presents a structurally optimized FLT3 inhibitor that exhibits superior binding affinity, stability, and pharmacokinetic properties compared to CHEMBL4444839, positioning it as a promising candidate for AML therapy. The incorporation of a fluorocyclobutane moiety significantly enhances ligand-protein interactions through increased hydrogen bonding, electrostatic stabilization, and hydrophobic engagement with key FLT3 residues. Molecular dynamics simulations and free energy calculations confirm the analogue\u0026rsquo;s reduced conformational fluctuations, lower entropic penalties, and stronger protein-ligand stabilization, while DCCM analysis reveals improved allosteric communication within FLT3. Furthermore, favorable ADME and toxicity assessments indicate enhanced metabolic stability, membrane permeability, and drug-likeness. These findings provide compelling computational evidence for the analogue\u0026rsquo;s therapeutic potential, warranting further \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e validation to assess its efficacy and translational applicability in AML treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank NCBS, Bangalore for providing computational support. The authors would like to thank NCBS (TIFR) for infrastructural facilities. This work was supported by the Department of Atomic Energy, Government of India, Project Identification No. R TI 4006. RS is a J.C. Bose National Fellow (JBR/2021/000006) from the Science and Engineering Research Board, India. RS would also like to thank Bioinformatics Centre Grant funded by the Department of Biotechnology, India (BT/PR40187/BTIS/137/9/2021) and the Institute of Bioinformatics and Applied Biotechnology for the funding through her Mazumdar-Shaw Chair in Computational Biology (IBAB/MSCB/182/2022). A warm heartfelt thanks to the staff and administration at the NCBS, Bangalore for their support. We would also like to thank Dr. Abhijit Kayal, Senior Scientist II at Schr\u0026ouml;dinger, for his assistance with Free Energy Landscape (FEL) calculations and for his support with the Python scripts used in the analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors report no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research did not involve any human participants or animal subjects, and therefore, no ethical approval was required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research has received funding from \u0026nbsp;the Department of Biotechnology (DBT) Human Resource Development (HRD), Ministry of Science and Technology, Government of India.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eUddalak Das:\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e0009-0004-0007-5691\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eUddalak Das\u003c/em\u003e\u003c/strong\u003e: Conceptualization, Methodology, Formal Analysis, Writing\u0026mdash;Original Draft, Writing\u0026mdash;Review \u0026amp; Editing. \u003cstrong\u003e\u003cem\u003eDheemanth Regati\u003c/em\u003e\u003c/strong\u003e: Conceptualization, Methodology, Formal Analysis, Writing\u0026mdash;Original Draft, Writing\u0026mdash;Review \u0026amp; Editing. \u003cstrong\u003e\u003cem\u003eR. Sowdhamini\u003c/em\u003e\u003c/strong\u003e: Supervision, Project Administration. \u003cstrong\u003e\u003cem\u003eJitendra Kumar\u003c/em\u003e\u003c/strong\u003e: Supervision. All the authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the writing process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe writing of this research paper involved the use of generative AI and AI-assisted technologies only to enhance the clarity, coherence, and overall quality of the manuscript. The authors acknowledges the contributions of AI in the writing process while ensuring that the final content reflects the author\u0026apos;s own insights and interpretations of the literature. All interpretations and conclusions drawn in this manuscript are the sole responsibility of the author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eVakiti A, Mewawalla P. Acute Myeloid Leukemia. StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 [cited 2024 Feb 20]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK507875/.\u003c/li\u003e\n\u003cli\u003eSiegel RL, Miller KD, Wagle NS, \u003cem\u003eet al\u003c/em\u003e. Cancer statistics, 2023. CA Cancer J Clin. 2023;73(1):17\u0026ndash;48.\u003c/li\u003e\n\u003cli\u003eSaultz JN, Garzon R. Acute Myeloid Leukemia: A Concise Review. J Clin Med. 2016;5(3):33.\u003c/li\u003e\n\u003cli\u003eKazi JU, Al Ashiri L, Purohit R, \u003cem\u003eet al\u003c/em\u003e. Understanding the Role of Activation Loop Mutants in Drug Efficacy for FLT3-ITD. Cancers. 2023;15(22):5426.\u003c/li\u003e\n\u003cli\u003eLevis MJ, Perl AE, Altman JK, \u003cem\u003eet al\u003c/em\u003e. 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Nucleic Acids Res. 2007;35(Database issue):D198-201.\u003c/li\u003e\n\u003cli\u003eDas U, Chanda T, Kumar J, \u003cem\u003eet al\u003c/em\u003e. Discovery of Natural MCL1 Inhibitors using Pharmacophore modelling, QSAR, Docking, ADMET, Molecular Dynamics, and DFT Analysis [Internet]. 2024 [cited 2025 Jan 9]. Available from: http://biorxiv.org/lookup/doi/10.1101/2024.10.14.618373.\u003c/li\u003e\n\u003cli\u003eLipinski CA, Lombardo F, Dominy BW, \u003cem\u003eet al\u003c/em\u003e. Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings 1PII of original article: S0169-409X(96)00423-1. The article was originally published in Advanced Drug Delivery Reviews 23 (1997) 3\u0026ndash;25. 1. Adv Drug Deliv Rev. 2001;46(1\u0026ndash;3):3\u0026ndash;26.\u003c/li\u003e\n\u003cli\u003eKawase T, Nakazawa T, Eguchi T, \u003cem\u003eet al\u003c/em\u003e. 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Proc Natl Acad Sci. 2003;100(20):11367\u0026ndash;11372.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-molecular-modeling","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmmo","sideBox":"Learn more about [Journal of Molecular Modeling](https://www.springer.com/journal/894)","snPcode":"894","submissionUrl":"https://submission.nature.com/new-submission/894/3","title":"Journal of Molecular Modeling","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Acute myeloid leukemia, FLT3, pharmacophore, molecular docking, ADMET, molecular dynamic simulation","lastPublishedDoi":"10.21203/rs.3.rs-7508862/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7508862/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe FLT3 protein is a well-established therapeutic target for treating \u0026nbsp;Acute Myeloid Leukemia (AML), with its inhibition playing a crucial role in disease management. In this study, we identify and propose a novel FLT3 inhibitor that demonstrates superior binding affinity, stability, and pharmacokinetic properties compared to currently available inhibitors. We initially characterized the binding interactions of known FLT3 inhibitors through molecular docking and then strategically modified functional groups to enhance binding affinity, optimize drug-likeness, and minimize toxicity. The resulting analogue exhibits improved metabolic stability, lower toxicity, higher intestinal absorption, and superior permeability. Molecular dynamics simulations further confirm that the novel inhibitor forms stable and persistent interactions with FLT3, as evidenced by reduced conformational fluctuations and compact structural integrity. Free energy calculations reveal stronger ligand stabilization, while dynamic correlation analysis suggests enhanced engagement with critical residues, reinforcing its potential as an effective therapeutic agent. These findings highlight a promising candidate for further experimental validation and potential development in AML treatment.\u003c/p\u003e","manuscriptTitle":"Rational design and comparative docking and simulation of modified FLT3 inhibitors: A study on enhanced binding stability and inhibition potency","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 17:29:12","doi":"10.21203/rs.3.rs-7508862/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-10T06:50:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-10T03:09:46+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-10T03:08:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Molecular Modeling","date":"2025-09-01T13:10:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-molecular-modeling","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jmmo","sideBox":"Learn more about [Journal of Molecular Modeling](https://www.springer.com/journal/894)","snPcode":"894","submissionUrl":"https://submission.nature.com/new-submission/894/3","title":"Journal of Molecular Modeling","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"9f68a279-516f-11e9-9e20-12b504df345a","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T19:39:21+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 17:29:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7508862","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7508862","identity":"rs-7508862","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}