Molecular docking analysis and interaction maps of pesticides with TLR4Molecular docking is a method which is used to predict the preferred arrangement of one molecule relative to another when a ligand binds to a target protein, forming a stable complex32. Scoring functions, such as knowledge of the preferred orientation, can be utilized to predict the binding affinity or the degree of association strength between two molecules33,34. The binding energies of different pesticides (ligands) with the protein TLR4 are presented in Table 1. C107, C160 and C165 showed the best binding with TLR4 with binding affinities of -8.0, -8.2 and − 8.0 kcal/mol respectively, based on the scores these three molecules were selected for further analysis.The two-dimensional (2D) interactions of TLR4 with C107 are shown in Fig. 3a. Hydrogen bonds were formed exclusively by the amino acid residues ASP453_3.37 and THR459_3.16. ILE454_3.11 is the only amino acid residue which forms π -sigma bond. The amino acid residues that displayed halogen bonding interactions, along with their corresponding distances, are as follows: ASN433_0.97/1.72/3.58, SER432_3.53/2.52, THR457_2.06/3.12, ASP453_2.33, LEU452_2.65, HIS426_3.08, LEU427_2.94/1.37, LEU434_1.86/1.95/3.67. Alkyl and π-alkyl bonds are being formed by LEU449_5.17 and LEU434_3.73/4.99 whereas TYR_4.51 showed π- π stacked interaction. Furthermore, the 2D interaction maps of TLR4 with C160 are represented in Fig. 3b. Conventional hydrogen bond and π-alkyl were the two molecular interactions observed in 2D map. The amino acid residues and their distances from the ligand, involved in hydrogen bonding interactions are ARG382_2.15, ASN433_2.69, GLY363_1.97, ASN361_2.43, ASN383_1.91 whereas the residues which are involved in the formation of π-alkyl bond are HIS458_4.12, HIS431_5.17. Figure 3c displays 2D view of molecular interactions in TLR4 with C165. THR232_3.06, LYS230_3.18, ARG289_3.60 and ARG234_3.18/3.21 are exhibiting the conventional hydrogen bonding interactions. SER207_3.32 is the only residue which is showing carbon hydrogen bonding interactions. π -anion bond formation is demonstrated by ASP209_3.44. The residues which are involved in the formation of alkyl and π-alkyl bonds are VAL259_4.94, VAL316_4.79, ALA291_5.02/4.44, ARG264_4.53. Halogen bonding interactions is shown by SER207_3.31 and LEU208_3.32/3.16.Fig. 32D docked poses of (a) C107 with active site region of TLR4 protein, (b) C160 with active site region of TLR4 protein, (c) C165 with active site region of TLR4 protein.Table 1 Binding energies obtained through molecular docking studies.The structural comparison of the emamectin benzoate (EMB) with the chosen pesticides, C107, C160 and C165 was done to check the similarity in their structures to identify any specific group which might be responsible for toxicity (Fig. 4). EMB and C160 exhibited most structural similarity, namely, presence of a macrocyclic ring containing ether functional group, both the structures possess tetrahydopyran in the side chains. The pesticide derivates C107 and C165 does not possess any noticeable similarities with EMB other than the fact they both are macromolecules containing heteroatoms and multiple functional groups among which carboxyl is a commonality. However, C107 and C165 structures have many similarities with each other, notably, both the structures possess CF3 substituent and difluorobenzene at the terminals along with amide group in their respective structures.Fig. 42D structures of emamectin benzoate, C107, C160 and C165.Molecular dynamics (MD) simulations to understand the formation of the complex100 ns simulationRMSD, RMSF and hydrogen bond analysisMD simulations are real-time simulations extensively employed to examine the conformational changes, stability, and interactions between proteins and ligands within the complex35. Stability of conformational changes with nanosecond-level precision, enabling the system to illustrate atomic-level changes in terms of coordinates can be studied with MD simulations36,37,38. MD simulations were performed to analyse the stability of native protein and complex. It produces a range of trajectories, which provide a valuable understanding of protein-ligand interactions and enable further investigation of these interactions39,40. The different trajectories include root mean square deviation (RMSD), root mean square fluctuations (RMSF), and hydrogen bonds.RMSD quantifies the average displacement of atoms within41 the protein-ligand complex relative to their positions in the initial frame42,43. The binding of a ligand into the active site enhances the conformational stability of the macromolecular system44. Figure 5a represents RMSD plots of TLR4 protein bound to compounds with favourable binding affinities, namely C107, C160, and C165 (ligands). The RMSD value of the native protein was measured at 4.9 Å at 79 nanoseconds (ns). The protein exhibited the highest fluctuations during the entire simulation time. In the TLR4 and C107 complex, the highest RMSD value obtained was 5.9 Å at 26 ns, with maximum fluctuations observed until 90 ns. Subsequently, the complex began to stabilize, exhibiting fewer fluctuations. The peak RMSD value recorded for the TLR4 complexed with C160 reached 5.3 Å at 84 ns, exhibiting minimal fluctuations throughout the entire simulation period. Conversely, the TLR4 complexed with C165 exhibited its highest RMSD value of 7.4 Å at 75 ns, with fewer fluctuations observed until 65 ns. At this point, there was a sudden decrease in RMSD value, followed by a sharp rise within the time frame of 69 to 73 ns. Interestingly, all three complexes exhibited more stable fluctuations compared to the native protein.RMSF provides insights into the fluctuation levels of individual residues across the entire structure of protein and protein-ligand complex. RMSF is a quantitative measure of how much the amino acid residue has deviated from its mean position throughout the simulation period. It offers valuable information regarding the flexibility or mobility of the residue45. It is usually plotted for a better understanding of residue-wise fluctuation in both protein and complex. Figure 5b illustrates RMSF plots of the native protein and the three protein-ligand complexes. The highest fluctuating residues obtained in native protein were SER563, LEU202, TYR266, GLY439 and THR585 residues with a RMSF values of 1.5 Å, 1.3 Å, 1.2 Å, 1.1 Å and 1.0 Å. In contrast, the highest fluctuating amino acid residues in TLR4 complexed with C107 were GLN562 at 1.5 Å, LEU102 at 1.4 Å ALA265 at 1.2 Å, LYS124 and LEU202 at 1.3 Å. The amino acid residues that showed maximum fluctuations in TLR4 complexed with C160 were ALA265 at 1.9 Å and GLN562 at 2.2 Å. Finally, the amino acid residues exhibiting maximum fluctuations in the complex between TLR4 and C165 were GLN562 at 2.8 Å, LEU485 at 1.7 Å and THR585 at 1.8 Å.Fig. 5(a) RMSD and (b) RMSF obtained from the trajectories of 100ns simulations.The radius of gyration (Rg) was calculated to assess the compactness of the macromolecules in the absence and presence of ligands. Rg refers to the radial mass-weighted distance between an atom and its centre of mass46. Figure 6a demonstrated Rg plots calculated for both TLR4 and TLR4 complexed with C107, C160 and C165. High Rg values during molecular dynamics simulations suggests low compactness of the protein, whereas low Rg values indicate greater compactness of the protein47. The maximum Rg value of the TLR4 observed was 33.2 Å at 54 ns while that of TLR4 complexed with C107, C160 and C165 were 35.4 Å at 26 ns, 33.6 Å at 24 ns and 35.9 Å at 75 ns. The native protein exhibited lower Rg value than the complexes, indicating more compactness and stability in the protein structure than the complexes. Furthermore, fewer fluctuations were observed in the complexes compared to the native protein, possibly indicating increased stability of the protein upon ligand binding.Higher number of hydrogen bonds can be correlated to the stability of the complex (protein-ligand)48. Figure 6b illustrates the total count of hydrogen bonds produced by all three complexes over the whole duration of the simulation. All three complexes established a maximum of four hydrogen bonds.Fig. 6(a) Radius of gyration analysis plots of TLR4 with C106, C160 and C165. (b) Number of hydrogen bonds formed during 100 ns simulations.Principal component analysisPCA analysis was performed to determine the conformational changes of the protein induced by ligands binding and to elucidate the collective motions within the MD trajectories49. It is feasible to have a deeper knowledge of how the complex develops over time and which motion patterns are most significant by examining the PCA findings at each time point. Each point on the plot represents a snapshot from the MD trajectory projected onto the first two principal components (eigenvectors), which capture the most significant collective motions. According to Fig. 7, the native protein occupied a small phase space with a compact cluster, suggesting limited conformational variability and good stability in the absence of ligand. However, it was observed that upon ligand binding, the phase space increased and the structure became more flexible. Moreover, there was also a difference in phase space when all the three complexes were compared. Among all the three complexes, TLR4-C107 demonstrated a relatively tighter cluster, indicating less structural variation and more stability upon binding with C107 as shown in Fig. 7b whereas TLR4 bound with C165 was shown to occupy the largest phase space exhibiting highest structural variation and flexibility, indicating minimal stability as represented in Fig. 7d. The TLR4 and C160 complex indicated that their binding significantly increases the conformational space occupied by the protein and the principal components were distributed broadly as displayed in Fig. 7c. TLR4-160 complex was an intermediate between both the above complexes in terms of phase space, structural variation, and stability. This means it was neither too compact nor flexible and no major structural variations were observed. From this it can be said that the native protein upon binding with different ligands underwent major structural variations making the structure less compact, less stable and more flexible.Fig. 7illustrates PCA analysis of (a) TLR4 protein and TLR4 complexed with (b) C107 (c) C160 (d) C165.MD simulations for 300 nsTher RMSD and RMSF analyses from the 300 ns molecular dynamics (MD) simulations of the 3FXI protein with Flufenoxuron (107), Natamycin (160), and Oxathiapiprolin (165) reveal significant insights into the stability and flexibility of these complexes. The RMSD plots (Fig. 8a) indicate that all three complexes reach equilibrium after an initial adjustment period, with the 3FXI-Flufenoxuron complex showing the highest RMSD values, suggesting greater structural fluctuations. In contrast, the 3FXI-Natamycin and 3FXI-Oxathiapiprolin complexes exhibit lower RMSD values, indicating more stable interactions. The RMSF plots (Fig. 8b) further supports these findings, showing reduced fluctuations in the binding site regions for all three ligands, with the 3FXI-Oxathiapiprolin complex displaying the least fluctuation, suggesting a more rigid and stable interaction at the binding site.Fig. 8(a) RMSD and (b) RMSF obtained from the trajectories of 300ns MD simulations.The analysis of hydrogen bond number and Rg provides additional information about the stability and compactness of the protein and protein-ligand complexes. Figure 9a contains the plots of hydrogen bond number between ligands and the protein. The 3FXI-Natamycin complex have the most number of hydrogen bonds, correlating with its lower RMSD and Rg values, indicating a stable and tightly bound complex. The 3FXI-Oxathiapiprolin complex also shows a significant number of hydrogen bonds, resulting into increased stability. The Rg values for all three complexes remain relatively stable throughout the simulation (Fig. 9b), with the 3FXI-Natamycin complex showing a slightly lower Rg value, suggesting a more compact structure. These findings highlight the importance of hydrogen bonding in stabilizing the complexes and provides important information about the dynamic behaviour of these protein-ligand interactions.Fig. 9a Number of hydrogen bonds, and b radius of gyration analysis plots of TLR4 with C106, C160 and C165 formed during 300 ns MD simulations.Pre- and post-MD comparison of docked ligandDocked pose analysisComparing the docked ligand in the protein before and after MD simulations provides valuable insights into conformational changes and interaction dynamics. The Fig. 10 presents a surface view of the TLR4 binding cavity with the superimposed conformations of the selected molecules (C107, C160, and C165) from both the molecular docking and MD simulation analyses. The figure clearly illustrates how each ligand fits within the active site.Fig. 10Surface view of molecular docking and MD Simulations a C107 with active site region of TLR4 protein, b C160 with active site region of TLR4 protein, c C165 with active site region of TLR4 protein.2D and 3D representations of docked and MD poses of C107, C160 and C165 with 3FXI are present in Fig. 11. For the ligand C107, the docking interactions included THR A:457, SER A:432, ASP A:453, ILE A:454, LEU A:449, and ASN A:433. After 300 ns of MD simulation, the interactions with ASP A:453, LEU A:427, and THR A:457 were retained, indicating stable binding. New interactions with GLU A:425, LEU A:446, and PHE A:418 emerged, suggesting a shift in the binding pose. The types of interactions observed include conventional hydrogen bonds (green) and pi-alkyl interactions (pink).Fig. 112D and 3D representations of docked and MD poses of C107, C160 and C165 with 3FXI.For the ligand C160, the docking interactions included ASN A:433, HIS A:458, HIS A:431, ARG A:382, TYR A:451, ASN A:383, and GLY A:363. Post-MD simulation, the interaction with ARG A:382 was maintained, while new interactions with GLY A:384, GLY A:410, and PHE A:408 were observed. This indicates a partial retention of the original binding interactions with some reorientation. For the ligand C165, the docking interactions included ARG A:289, ASN A:361, ARG A:264, SER A:207, LEU A:208, VAL A:259, ALA A:291, ASP A:209, LYS A:230, VAL A:316, and ARG A:234. After 300 ns MD simulation, the interactions with ILE A:454, LEU A:434, and THR A:457 were retained, while new interactions with PHE A:443, VAL A:442, and MET A:437 emerged. This suggests a stable binding with additional interactions forming over the simulation period.Hydrogen bond occupancyHydrogen bond occupancy was calculated from the 300 ns trajectories, with the results detailed in Table 2. For compound C107, the hydrogen bond occupancy data reveals that the interactions between the side chain of LIG628 and the main chain of GLU425, with an occupancy of 4.10%, has significant contribution to the stability. This interaction likely plays a key role in stabilizing the ligand within the binding site. Other interactions, such as the THR457-Side to LIG628-Side (1.10%) and ASP453-Main to LIG628-Side (0.75%), are moderately significant but less critical compared to the GLU425 interaction. The remaining hydrogen bonds, including those between the main chains of LEU427, LEU434, and HIS426 with LIG628, show much lower occupancies (ranging from 0.15 to 0.30%), suggesting they contribute minimally to the overall stability of the compound within the binding site.Compound C160 exhibits the strongest and most numerous hydrogen bond interactions among the three compounds analyzed. The most prominent interaction is between ARG382 and the side chain of LIG628, with an exceptionally high occupancy of 27.67%. This indicates a critical and consistent interaction that likely dominates the stabilization of C160 in its binding environment. Additional significant interactions include those between LIG628-Side and ASN361-Main (5.84%), LIG628-Side and ASN339-Side (5.69%), and LIG628-Main and SER360-Side (5.44%), all of which further enhance the stability of the compound. Moderate interactions, such as those involving ASN409, SER360, and ASN383, also contribute to the overall binding stability but to a lesser extent. The numerous low-occupancy interactions observed in C160, while less critical, may still provide additional stabilization through minor contributions.For compound C165, the hydrogen bond occupancy data suggests a more modest level of interaction compared to C160. The primary interactions involve the side chain of ASN433 with both the main and side chains of LIG628, with occupancies of 2.10% and 1.60%, respectively. These interactions, while not as strong as those seen in C160, are likely important for the stabilization of C165 within the binding site. The limited number of significant interactions and the absence of high-occupancy bonds suggest that C165 may be less stable compared to C160, but possibly more stable than C107 due to these moderately consistent interactions.Table 2 Hydrogen bond occupancy percentage for C107, C160 and C165.Density functional theory (DFT) calculations of promising pesticidesFrom a computational perspective, using DFT has developed into an essential approach for estimating the electronic and quantum behavior of molecules50. These calculations assist in estimating the stability, enthalpy of formation, and polarity of the compound by computing the energies of the optimised geometry. Due to its exceptional precision and efficiency, DFT has become one of the most widely used techniques for determining electronic structures51. Here, these calculations were employed in analysing the structural properties of all three compounds.Frontier molecular orbitals (HOMO and LUMO) are essential in quantum chemistry, offering key insights into molecular behaviour, properties, and reactivity52. The HOMO is the highest energy orbital that contains electrons, making it an electron donor. The lowest energy orbital, or LUMO, on the other hand, is an electron acceptor since it has the ability to take electrons. These HOMO, LUMO and optimized geometry are obtained through DFT calculations are illustrated in Fig. 12. The thermodynamic parameters of C107, C160 and C165 are computed in Table 3. The dipole moment (µ) is frequently used to determine the polarity of the molecules. It assesses how charge fractions are distributed among molecules. Molecules’ solubility in water as well as their ability to combine with other solvents can both be affected by their polarity53. C107 exhibited highest dipole moment of 12.014129 Debye, indicating highly polar and soluble molecule than C160 and C165. Moreover, the optimization energy was also noted for all three compounds. The optimization energy of C160 (-2318.301 Hartree) was the highest as compared with C107 and C160.Fig. 12Visual representation of HOMO, LUMO orbitals and optimized geometry of C107, C160 and C165.Table 3 Computed thermodynamic parameters of the C107, C160 and C165.Table 4 displays the energies of HOMO and LUMO which are the important parameters for determining chemical reactivity and selectivity. The energy gaps were determined for HOMO and LUMO orbitals of all three compounds i.e. C107, C160 and C165. The energy gap between the FMOs indicates the structure’s kinetic stability. Generally, large energy gaps indicate hard, stable and less reactive molecules whereas small energy gaps indicate soft, less stable and reactive molecule 54,55. The EHOMO-ELUMO energy gap of C165 (-4.680 eV) was the largest, corresponding to hard molecule with high stability and low chemical reactivity whereas the molecule with the smallest energy gap was C160 (-3.401 eV), suggesting a soft molecule with high chemical reactivity and low stability.Physiochemical descriptors of the pesticidesThe HOMO and LUMO energies are crucial parameters for determining molecular reactivity, including chemical reactivity descriptors38. The HOMO and LUMO values have been used to perform various other calculations, such as determining softness (s), chemical potential (µ), chemical hardness (η), global electrophilicity index (ω) and electronegativity (χ). Electronegativity measures a molecule’s ability to attract electrons, with higher χ values indicating a stronger tendency and capacity to attract electrons56,57. Chemical potential is the quantity of energy that is either released or absorbed as a result of a change in a species’ population58. The chemical hardness of a molecule indicates its resistance to the distribution of electron density and helps in understanding the molecular stability whereas softness is a key factor in determining the reactivity of a molecule. Chemical hardness quantifies the resistance to the polarization of electron density on atoms in a system58,59. Electrophilicity serves as a significant indicator of reactivity, allowing for a quantitative assessment of a molecule’s electrophilic character60,61,62. The physiochemical descriptors of all three compounds are summarized in Table 4. The most electronegative and electrophilic compound was determined to be C107, suggesting its potential to attract and accept electrons (most likely to interact with nucleophiles). Greater polarizability of a species corresponds to higher softness or lower hardness 63,64,65. All three compounds showed increased softness and decreased hardness, indicating greater chemical reactivity and lower stability. The order of increasing softness is C165 < C107 < C160 while the order of increasing hardness is C160 < C107 < C165. The low values of the chemical potential of studied molecules indicate stability. This means these molecules cannot break down into the elements from which they were initially formed. It is less likely for molecules with higher negative values of chemical potential to vary their chemical and/or physical states than those with large positive values66. Here, C107 was having highest negative chemical potential value of -0.167 Hartree/particle, suggesting that this compound/molecule will not be able to alter its states.Table 4 Calculated HOMO, LUMO energies and physiochemical descriptors.Post MD simulations analysisThe Density Functional Theory (DFT) studies of ligands C107, C160, and C165 post-MD simulations provide significant insights into their electronic properties and potential interactions with the 3FXI protein. The visual representations of the HOMO and LUMO for these ligands (Fig. 13) reveal crucial information about their chemical reactivity and stability. Table 5 mentions the electronic energy and dipole moment of the ligands post 300 ns MD simulations at the binding pocket of the protein. The optimization energies and dipole moments indicate that C165 has the highest dipole moment (13.679147 Debye), suggesting a higher polarity which could enhance its interaction with the 3FXI protein through dipole-dipole interactions.Fig. 13Visual representation of HOMO, LUMO orbitals and optimized geometry of C107, C160 and C165 post-MD simulations.Table 5 Computed thermodynamic parameters of the C107, C160 and C165 post 300ns MD simulation’s.The computed physiochemical descriptors (Table 6) show that C160 has the smallest HOMO-LUMO gap (0.11486 Hartree/particle or 3.1254 eV), indicating it is the most chemically reactive among the three ligands. In contrast, C107 and C165 have larger gaps (0.17322 Hartree/particle or 4.7135 eV and 0.1703 Hartree/particle or 4.6340 eV, respectively), suggesting higher stability58. The physiochemical descriptors further elucidate the reactivity and stability of the ligands. The chemical potential (µ) and electronegativity (χ) values are relatively similar across the ligands, indicating comparable tendencies to attract electrons. However, the hardness (η) and softness (s) values highlight differences in their resistance to deformation and reactivity. C160, with the highest softness value, is the most reactive, while C107 and C165 are more resistant to electronic deformation. The DFT calculations post-MD simulations reveal that C160 is the most chemically reactive ligand, while C165, with its high dipole moment, may have stronger interactions with the 3FXI protein.Table 6 Calculated HOMO, LUMO energies and physiochemical descriptors post 300 ns MD simulations.The comparison of DFT calculations for ligands C107, C160, and C165 pre- and post-MD simulations reveals significant changes in their electronic properties and potential interactions with the 3FXI protein. Pre-MD, the optimization energies for C107, C160, and C165 were − 2201.360829 Hartree, -2318.301570 Hartree, and − 2249.337167 Hartree, respectively. Post-MD, these energies slightly increased due to the protein environment. Notably, C165’s dipole moment increased from 10.890072 Debye pre-MD to 13.679147 Debye post-MD, suggesting enhanced polarity and stronger protein interactions.The HOMO-LUMO gaps (ΔE) also changed. Pre-MD, C160 had the smallest gap (0.125 Hartree/particle or 3.401 eV), indicating high reactivity. Post-MD, this gap decreased further to 0.11486 Hartree/particle (3.1254 eV), reinforcing its reactivity. Conversely, C107 and C165 showed slight increases in their HOMO-LUMO gaps post-MD, suggesting increased stability. The chemical potential (µ) and electronegativity (χ) values remained consistent, but the hardness (η) and softness (s) values highlighted differences in electronic deformation resistance and reactivity. C160, with the highest softness value post-MD, remained the most reactive, while C107 and C165 became more resistant to deformation.NCI analysis (post MD simulation)RDG isosurface plot is a powerful tool for visualizing and analyzing the non-covalent interactions within a molecular system, providing insights into the nature, location, and strength of these interactions. The blue region refers to the attractive interactions, typically associated with H-bonds or other strong non-covalent attractive interactions, and the green region is for weak van der Waals interactions or steric clashes. The red area corresponds to strong repulsive interactions due to steric repulsion between atoms67,68. Figure 14 shows the non-covalent interactions and RDG scatter graph of C107, C160, and C165. The RDG plot for C107 shows a balance between attractive (hydrogen bonding), weak van der Waals, and repulsive interactions. The presence of strong blue regions suggests that C107 forms significant hydrogen bonds or other non-covalent interactions with the amino acids in the system. Meanwhile, the red regions indicate areas where steric repulsion might be significant, possibly due to the proximity of certain atoms. The RDG scatter plot for C160, like C107, reveals a combination of attractive, van der Waals, and repulsive interactions. Differences in the green and red regions might suggest that C160 has a different balance of van der Waals and steric interactions. C165 has fewer attractive interactions in the blue region, which might indicate weaker or fewer hydrogen bonds. The green and red regions are consistent with the other compounds, suggesting similar weak and repulsive interactions69,70.Fig. 14Non-covalent interactions and RDG scatter graph of a C107, b C160, and c C165.Analysis of ADME properties of the pesticidesThe three pesticides, C107, C160, and C165, exhibit distinct physicochemical and pharmacokinetic properties that can influence their toxicity and behaviour within biological systems as shown in Table 7. C107, with a molecular weight of 488.77 g/mol, C160 at 665.73 g/mol and C165 at 539.52 g/mol, vary significantly in size, which directly impacts their ability to cross cell membranes and potentially affects their distribution within the body. The number of heavy atoms, ranging from 33 in C107 to 47 in C160, reflects the molecular complexity, which may also influence their solubility and interaction with biological targets. The ability to form hydrogen bonds is represented by the number of H-bond acceptors and donors and it plays a crucial role in the solubility and permeability of these pesticides. C160, with 14 H-bond acceptors and 7 donors, has a more significant potential for water solubility but might face challenges in crossing lipid membranes, compared to C107 and C165, which have fewer acceptors and donors. The topological polar surface area (TPSA) further highlights this difference, with C160 having a TPSA of 230.99 Ų, significantly larger than C107 (67.43 Ų) and C165 (100.85 Ų), suggesting that C160 may have lower membrane permeability and potentially reduced bioavailability. The lipophilicity, indicated by the Consensus Log Po/w, reveals that C107 (5.77) and C165 (4.81) are more hydrophobic, which may enhance their ability to permeate cell membranes but could limit their solubility in aqueous environments. Conversely, C160’s Log Po/w of -0.52 suggests a more hydrophilic nature, possibly reducing its ability to cross lipid-rich barriers like cell membranes, but enhancing its solubility in biological fluids. The Log S (ESOL) values, indicating solubility, further support this, with C160 being more soluble (-2.92) compared to C107 and C165, which has much lower solubility (-6.69 and − 5.82, respectively). All three compounds exhibit low gastrointestinal (GI) absorption, which could limit their effectiveness and bioavailability when administered orally. None of the pesticides are permeant to the blood-brain barrier (BBB), reducing concerns about central nervous system toxicity. However, their interactions with transport proteins and metabolic enzymes vary, as C107 and C165 are substrates for P-glycoprotein (P-gp), potentially limiting their intracellular concentrations. Additionally, C107 and C165 inhibit multiple cytochrome P450 enzymes (CYP2C19, CYP2C9, CYP2D6, and CYP3A4 in C165), raising the risk of drug-drug interactions and altered metabolic profiles, which could influence their toxicity. Skin permeability, as indicated by Log Kp, is notably low for all three compounds, suggesting that dermal exposure may not be a significant route of toxicity. According to Lipinski’s Rule of Five, C107 and C165 each have one violation, while C160 has three, indicating potential challenges in oral bioavailability, particularly for C160. The bioavailability score further underscores these differences, with C107 and C165 having a moderate score of 0.55, while C160 has a much lower score of 0.17, indicating that C160 may have the least oral bioavailability. Overall, the physicochemical properties of these pesticides suggest that C160, with its higher molecular weight, larger polar surface area, and multiple violations of Lipinski’s rules, may have the lowest bioavailability and possibly reduced systemic toxicity, but could still pose risks through specific pathways. In contrast, C107 and C165, with their higher lipophilicity and enzyme inhibition potential, may have more pronounced interactions within the body, influencing their toxicity profiles and necessitating careful consideration of their pharmacokinetic behaviour.Table 7 Physiochemical parameters of compounds obtained through ADME studies.The DFT calculations provided critical insights into the electronic properties of the pesticides C107, C160, and C165, which are essential for understanding their reactivity, stability, and potential interactions with biological targets like TLR4. Specifically, the energy difference (ΔE) between HOMO and LUMO, along with parameters like chemical potential (µ), electronegativity (χ), hardness (η), and global electrophilicity index (ω), are key indicators of the chemical reactivity and stability of these compounds. The compound C107 shows moderate global electrophilicity (0.172 Hartree) and moderate softness (6.172 Hartree), suggesting a balanced reactivity. The ADME profile shows a high molecular weight (488.77 g/mol), which might affect its GI absorption, and it does have some CYP enzyme inhibition activity, suggesting possible drug-drug interactions. However, its bioavailability score is relatively high (0.55). C160 exhibits the highest electrophilicity (0.153 Hartree) and softness (8.064 Hartree), C160 is the most reactive, which could correlate with stronger biological activity but also a potential for toxicity. The ADME profile, however, shows several violations of Lipinski’s rule, a low bioavailability score (0.17), and poor GI absorption, indicating that despite its reactivity, C160 may face significant challenges as a drug candidate. C165 has a ΔE of 0.172 Hartree or 4.680 eV and moderate global electrophilicity (0.136 Hartree), suggesting it is reactive and stable. Its ADME profile indicates a high molecular weight (539.52 g/mol) and some CYP enzyme inhibition, similar to C107, but also a moderate bioavailability score (0.55), albeit with some challenges related to metabolism.
