Exploring the therapeutic potential of benzodioxane carboxylic acid-based hydrazones: Structural, computational, and biological insights

2.2.1. Synthesis of benzodioxane-6-carboxylic acid methyl ester (2)

A mixture consisting of 3 g (16.65 mmol) of 2,3-dihydrobenzo[1,4] dioxine-6-carboxylic acid, 20 mL of methanol (494 mmol), and 2 mL of concentrated sulfuric acid (98%) was refluxed for 3-4 h in a round-bottom flask. Upon successful completion of the reaction, the mixture was neutralized to a pH of 7–8 using a 10% aqueous sodium bicarbonate solution and allowed to cool to room temperature. Precipitation was induced by adding 200 mL of ice-cold water. The resultant precipitate was filtered, washed with water, dried, and crystallized, yielding the product with an 84.6% yield. The progress of the reaction was monitored by TLC using a solvent system of n-hexane/ethyl acetate (90:10 v/v), and spots were visualized under UV light [35,36].

2.2.2. Synthesis of benzodioxane-6-carboxylic acid hydrazide (3)

The following steps were performed: A solution of 2.5 g (12.88 mmol) of benzodioxane-6-carboxylic acid methyl ester and 2 mL of hydrazine hydrate (41.2 mmol) in 20 mL of ethanol (494 mmol) was refluxed for approximately 3 h. The progress of the reaction was monitored by TLC using a solvent system of n-hexane/ethyl acetate (90:10 v/v), and spots were visualized under a UV lamp at 254 nm. After refluxing, 200 mL of ice-cold water was added to the mixture to induce precipitation. The mixture was then filtered, washed with water, and dried, yielding a precipitate with an 81.60% yield [36].

2.2.3. Synthesis of benzodioxane-6-carboxylic acid hydrazide hydrazones (4-7)

A mixture of benzodioxane-6-carboxylic acid hydrazide (3), 1 mmol of the appropriate aldehydes (4-(trifluoromethyl)benzaldehyde, 2,4-dichlorobenzaldehyde, 4-methoxybenzaldehyde, furan-2-carbaldehyde), and 15 mL of ethanol was stirred under reflux conditions. The progress of the reaction was monitored by TLC using a mobile phase of n-hexane/ethyl acetate (90:10 v/v), with spots visualized under UV light. Upon completion, the solvent was removed under vacuum, yielding a thick liquid. The mixture was then allowed to stand at 0–5°C to promote crystallization. The resulting crystals were filtered, washed with cold ethanol, and dried to obtain hydrazide-hydrazones. The synthesized compounds were analyzed using various spectral methods, including UV, IR, ¹H NMR, ¹³C NMR, and EI-MS.

Using similar techniques and equimolar quantities of reactants, four molecules were synthesized with excellent yields. The physical and analytical parameters of the synthesized compounds have been provided in Table 1, with the compounds labeled by their code names (i.e., 4, 5, 6, and 7). The synthetic process for the preparation of the title compounds has been illustrated in Figure 1.

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Figure 1.

Synthesis of Compounds (4-7) are derived from 2,3-dihydrobenzo[1,4]dioxine-6-carboxylic acid. Esterification, hydrazide formation, and condensation reactions with a variety of aldehydes were among the numerous phases that comprise the scheme 1; A unique compound was produced by each condensation reactions (4-7), using different aromatic aldehydes such as 4-(trifluoromethyl) benzaldehyde, 4-methoxybenzaldehyde, 2,4-dichlorobenzaldehyde, and thiophene-2-carbaldehyde.

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2.3.1. Molecular docking studies

2.3.2. Stability assessment of protein–ligand complexes through MD simulation

A molecular docking experiment identified the top-ranked complexes of the synthesized compounds with various receptors. To further improve and stabilize these complexes, molecular dynamics (MD) simulations were performed. First, the Amber 17 Antechamber module was used to assign partial atomic charges to the ligands. The protein and ligand were prepared using the Leap module by adding missing hydrogens, neutralizing the system, solvating the complexes, and generating parameter and coordinate files for the simulations. The General Amber Force Field (GAFF) was applied to model the interactions between the proteins and ligands. Chlorine or sodium ions were used to neutralize the protein. The complex was solvated with an octahedral TIP3P water model at a 10.0 Å buffer distance. The final complexes were saved in PDB format.

To minimize energy and eliminate steric effects, three reduction steps were performed: First, the protein and ligand were optimized with solvated water and ions. Then, the backbone of the protein's amino acids and bases was refined. Finally, the entire protein-ligand complex was fully optimized. Each reduction phase consisted of 2500 steps of steepest descent and 5000 steps of conjugate gradient optimization. After the system was minimized, it was gradually heated from 0 to 300K. Langevin dynamics were applied with a contact frequency of 1 ps⁻¹ and a force constant of 10 kcal/mol Ų. MD simulations were conducted in the NPT ensemble at 300K and 1 atm for 100 ns, allowing the system to equilibrate.

The Amber 17 CPPTRAJ tool was used to analyze key features after the MD simulations, including RMSD, root mean square fluctuation (RMSF), hydrogen bonds, and distances. For additional MD analysis, the method described by Arantes et al. [49] was employed, incorporating more complex metrics such as 2D-RMSD (shape changes), radius of gyration (Rg), solvent accessible surface area (SASA, indicating exposure to water), principal component analysis (PCA, identifying motion patterns), and cross-correlation (DCCM). The last 50 ns of the MD simulations were carefully examined to assess the stability and activity of the complexes [50].

2.3.3. Binding free energy analysis of protein–ligand complexes

Using the images obtained during MD simulations, binding free energies (BFE) were calculated to assess the energetic and structural characteristics of each complex. The molecular mechanics MMGB/PBSA modules within the AMBER17 software were employed for BFE calculations in a molecular mechanics-based investigation. Using data from one thousand snapshots taken during the last twenty nanoseconds of the simulations, the energies of six complexes were determined. The absolute difference in total energies of the inhibitor-complex system (ΔGcom) was computed through BFE analysis.

The steps for calculating entropy (TΔS), molecular mechanics energy (ΔEmm), and solvation free energy (ΔGsol) using BFE were outlined in a prior publication by Chohan et al. [51]. The energy fragmentation of molecular mechanics was further explored in terms of van der Waals energies (ΔEvdW), non-bonded electrostatic energies (ΔEele), and solvation free energy (ΔGsol). These variables were further subdivided into polar solvation energies and non-polar solvation contributions. To calculate the electrostatic (ΔGele), polar (ΔGele, sol), nonpolar (ΔGnonpol, sol), and van der Waals (ΔGvdW) decomposition parameters. BFE energies were broken down into individual residue contributions for inhibitor interactions. Additionally, BFE images were utilized to examine these decomposition parameters.

2.3.4. DFT analysis of HOMO-LUMO, molecular reactivity, and structural optimization

The evaluation of HOMO/LUMO and the determination of optimal chemical structures is a promising approach. Density functional theory (DFT) calculations were performed following the previously outlined procedure, with some variations. The calculations were conducted in both gas and solvent phases using the Gaussian 09 software package (Revision E.01) with its standard configurations. The Becke-3-Parameter-Lee-Yang-Parr (B3LYP) functional and the SVP basis set were employed to achieve high accuracy in vibrational spectra [52,53]. This theoretical framework is effective for determining the electronic structures of atoms and molecules.

The present investigation aimed to ascertain various key parameters, including optimized geometric characteristics, frontier molecular orbital (FMO) energies, as well as global and local reactivity indices, and molecular electrostatic potential (MEP). The generated checkpoint files were analyzed using the Gauss View 6 program [54,55].

2.3.5. Pharmacokinetic and toxicological evaluation of synthesized compounds using ADMET predictions

The pharmacokinetic profile of chemically prepared compounds was evaluated biologically through Absorption, distribution, metabolism, excretion and toxicity (ADMET) studies, which are considered a crucial tool in drug development. Specific computational models were standardized to reduce testing time and generate the ADMET profile for each compound. The pharmacokinetic profiles of compounds (4-7) were screened using the Swiss ADME online server (http://www.swissadme.ch/) to assess absorption, distribution, metabolism, and excretion. Predictions regarding carcinogenicity and the Ames toxicity profile of the synthesized compounds were also made. Additionally, the cytotoxicity profile and cosmetic risk assessment of these compounds were evaluated using the online tool ADMETSAR 3.0 (https://lmmd.ecust.edu.cn/admetsar2/).

2.3.6. Drug-like property evaluation of synthesized compounds

The AdmetLab 3.0 (https://admetlab3.scbdd.com/server/evaluation) and admetSAR 3.0 (https://lmmd.ecust.edu.cn/admetsar2/) online tools were used to evaluate the drug-like properties of the compounds. These compounds were also assessed for water solubility (log S), hydrogen bond donor and acceptor capacity, n-octanol/water partition coefficient (Log Po/w), blood–brain barrier (BBB) permeability, and P-glycoprotein (P-gp) substrate potential. The bioavailability and pharmacokinetics of each compound were determined using Lipinski's rule of five. ChemDraw 22.2.0 was used to generate SMILES, which were then applied in the online software to evaluate the drug-like characteristics and physicochemical properties of all synthesized compounds.

2.4.1. α-amylase inhibition assay

The 3,5-dinitrosalicylic acid (DNSA) procedure was commonly used to test α-amylase inhibition, with only minor modifications [56]. To prepare the two concentrations (100 and 400 μg/mL), 5 mg of the synthetic compounds were initially dissolved in a small amount of 10% DMSO and then mixed with 20 mM sodium phosphate buffer and 6 mM NaCl at pH 6.9. The reaction mixture, containing 200 μL of α-amylase solution (2 units/mL) and 200 μL of the test compounds, was incubated at 30°C for 10 mins. Afterward, 200 μL of a 1% starch solution (w/v) in distilled H₂O was added to the test samples, which were then incubated for 3 mins. To stop the reaction, 200 μL of DNSA reagent (12 g of Rochelle salt in 8 mL of 2 M NaOH and 20 mL of 0.096 M 3,5-DNSA solution) was added, followed by heating in a water bath for 10 mins at 90°C. After cooling, the absorbance of all reaction mixtures was measured at 540 nm using an ultraviolet-visible spectrophotometer. A blank with 100% enzyme activity was prepared by mixing 200 μL of buffer (20 mM sodium phosphate buffer with 6.7 mM sodium chloride, pH 6.9) in place of the synthetic compounds. Additionally, a blank reaction without the enzyme solution was performed at each concentration using the synthesized compounds. A positive control was prepared by using acarbose (100 μg/mL–2 μg/mL) as the standard drug, and the reaction was carried out in the same manner as with the prepared compounds. The following equation was used to compute the α-amylase activity, expressed as the percentage of inhibition, to determine the α-amylase activity (Eq. 1):

Abs_control refers to the absorbance at 540 nm in the control sample without the synthesized compounds, while Abs Treatment corresponds to the absorbance in the treatment with the prepared compounds. The IC₅₀ was determined through linear regression analysis.

2.4.2. β-glucosidase inhibition assay

The inhibition screening for β-glucosidase was performed using p-nitrophenyl-D-glucopyranoside (PNPG) as a substrate. β-glucosidase from E. coli was dissolved in a sodium phosphate buffer (pH 6.8). A total volume of 1 mL of sodium phosphate buffer, 100 µL of each test sample [dissolved in 1% Dimethyl sulfoxide (DMSO)], and 200 µL of enzyme (1.5 U/mL) were combined and incubated at 37°C for 5 mins. Subsequently, 50 µL of p-nitrophenyl-β-D-glucopyranoside (dissolved in buffer solution) was added. The resulting mixture was incubated for 30 mins, and 1750 µL of 1M sodium carbonate solution was added to terminate the reaction. DMSO (1%) was used as a negative control. To assess the enzyme activity of the test compounds against β-glucosidase, the p-nitrophenol produced from p-nitrophenyl-β-D-glucopyranoside was analyzed at a λmax of 405 nm [56]. The % inhibition of β-glucosidase was calculated using the following formula (Eq. 2):

IC₅₀ value was calculated by the linear regression analysis.

2.4.3. Acetylcholinesterase inhibition assay

This was assessed according to the modified spectrophotometric procedure proposed by Ferreira et al. [57]. To determine the optimal concentration for nonenzymatic hydrolysis of acetylcholine, a blank cuvette was prepared by mixing 500 µL of 3 mM DTNB (5,5'-dithio-bis-(2-nitrobenzoic acid)) solution (in 0.1 M potassium phosphate, pH 8), 100 µL of 15 mM acetylcholine iodide (in water), 275 µL of 0.1 M potassium phosphate (pH 8), and 100 µL of synthesized acetylcholine at varying concentrations (0.1 mg/mL, 0.5 mg/mL, 1.0 mg/mL). In the reaction cuvette, 25 µL of buffer was replaced with 25 µL of acetylcholinesterase (AChE) solution (0.16 U/mL). The resultant solutions were placed in a spectrophotometer. The thiocholine produced during the hydrolysis of acetylcholine rapidly interacts with DTNB, forming a yellow-colored product. The reaction was monitored at 405 nm for 5 min, with absorbance measurements taken every minute. The assays were performed in triplicate. The percentage inhibition of acetylcholinesterase was calculated using the following formula (Eq. 3):

IC₅₀ value was calculated by linear regression analysis.

2.4.4. Peroxidase inhibition assay

The spectrophotometric determination of peroxidase activity of the test compounds was performed using hydrogen peroxide as the first substrate and a hydrogen donor, such as guaiacol, as the second substrate. Substrate I was prepared by mixing 1.0 mL of buffer containing 0.2 mM H₂O₂ with 10 µg of enzyme. Substrate II, a similar buffer containing hydrogen donors at different doses and optimal pH, was added. Under ideal conditions, activity was assessed by varying the second substrates to study the affinities of test compounds for different substrates. Both temperature and pH influence enzyme activity. To assess pH-dependent peroxidase activity, the desired pH (ranging from 0.5 to 12.0) was achieved by incubating 10 µg of test compounds with the buffer. The mixture was equilibrated at ambient temperature for 30 mins, and guaiacol, along with 2 mM H₂O₂, were added as substrates I and II, respectively. The assay was conducted at 25°C, following the procedure described. A control assay was run at each pH without an enzyme in the reaction mixture and used as reference blanks. The experiment was conducted using a 50 mM buffer. Similarly, the enzyme's peroxidase activity was assessed in relation to temperature. The reaction mixture, containing 10 mg of test compounds, was incubated in 50 mM acetate buffer, pH 5.0, at varying temperatures (20°C to 80°C) for 15 min, after which the activity was tested [58]. The percentage inhibition of peroxidase was calculated using the following formula (Eq. 4):

IC₅₀ value was calculated by the linear regression analysis.

2.4.6. Tyrosinase inhibition assay

To evaluate the tyrosinase inhibitory activity of the target compounds, an assay was performed as previously reported with slight modifications [60]. A mixture was prepared by adding 140 µL of phosphate buffer (20 mM, pH 6.8), 30 units of tyrosinase, and 20 µL of test compounds. Then, 20 µL of 4-dihydroxyphenylalanine (L-DOPA, 0.85 mM) was added to the mixture. The test compounds were dissolved in DMSO to prepare stock solutions. The mixture was incubated at 25°C for 20 mins. Finally, the absorbance of dopachrome at 475 nm was measured. The experiment was repeated independently three times for each test compound. The percentage inhibition was calculated using the following formula (Eq. 6):

Activity was expressed as sample concentration that gave a 50% inhibition in enzyme activity (IC₅₀) and calculated by linear regression analysis.

2.4.7. Serine protease inhibition assay

The assay for measuring protease activity in test compounds was based on the release of tyrosine from casein, with minor modifications [61]. A 25 mL Erlenmeyer flask was filled with 1400 µL of a 1% casein solution, 5 mL of Tris buffer, and 1 µL of the synthesized compounds. The mixture was vortexed briefly to ensure thorough mixing, then incubated in a water bath at 50°C for 2 h. After incubation, 300 µL of trichloroacetic acid (TCA) was immediately added to stop the enzyme activity and precipitate any remaining casein. The mixture was thoroughly mixed and centrifuged at 10,000 rpm for 10 mins at 4°C. Following centrifugation, 7.5 mL of alkaline reagent was added, and 5 mL of the clear supernatant was transferred to a new flask. The solution was left to stand at room temperature for 15 mins, after which the flask was shaken, and 100 µL of Folin-Ciocalteu reagent (FC) was added. The mixture was then filtered and incubated for 1 h. Finally, the absorbance was measured at 700 nm using a spectrophotometer. The percentage inhibition of serine protease was calculated using the following formula (Eq. 7):

The IC₅₀ value was calculated by linear regression analysis.

2.5.1. Cell lines and cell culture

The cell lines were cultured in Eagle's minimal essential medium supplemented with 5% fetal bovine serum (v/v). To ensure proper growth and viability, the cultures were maintained in an incubator at 37°C with 5% CO₂. The HeLa cell lines and prostate cancer cell lines (PC3) were kindly provided by the Husein Ebrahim Jamal Research Institute of Chemistry, International Center for Chemical and Biological Sciences, University of Karachi, Karachi, Pakistan.

2.5.2. Anticancer activity evaluation

A well-known colorimetric technique, 3-[4,5-dimethylthiazole-2-yl]-2,5-diphenyltetrazolium bromide (MTT), was employed to assess the anticancer effects of the composites in 96-well flat-bottom microplates [62,63]. The medium used for culturing HeLa cells (cervical cancer cell line) and prostate cancer cells (PC3) was Eagle's Minimal Essential Medium. The cells were cultured in 75 cm² flasks in a 5% CO₂ incubator at 37°C, supplemented with 5% fetal bovine serum (FBS), 100 IU/mL penicillin, and 100 µg/mL streptomycin. Exponentially growing cells were harvested, counted using a hemocytometer, and diluted with the specific medium. The cell culture was prepared to a concentration of 6 × 10⁴ cells/mL and transferred to 96-well plates at 100 µL/well. After incubating overnight, the medium was removed, and fresh medium containing varying concentrations of compounds (1-30 µM) was added, followed by 200 µL of MTT (0.5 mg/mL). The plates were incubated for an additional 4 h after an initial 48-h incubation period. Subsequently, 100 µL of DMSO was added to each well. The compounds (4-7) were dissolved in 1% DMSO at a concentration of 100 µg/mL, and doxorubicin, the standard drug, was prepared in the same manner and used as a positive control. Absorbance was measured at 570 nm to monitor the reduction of MTT to formazan in the cells, using a microplate reader (SpectraMax Plus, Molecular Devices, CA, USA). The anticancer activity for HeLa and prostate cell lines was determined by calculating the concentration that caused 50% inhibition (IC₅₀). The following formula was used to calculate the percentage of inhibition (Eq. 8):

% Inhibition was equal to results organized by using the Soft-Max Pro software (Molecular Device, USA).

2.6. Antimicrobial activity

Compounds (4-7) were screened in vitro for antibacterial and antifungal activities using the agar well diffusion method.

3.3.1. (4-(trifluoromethyl) benzaldehyde)-2,3-dihydrobenzo[1,4]dioxine-6-carbohydrazide (4)

¹H NMR (400 MHz, DMSO-d6) 11.65 (s, 1H), 8.64 (s, 1H), 7.65 (d, J = 5.0 Hz, 1H), 7.47 – 7.40 (m, 3H), 7.13 (dd, J = 5.1, 3.6 Hz, 1H), 6.97 (d, J = 8.3 Hz, 1H), 4.34 – 4.25 (m, 4H).

¹³C NMR (101 MHz, DMSO-d6) 162.51, 146.98, 143.48, 142.94, 139.71, 131.18, 129.25, 128.30, 121.61, 117.46, 117.00, 64.87, 64.49, 40.59, 40.38, 40.17, 39.96, 39.76, 39.55, 39.34.

3.3.2. (4-methoxybenzylidene)-2,3-dihydrobenzo [1,4] dioxine-6-carbohydrazide (5)

¹H NMR (400 MHz, DMSO-d6) 11.70 (s, 1H), 8.41 (s, 1H), 7.49 – 7.42 (m, 2H), 7.36 (t, J = 8.0 Hz, 1H), 7.26 (d, J = 5.7 Hz, 2H), 7.03 – 6.95 (m, 2H), 4.30 (q, J = 5.3 Hz, 4H), 3.80 (s, 3H).

¹³C NMR (101 MHz, DMSO-d6 162.66, 160.00, 147.68, 147.01, 143.48, 136.32, 130.40, 126.61, 121.67, 120.45, 117.46, 117.06, 116.56, 111.58, 64.87, 64.49, 55.62.

3.3.3. (2,4-dichlorebenzylidene)-2,3-dihydrobenzo [1,4] dioxine-6-carbohydrazide (6)

¹H NMR (400 MHz, DMSO-d6) 11.96 (s, 1H), 8.78 (s, 1H), 8.00 (d, J = 8.5 Hz, 1H), 7.68 (d, J = 2.2 Hz, 1H), 7.53 – 7.44 (m, 3H), 6.98 (d, J = 8.2 Hz, 1H), 4.31 (q, J = 5.5, 2.9 Hz, 3H).

¹³C NMR (101 MHz, DMSO-d6) 162.66, 147.20, 143.52, 142.52, 135.40, 134.23, 129.81, 128.46, 126.22, 121.77, 117.50, 117.10, 64.89, 64.49, 40.59, 40.38, 40.17, 39.96, 39.75, 39.55, 39.34.

3.3.4. (Thiophene-2-ylmethylene)-2,3-dihydrobenzo [1,4] dioxine-6-carbohydrazide (7)

¹H NMR (400 MHz, DMSO-d6) 11.65 (s, 1H), 8.64 (s, 1H), 7.65 (d, J = 5.0 Hz, 1H), 7.47 – 7.40 (m, 3H), 7.13 (dd, J = 5.1, 3.6 Hz, 1H), 6.97 (d, J = 8.3 Hz, 1H), 4.34 – 4.25 (m, 4H).

¹³C NMR (101 MHz, DMSO-d6)162.51, 146.98, 143.48, 142.94, 139.71, 131.18, 129.25, 128.30, 121.61, 117.46, 117.00, 64.87, 64.49, 40.59, 40.38, 40.17, 39.96, 39.76, 39.55, 39.34.

3.7.1. Ligand preparation and structural characterization of synthesized compounds

The ligand preparation process for Compounds 4-7 involved generating their 2D and 3D structures along with their respective SMILES representations, as presented in Table 2. This process was undertaken to ensure structural accuracy and suitability for molecular docking and dynamic simulation studies.

Table 2. The docking score of the synthesized compounds (4-7) with different target proteins.

Codes	2D Structure	3D Structure	SMILES	Gold docking scores
				Acetylcholinesterase	Peroxidase	β-glucosidase	Serine protease	Androgen	Estrogen
Compound 4			O=C(N/N=C\1C=CC=C(C(F)(F)F)C=C1)C2=CC(OCCO3)=C3C=C2	37.7506	45.4390	38.6242	42.7933	45.8561	49.5151
Compound 5			O=C(N/N=C\1C=CC=C(OC)C=C1)C2=CC(OCCO3)=C3C=C2	40.3568	45.9492	47.1030	46.3432	53.3481	57.2130
Compound 6			O=C(C1=CC2=C(C=C1)OCCO2)N/N=C\3C=C(C(Cl)=C(Cl)C3)Cl	37.6500	45.3059	47.0415	48.4475	49.1189	56.6566
Compound 7			O=C(N/N=C\1C=CC=CS1)C2=CC(OCCO3)=C3C=C2	38.0980	44.0606	46.2873	45.8053	54.9091	55.6677

3.7.2. Docking analysis and binding affinities of synthesized compounds

The GOLD docking scores of synthesized compounds (4–7) against six target enzymes were evaluated to determine their binding affinities and potential inhibitory activities, as shown in Table 2. Higher docking scores indicated stronger binding and better docking performance. Compound 5 exhibited the highest binding affinity for acetylcholinesterase (40.3568), β-glucosidase (47.1030), peroxidase (45.9492), and the estrogen receptor (57.2130), suggesting broad inhibitory properties. Compound 6 demonstrated the strongest binding affinity for serine protease (48.4475), indicating its potential as a protease inhibitor. Compound 7 showed the most robust binding affinity for the androgen receptor (54.9091), highlighting its potential as an androgen receptor modulator. These findings suggest that Compound 5 possesses the most extensive inhibitory potential, while Compounds 6 and 7 show significant promise for specific targets, warranting further investigation.

3.7.3. Protein-ligand interaction profiles of the most active ligand complexes

3.7.3.1. Protein-ligand interaction analysis in acetylcholinesterase binding with compound 5

The protein-ligand interaction profile highlighted the distances between specific residues within the protein and the ligand at designated positions. The receptor's hydrophilic and hydrophobic properties were visualized through the interaction profile. Non-polar molecules or molecular groups formed hydrophobic interactions (depicted by orange lines) to avoid contact with water. Hydrophobic amino acids (PRO59A, PRO135A, PRO137A, GLN171A) were frequently present within the protein's binding pocket, interacting with the ligand's hydrophobic regions and stabilizing it within the binding site of acetylcholinesterase.

Additionally, hydrogen bonds were established between the ligand and amino acid residues (TYR136A, GLN171A, ARG174A) in the protein's binding site. These interactions, represented by dotted blue lines, significantly influenced binding affinity and specificity, playing a critical role in stabilizing the ligand within the binding site. These interactions are vital for molecular recognition, as well as protein structure and function, and are crucial for effective protein-ligand binding, as illustrated in Figure 8.

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Figure 8.

(a) Ligand was binding in showed in the active site in 3D protein structure (Acetylcholinesterase complex with compound 5). (b) A comprehensive 3D model revealed specific interactions, such as hydrogen bonds, hydrophobic interactions, and π-cation interactions, between ligand and active site residues. (c) The 2D interaction map showed many interactions between the ligand and the residue, including the hydrogen bonds, aromatic stacking, and van der Waals forces. (d) Distances and interacting residues were among the most important interaction mentioned in the table.

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3.7.3.2. Protein-ligand interaction analysis of androgen receptor complex with compound 7

The interaction analysis of the protein-ligand complex with compound 7 revealed that the ligand forms critical stabilizing interactions within the protein's active site. Hydrophobic interactions were observed with the residues MET746A (3.32 Å) and THR878A (3.25 Å), which played a significant role in stabilizing the ligand, as shown in Figure 9.

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Figure 9.

This graphic highlighted the results of protein-ligand complex molecular interaction investigation. (a) Ligand was shown to be located within the active site in the three-dimensional protein structure, which revealed the binding pocket; (b) The comprehensive 3D interaction profile revealed that ligand formed the hydrogen bonds and hydrophobic interactions with important residues MET746A and THR878A; (c) The 2D interaction map brought attention to the presence of the hydrogen bonding, π-interactions, and the van der Waals interactions with certain residues; (d) Specifics of the hydrophobic interactions, such as residues, distances, and ligand/protein atom indices, were reported in a table.

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3.7.3.3. Protein-ligand interaction analysis of estrogen receptor complex with compound 5

The interaction profile of the estrogen complex with Compound 5 revealed key interactions between the ligand and the active site residues of the protein. Hydrophobic interactions with residues such as PRO324A, MET355A, and PHE445A significantly contribute to stabilizing the ligand within the binding pocket. Additionally, hydrogen bonds formed with key residues, including LEU327A, ARG394A, and LYS448A, play a crucial role in defining the ligand's binding affinity and specificity, as shown in Figure 10.

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Figure 10.

(a) The 3D structure of the protein highlighted the ligand bound within the binding pocket Estrogen complex with compound 5. (b) A detailed 3D interaction profile showcased hydrophobic interactions with residues PRO324A, MET355A, and PHE445A, along with hydrogen bonds formed with residues LEU327A, ARG394A, and LYS449A. (c) The 2D interaction map illustrated specific interactions, including the van der Waals forces, hydrogen bonds, and the π-interactions with residues such as GLU325, GLY329, and ILE386. (d) The interaction table provided detailed information on hydrophobic interactions, hydrogen bonds, and their respective residue distances, ligand atoms, and protein atoms.

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3.7.3.4. Protein-ligand interaction profile of β-glucosidase complex with compound 5

Molecular interaction studies of the ligand-protein complex revealed that both hydrophobic and hydrogen bonding interactions significantly impact the ligand’s stability within the binding pocket. Hydrophobic interactions with residues VAL341X, ALA380X, ASP384X, GLN385X, and TYR390X stabilize the ligand within the hydrophobic environment of the protein’s active site. The ligand’s binding affinity and specificity were further enhanced by hydrogen bonds formed with residues GLN376X, THR377X, GLN385X, and GLN386X. Both the 3D interaction profile and the 2D interaction map confirmed that the ligand was optimally positioned within the binding pocket. Additionally, these profiles highlighted the critical role of van der Waals forces, conventional hydrogen bonds, and alkyl interactions in ligand binding. The interaction table, detailing their distances and angles, provides further confirmation that these interactions contribute to the ligand’s stability and binding efficiency, as shown in Figure 11.

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Figure 11.

(a) The protein's three-dimensional form revealed ligand bound within the binding pocket. (b) The thorough 3D interaction profile underlined hydrophobic interactions with residues VAL 341X, ALA380X, ASP 384X, hydrogen bonds with GLN376X, GLN385X, and THR377X. (c) The 2D interaction map displays van der Waals forces, hydrogen bonds, and alkyl interactions helping to bind the ligand. (d) The interaction table compiled hydrophobic and hydrogen bond interactions together with their corresponding lengths, angles, and implicated residues.

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3.7.3.5. Protein-ligand interaction profile of peroxidase complex with compound 5

Hydrophobic and hydrogen bonding interactions played a crucial role in stabilizing the ligand within the binding pocket, as demonstrated by the molecular interaction study of the ligand-protein complex shown in Figure 12. Hydrophobic interactions with residues VAL341X, ALA380X, ASP384X, GLN385X, and TYR390X help stabilize the ligand in the hydrophobic environment of the protein's active site. The binding affinity and specificity of the ligand were further enhanced through hydrogen bonds formed with residues GLN376X, THR377X, GLN385X, and GLN386X. Both the 3D interaction profile and the 2D interaction map confirmed that the ligand was optimally positioned within the binding pocket. Additionally, the 3D profile highlighted the importance of van der Waals forces, conventional hydrogen bonds, and alkyl interactions, all of which were essential for effective binding.

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Figure 12.

(a) The spatial arrangement was illustrated by 3D structure of protein, which showed ligand bound within the binding pocket; (b) The detailed 3D interaction profile illustrated hydrophobic interactions with residues THR40A, MET123A, ALA147A, and others, as well as a hydrogen bond with VAL42A; (c) The 2D interaction map demonstrated critical interactions, including hydrophobic interactions, van der Waals forces, and the π-alkyl interactions with residues like THR148 and MET123; (d) Interaction table provided a comprehensive summary of hydrophobic and hydrogen bond interactions, including the precise distances and participating residues.

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3.7.3.6. Protein-ligand interaction profile of serine protease complex with compound 6

The protein-ligand complex exhibited strong hydrophobic and π-alkyl interactions that helped stabilize the ligand within the binding pocket, as shown in Figure 13. According to the interaction table, residues LYS278A, LEU279A, and GLN280A formed hydrophobic interactions, contributing to the ligand's consistent orientation and placement within the active site. The binding stability was further reinforced by the 2D interaction map, which revealed van der Waals forces and π-alkyl interactions with residues such as ALA23, PHE281, and LEU145. The presence of strong hydrophobic and van der Waals contacts ensured that the overall binding was only minimally affected by an unfavorable donor-donor interaction with GLN280A. The structural configuration of the binding pocket enabled effective engagement of the ligand, enhancing its potential affinity and specificity for the protein.

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Figure 13.

(a) Protein's 3D structure revealed the ligand situated within the binding pocket. Serine protease receptor complex with compound 6; (b) A comprehensive 3D interaction profile demonstrated hydrophobic interactions involving residues LYS278A, LEU279A, and GLN280A, in addition to an unfavorable donor-donor interaction with GLN280A; (c) The 2D interaction map indicated significant van der Waals and π-alkyl interactions with residues ALA23, PHE281, and LEU145, as well as an unfavorable contact; (d)The interaction table presented quantitative specifics of hydrophobic interactions, encompassing residue distances and the atoms of the ligand and protein involved.

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3.8.1. Structural dynamics and binding interactions of protein-ligand complexes

For each protein-ligand combination, the flexibility of individual residues was depicted by the RMSF. Lower RMSF values indicated stable, inflexible regions, typically seen in secondary structural elements like α-helices and β-sheets, while peaks in RMSF highlighted highly flexible regions, often found in loops or terminal residues. The Rg depicted the rigidity of the protein throughout the simulation, with lower Rg values suggesting a more tightly packed structure, while larger values indicated expansion. The SASA measured the surface area exposed to the solvent, reflecting ligand accessibility or conformational changes.

The structural dynamics of the protein-ligand complexes (Acetylcholinesterase-Compound 5, Androgen Receptor-Compound 5, and Estrogen Receptor-Compound 5) were analyzed using RMSF, Rg, and SASA. RMSF analysis revealed flexible regions in acetylcholinesterase (residues 120-140, 420-450), androgen receptor (residues 60-80, 200-220), and minor fluctuations in the estrogen receptor (residues 50-70, 250-270), indicating structural adaptability in specific areas. Rg analysis demonstrated consistent compactness for acetylcholinesterase (∼22.5 Å), moderate adaptability for androgen receptor (∼24 Å), and tight packing for Estrogen Receptor (∼22 Å). SASA results indicated stable solvent exposure for acetylcholinesterase (∼160 Ų), slightly higher exposure for the androgen receptor (∼175 Ų), and the lowest and most stable SASA (∼150 Ų) for the estrogen receptor. These findings highlighted the highest structural stability for the estrogen receptor, moderate flexibility for the androgen receptor, and a balance of stability and adaptability for acetylcholinesterase.

The β-glucosidase, peroxidase, and serine protease protein-ligand complexes were also evaluated using RMSF, Rg, and SASA analyses during MD simulations. Each protein-ligand complex displayed unique regions of mobility after assessing residue flexibility. Higher variations were observed in β-glucosidase, particularly around specific residues, indicating flexible areas crucial for ligand interaction. Peroxidase's active site exhibited moderate flexibility, while serine protease showed more localized variations, reflecting the dynamic behavior of certain residues. These results underscore the distinct dynamic characteristics of each protein in facilitating ligand binding.

Analysis of the Rg (Middle Row) revealed consistent protein structural uniformity throughout the simulation. β-glucosidase maintained stable Rg values, confirming stable conformations, while serine protease showed minimal changes, retaining a very compact structure. Peroxidase demonstrated small variations in Rg values, suggesting slight structural rearrangements. These findings validate the structural stability of all three protein-ligand complexes under dynamic conditions. Protein surface exposure was assessed by SASA. Stable SASA readings for β-glucosidase indicated minimal surface conformation changes. Peroxidase exhibited moderate SASA fluctuations, suggesting slight alterations in surface exposure during the simulation, while serine protease showed greater SASA variability, reflecting higher flexibility and potential structural changes, as shown in Figure 15.

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Figure 15.

The protein-ligand complexes' MD simulation analyses were depicted in this figure. Top row showed residue flexibility was underlined by RMSF plots, which identified extremely mobile sites and displayed fluctuations along the protein chain; Middle row: Rg plots depicted the homogeneity of the protein structures over the simulation time; Bottom row: solvent accessible surface area (SASA) analyses showed changes in protein conformation during the simulation.

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3.9.1. Dynamic residue-residue interactions and structural flexibility in protein-ligand complexes

The cross-correlation plots illustrate the dynamic residue-residue interactions within six distinct protein-ligand complexes including acetylcholinesterase (a), androgen receptor (b), estrogen receptor (c), β-glucosidase (d), peroxidase (e), and serine protease (f), as depicted in Figure 16. These plots highlight both correlated and anti-correlated movements observed during MD simulations. Coordinated movements of residues, particularly in active sites or binding pockets, are often seen in regions critical for structural stability and functional dynamics, as indicated by positive correlations (red regions). Conversely, regions with opposing movements, known as anti-correlated motions (blue regions), are typically associated with allosteric effects or increased flexibility. Receptors such as the androgen receptor and acetylcholinesterase show extensive positively correlated regions, suggesting stable and cooperative motion among residues, likely contributing to robust ligand binding. In contrast, receptors like the estrogen receptor and serine protease display more anti-correlated movements, indicating greater adaptability in specific domains. This flexibility may influence dynamic binding behavior or ligand adaptability. β-glucosidase and peroxidase exhibit a combination of correlation patterns, indicating a balance between flexible regions and stable structural elements. Overall, the cross-correlation diagrams provide valuable insights into the structural dynamics of the receptors, with variations in correlated motions linked to their functional roles and interaction mechanisms.

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Figure 16.

The cross-correlation plots illustrate residue-residue dynamics within six receptor systems during MD simulations. (a) Acetylcholinesterase and ligand complex; (b) androgen receptor and ligand complex; (c) estrogen receptor and ligand complex; (d) β-glucosidase and ligand complex; (e) peroxidase and ligand complex; and (f) serine protease and ligand complex. Correlated motions (red regions) indicate coordinated residue movement, reflecting structural stability and active site interactions, while anti-correlated motions (blue regions) represent flexibility and potential allosteric effects. These plots provide insights into receptor stability, flexibility, and functional dynamics essential for ligand binding.

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3.9.2. MD and PCA analysis of ligand-protein complexes

The PCA analysis of protein-ligand complexes with synthesized compounds (4-7) is illustrated in Figure 17. MD simulations analysis of the acetylcholinesterase complex with compound 5 and the androgen receptor complex with compound 7 showed significant ligand-induced stabilization, confirmed by both PCA and free energy map results. In the acetylcholinesterase complex, compound 5 demonstrated well-defined regions in the PCA projection, with PC1 capturing large-scale structural modifications and PC2 highlighting localized side-chain movements. The free energy landscape revealed a notable global minimum with steep energy barriers, indicating a stable conformation, which is consistent with its strong binding interactions through hydrogen bonding and π-π stacking with residues like GLN376X and PHE445A. Structural overlay confirmed minimal deviations, suggesting a stable and limited conformation of the active site.

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Figure 17.

(a) PCA projection, 3D free energy landscape, and structural superimposition of the acetylcholinesterase-compound 5 complex showed stable conformational clusters, a well-defined global minimum, and minor protein structure variations. (b) For the androgen receptor-compound 7 complex, PCA indicated dense clusters, the free energy landscape displayed an obvious global minimum, and the structural overlay revealed a rigid receptor shape. (c) The estrogen-compound 5 complex showed tightly packed PCA clusters, a global minimum in the free energy landscape, and a thermodynamically stable structure. (d) The β-glycosidase-compound 5 complex displayed wider PCA clusters, multiple shallow free energy minima indicating structural flexibility, and protein backbone variations reflecting lower ligand stability. (e) The peroxidase-compound 5 complex exhibited discrete PCA clusters, a well-defined global minimum, and minimal structural variations, indicating high stability. (f) The serine protease-compound 6 complex showed larger PCA clusters, a global minimum with shallow barriers indicating flexible states, and moderate backbone variations suggesting dynamic interactions.

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Similarly, in the androgen receptor complex with compound 7, the PCA projection showed tightly packed clusters, with PC1 and PC2 indicating strong structural stability and restricted movement. The free energy landscape exhibited a dominant global minimum with sharp energy barriers, confirming high stability. This was attributed to the thiophene ring's π-sulfur and hydrophobic interactions with key residues such as PHE445A and MET355A.

In the estrogen receptor complex, the PCA projection demonstrated conformational states that were densely clustered along PC1 and PC2, emphasizing limited conformational variability during the simulation. The most stable conformations were found in the red-dense regions of the PCA projection. PC1 reflected large-scale global conformational changes, while PC2 captured localized structural adjustments in the receptor's binding site. A highly thermodynamically stable conformation was suggested by the 3D free energy landscape, which showed a well-defined global minimum surrounded by steep energy barriers. It was shown that Compound 5 rigidified the receptor, stabilizing its active conformation. Structural superimposition revealed minimal deviations in the receptor's backbone, consistent with the robust docking score of 57.2130. This stability is attributed to hydrogen bonding and π-π stacking interactions facilitated by the methoxy group of Compound 5 with critical residues, including PHE445A and MET355A.

Docking scores and functional group interactions corroborated the MD results, showing that Compounds 5 and 6 interact differently with the peroxidase and serine protease complexes, respectively. Compound 5, with a docking score of 45.9492 in the peroxidase complex, stabilized the enzyme by forming strong hydrogen bonds and π-π stacking interactions with residues such as THR148 and MET123 through its methoxy group. A highly stable thermodynamic state was indicated by the free energy landscape, which showed a well-defined global minimum with sharp barriers. The PCA projection revealed two clearly defined clusters, indicating stable conformational states. Structural superimposition showed minimal changes to the protein backbone, indicating that Compound 5 stiffened the enzyme.

In contrast, serine protease complexes with compounds 5 and 6 displayed enhanced conformational flexibility, as evidenced by broader and more dispersed PCA clusters. Compound 6's dichlorobenzylidene group, with a docking score of 48.4475, was capable of forming hydrophobic and π-alkyl contacts with residues such as LYS278 and GLN280. The free energy landscape showed a global minimum with shallow energy barriers, suggesting several similar stable states. Structural superimposition indicated substantial backbone variations, reflecting the dynamic nature of the interaction. These results suggested that Compound 5, which stiffened peroxidase, provided high stabilization, whereas Compound 6, which allowed for structural flexibility in serine protease, provided moderate stabilization.

3.13.1. Enzymatic inhibitory profiles of synthesized compounds

The enzymatic screening results for compounds 4-7 revealed diverse inhibitory activities against six target enzymes: acetylcholinesterase, α-amylase, β-glucosidase, peroxidase, lipase, and tyrosinase. The inhibitory activity was evaluated using percentage inhibition and IC₅₀ values (µg/mL), as summarized in Table 8. Compound 5 demonstrated the strongest inhibitory activity against acetylcholinesterase, with an IC₅₀ value of 1.22 ± 1.63 µg/mL, followed by compound 6 (1.95 ± 1.79 µg/mL) and compound 4 (2.03 ± 1.42 µg/mL). Compound 7 exhibited the lowest activity, with an IC₅₀ value of 2.16 ± 1.98 µg/mL.

For α-amylase inhibition, compound 7 emerged as the most potent inhibitor, with the lowest IC₅₀ value (2.81 ± 8.69 µg/mL) and the highest percentage inhibition (47.22%). Compound 6 showed moderate activity, with an IC₅₀ value of 3.42 ± 6.36 µg/mL and percentage inhibition of 46.05%. Compound 5 displayed slightly lower activity than compounds 6 and 7, with an IC₅₀ value of 3.56 ± 8.04 µg/mL and percentage inhibition of 38.98%. Compound 4 was the least effective, exhibiting the highest IC₅₀ value (3.15 ± 9.23 µg/mL) and the lowest percentage inhibition (40.64%). Compound 5 showed the strongest inhibition of β-glucosidase, with an IC₅₀ value of 0.37 ± 3.06 µg/mL, followed by compound 4 (0.46 ± 2.55 µg/mL). Compounds 6 and 7 displayed comparatively lower activities, with IC₅₀ values of 0.98 ± 2.08 µg/mL and 1.16 ± 1.99 µg/mL, respectively.

Against the peroxidase enzyme, compound 5 was the most efficient inhibitor, with the lowest IC₅₀ value (2.009 ± 3.19 µg/mL) and a percentage inhibition of 56.86%. Compound 7 also demonstrated good activity, with an IC₅₀ value of 2.19 ± 13.40 µg/mL and 53.60% inhibition. Compound 4 exhibited moderate activity, with an IC₅₀ value of 2.78 ± 6.59 µg/mL and a percentage inhibition of 47.12%. In contrast, compound 6 showed the weakest inhibitory action, with the highest IC₅₀ value (5.53 ± 2.80 µg/mL) and the lowest inhibition (26.22%).

For lipase inhibition, compound 7 was the most effective, with an IC₅₀ value of 2.78 ± 11.23 µg/mL and the highest percentage inhibition (45.08%). Compound 5 showed significant activity, with an IC₅₀ value of 3.44 ± 2.03 µg/mL and a 43.02% percentage inhibition. Compound 4 displayed moderate activity, with an IC₅₀ value of 3.30 ± 9.48 µg/mL and 38.88% inhibition. Compound 6 had the weakest activity, with the highest IC₅₀ value (4.28 ± 5.8 µg/mL) and the lowest inhibition (33.97%).

Compound 7 exhibited strong tyrosinase inhibition, with an IC₅₀ value of 0.70 ± 2.30 µg/mL, while compound 6 had the least inhibitory effect, with an IC₅₀ value of 7.46 ± 0.84 µg/mL. In the serine protease assay, compound 6 showed the highest inhibitory activity, with an IC₅₀ value of 2.36 ± 4.52 µg/mL, followed by compound 7, with an IC₅₀ value of 3.51 ± 4.50 µg/mL.

3.13.2. Anticancer potential of synthesized compounds

The anticancer activity of the synthesized compounds (4–7) was compared to that of the standard drug doxorubicin and evaluated against HeLa and PC3 cell lines, as illustrated in Figure 20. Compound 5 demonstrated the highest activity against HeLa cells, with a percentage inhibition of 50.17%, followed by compound 6 (33.58%) and compound 4 (29.69%). In contrast, compound 7 exhibited the lowest inhibition against HeLa cells (16.17%). However, against PC3 cells, compound 7 showed the most potent activity, with a percentage inhibition of 37.11%, followed by compound 4 (29.9%) and compound 6 (22.3%). Compound 5 displayed comparatively lower inhibition (19.7%) against PC3 cells.

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Figure 20.

Anticancer activity (% inhibition) of synthetic compounds (4–7) and standard medicine doxorubicin against HeLa and PC3 cell lines.

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The standard drug doxorubicin exhibited the highest inhibition rates for both cell lines, achieving 93.66% against HeLa cells and 58.01% against PC3 cells. These findings highlight compound 5's strong activity against HeLa cells and compound 7's selectivity for PC3 cells, encouraging further investigation into their potential as anticancer agents.

3.13.3. Antioxidant activity of synthesized compounds

The antioxidant activity of the synthesized compounds was evaluated using the DPPH assay, with IC₅₀ values compared to the standard ascorbic acid. Ascorbic acid exhibited the lowest IC₅₀ value of 7.3 ± 1.4 μg/mL, reflecting its potent free radical scavenging capacity. Compounds 5 and 7 demonstrated the strongest antioxidant activity, with IC₅₀ values of 17.49 ± 2.64 μg/mL and 17.16 ± 2.64 μg/mL, respectively, indicating significant DPPH radical neutralization efficiency. Compound 6 showed moderate antioxidant activity (IC₅₀ = 38.10 ± 3.80 μg/mL), while compound 4 exhibited the lowest activity (IC₅₀ = 40.37 ± 4.49 μg/mL). These results suggested that the hydrogen donation or electron transfer mechanisms, particularly via the methoxy group in compound 5 and the thiophene ring in compound 7, may enhance radical scavenging activity, as shown in Table 9.

3.13.4. Antibacterial and antifungal activity of synthesized compounds

Compounds 4-7 were tested for their antibacterial efficacy against Aspergillus flavus, Bacillus subtilis, and Escherichia coli, as shown in Table 9. Compound 5, which is similar to the standard medication Cefixime, demonstrated the strongest antibacterial activity against Escherichia coli, with a zone of inhibition measuring 1.75 ± 0.05 mm. Compounds 7 and 4 exhibited moderate activity, with inhibition zones measuring 1.63 ± 0.17 mm and 1.63 ± 0.26 mm, respectively. In contrast, Compound 6 displayed the lowest activity, with a zone of inhibition measuring 1.28 ± 0.39 mm. Compound 4 exhibited an inhibition zone of 1.96 ± 0.06 mm against Bacillus subtilis, matching the activity of Cefixime. The activity of compound 5 was moderate, measuring 1.83 ± 0.14 mm, while compound 7 showed mild activity (0.9 ± 0.45 mm). Notably, compound 6 demonstrated no inhibitory activity against Bacillus subtilis. In its antifungal activity against Aspergillus flavus, compound 4 showed modest inhibition (1.36 ± 0.28 mm), similar to the conventional clotrimazole. Compound 6 exhibited weak antifungal activity (1 ± 0.1 mm), while compound 5 showed very weak inhibition (0.83 ± 0.17 mm). Compound 7 displayed no antifungal activity.

The structural and functional integrity of the synthesized compounds (4-7) was thoroughly characterized using UV-Vis, NMR, FTIR, EI-MS, TGA, and DSC. Compound 5 was the primary focus due to its unique characteristics. In the conjugated systems, λ_max were observed between 345 and 368 nm, corresponding to π-π* transitions, as determined by UV-Visible spectroscopy. Compound 5 exhibited a λ_max of 350 nm, attributed to the electron-donating methoxy substituent, which enhanced conjugation compared to compound 4, which contained the electron-withdrawing trifluoromethyl group (λ_max = 345 nm). Compound 7, exhibiting the highest λ_max (368 nm) due to its thiophene substituent, indicated extended conjugation. Conversely, compound 6 (λ_max = 359 nm) demonstrated a balance of electron-withdrawing effects from its dichlorobenzylidene group [67].

The ¹H and ¹³C NMR spectra further confirmed the structures of the compounds. Compounds 4 and 5 showed aromatic singlets (7.0–8.0 ppm) for the benzylidene protons, whereas compound 6 displayed peaks corresponding to the dichlorobenzylidene substituent. The electron-donating thiophene group in compound 7 resulted in the greatest deshielding, causing downfield-shifted signals. The ¹³C NMR spectra revealed carbonyl carbons at ∼160-170 ppm, aromatic carbons at 120-140 ppm, and dioxane carbons. The chemical shifts of each compound reflected the electronic effects of the substituents. In compound 4, the strong electron-withdrawing nature of the trifluoromethyl group caused a downfield shift for its aromatic carbon neighbors. compound 5 showed a notable shift for the carbon directly attached to the methoxy group due to the electron-donating resonance effect. Compound 7 displayed an additional downfield shift due to the electron-withdrawing nature of the thiophene substituent.

The presence of characteristic functional groups in the compounds was confirmed by FT-IR spectroscopy. These included N-H stretching vibrations at approximately 3300–3500 cm⁻¹ and C=O stretching at approximately 1650–1700 cm⁻¹. Additionally, the structural framework was further validated by the observation of C-O-C stretching vibrations from the dioxane ring in all compounds. The thiophene-containing compound 7 exhibited shifts indicative of enhanced conjugation. The EI-MS spectra confirmed the molecular weights of the compounds, with molecular ion peaks matching the expected masses. The fragmentation patterns showed that the structures were stable [68-70].

TGA and DSC were employed to assess the thermal stability and behavior of the synthesized compounds (4-7). TGA revealed substituent-dependent degradation patterns. Compound 4 exhibited the lowest degradation onset temperature (∼280°C), attributed to the electron-withdrawing trifluoromethyl group, which increases thermal degradation sensitivity by destabilizing the compound. Compound 5 degraded slightly faster (∼290°C), likely due to the stabilizing resonance effect of the methoxy group, which enhances conjugation and heat resistance. Compound 6 demonstrated the best thermal stability, with a degradation onset of ∼310°C, possibly due to the dichlorobenzylidene group's electron-withdrawing effects, which stabilized the aromatic core. Compound 7 showed good thermal stability (∼300°C), benefiting from its rigid structure and the conjugation of the thiophene ring, which aids in efficient thermal energy distribution.

DSC analysis provided further insights into the compounds' intermolecular interactions and melting points. The trifluoromethyl group in compound 4 appeared to be responsible for the lowest heat flow, suggesting weaker intermolecular forces. In contrast, the methoxy substituent in compound 5, with its electron-donating nature, facilitated balanced intermolecular interactions, as indicated by moderate heat flow. The dichlorobenzylidene group's dual chlorine substituents led to the strongest intermolecular interactions, as reflected by the maximum heat flow in compound 6. The extended conjugation of the thiophene moiety in compound 7 enhanced π-π stacking interactions, contributing to elevated heat transfer. These results underscored the significant influence of substituent effects on the thermal stability and intermolecular interactions of hydrazide derivatives [70-72].

The molecular docking study of the synthesized compounds (4-7) provided valuable insights into their SARs, aligning with their thermal and spectroscopic data. Compound 5 emerged as the most promising candidate, exhibiting strong binding to acetylcholinesterase (40.3568), peroxidase (45.9492), β-glucosidase (47.1030), and the estrogen receptor (57.2130). This strong binding was primarily attributed to its methoxy group, which enhanced hydrogen bonding and π-π stacking interactions with key residues, including GLN376X, VAL341X, PHE445A, and MET355A. The UV-Vis λ_max (350 nm), NMR chemical shifts indicating resonance stabilization, and moderate thermal stability observed in DSC supported these strong interactions. Compound 6, featuring a dichlorobenzylidene group, exhibited significant hydrophobic and π-alkyl interactions with residues such as LYS278A, GLN280A, and VAL341X, leading to enhanced binding. This compound achieved the highest docking scores for serine protease (48.4475) and β-glucosidase (47.0415). Its thermal analysis revealed the highest degradation onset temperature in TGA (∼310°C) and a high heat flow in DSC, consistent with its strong thermal stability. In Compound 7, the thiophene ring facilitated extended conjugation and sulfur-based interactions, contributing to its superior binding to the androgen receptor (54.9091). These findings were further corroborated by thermal analysis and UV-Visible spectroscopy, which revealed strong intermolecular interactions and the highest λ_max (368 nm). Compound 4 showed moderate binding to estrogen receptors and peroxidase, consistent with the electron-withdrawing trifluoromethyl group. This group contributed to a lower λmax (345 nm) and reduced thermal stability (∼280°C). These results suggested that the functional groups identified in the spectroscopic studies (e.g., methoxy, NH, C=O, halogens, and sulfur) and the thermal stability trends observed in DSC and TGA directly influenced the enzyme-binding affinity and interaction strength of the synthesized compounds [73].

The stability, dynamic behavior, and interaction patterns of the ligand-protein complexes were evaluated through MD simulations. RMSD analysis showed that all complexes maintained stable protein backbones throughout the 100 ns simulation. Compound 5 demonstrated strong stability with consistent RMSD values, while compound 6 exhibited moderate ligand flexibility, suggesting adaptability within the binding pocket. Compound 4, which contains an electron-withdrawing trifluoromethyl group, had higher RMSD values, indicating weaker interactions. The moderate variations observed in compound 7 were attributed to the adaptive binding enabled by the flexibility of the thiophene ring.

RMSF analysis revealed that compounds 5 and 7 exhibited significant stabilization, as evidenced by reduced fluctuations at the binding interfaces. In contrast, compound 4 showed weakened interactions, as reflected by increased fluctuations in non-binding regions. The thiophene group in compound 7 stabilized hydrophobic residues, thereby enhancing cooperative interactions. These findings are consistent with prior research that highlights the correlation between hydrogen bonding, hydrophobic interactions, and reduced protein flexibility.

Stable compactness in all complexes was confirmed by SASA analysis and the Rg. Compound 5 exhibited moderate solvent shielding, which aligned with its stability. Compound 7 displayed adaptability through moderate SASA values, supporting its strong binding to androgen receptors. Energetically favorable conformations were often associated with such compactness.

These analyses provided crucial insights into protein-ligand interactions. The spectroscopic and thermal data were consistent with the MD simulation results. The methoxy group of compound 5 contributed to moderate thermal stability, as observed in TGA and DSC, and enhanced hydrogen bonding (UV-Vis λ_max = 350 nm). The dichlorobenzylidene group of compound 6 promoted thermal robustness and hydrophobic interactions (λ_max = 359 nm). The trifluoromethyl group in compound 4 was associated with lower stability. Conversely, the thiophene ring in compound 7 enhanced receptor adaptability and flexibility (λ_max = 368 nm). These findings emphasized the critical role of substituents in optimizing ligand design for therapeutic applications. The stability, dynamic behavior, and interaction patterns of the ligand-protein complexes were evaluated through MD simulations. RMSD analysis showed that all complexes maintained stable protein backbones throughout the 100 ns simulation. Compound 5 demonstrated strong stability with consistent RMSD values, while compound 6 exhibited moderate ligand flexibility, suggesting adaptability within the binding pocket. Compound 4, which contains an electron-withdrawing trifluoromethyl group, had higher RMSD values, indicating weaker interactions. The moderate variations observed in compound 7 were attributed to the adaptive binding enabled by the flexibility of the thiophene ring.

RMSF analysis revealed that compounds 5 and 7 exhibited significant stabilization, as evidenced by reduced fluctuations at the binding interfaces. In contrast, compound 4 showed weakened interactions, as reflected by increased fluctuations in non-binding regions. The thiophene group in compound 7 stabilized hydrophobic residues, thereby enhancing cooperative interactions. These findings are consistent with prior research that highlights the correlation between hydrogen bonding, hydrophobic interactions, and reduced protein flexibility.

Stable compactness in all complexes was confirmed by SASA analysis and the Rg. Compound 5 exhibited moderate solvent shielding, which aligned with its stability. Compound 7 displayed adaptability through moderate SASA values, supporting its strong binding to androgen receptors. Energetically favorable conformations were often associated with such compactness.

These analyses provided crucial insights into protein-ligand interactions. The spectroscopic and thermal data were consistent with the MD simulation results. The methoxy group of compound 5 contributed to moderate thermal stability, as observed in TGA and DSC, and enhanced hydrogen bonding (UV-Vis λ_max = 350 nm). The dichlorobenzylidene group of compound 6 promoted thermal robustness and hydrophobic interactions (λ_max = 359 nm). The trifluoromethyl group in compound 4 was associated with lower stability. Conversely, the thiophene ring in compound 7 enhanced receptor adaptability and flexibility (λ_max = 368 nm). These findings emphasize the critical role of substituents in optimizing ligand design for therapeutic applications [45-74].

PCA and FEL analyses revealed the unique structural states and dynamic binding pocket activity of each compound. In its FEL, compound 5 displayed tightly clustered conformational states with high energy minima, indicating strong ligand-protein stability and limited flexibility. This aligns with its high binding affinities and excellent pharmacokinetics. The results confirmed that the methoxy group enhances hydrogen bonding and π-π stacking interactions. Compound 7 demonstrated flexibility and adaptive binding with larger PCA clusters and numerous low-energy minima. The sulfur-containing thiophene ring influenced hydrophobic and π-π interactions, particularly with androgen receptors, supporting spectroscopic evidence of extended conjugation. Compound 4, with its electron-withdrawing trifluoromethyl group, exhibited increased structural flexibility and decreased stability, as indicated by the broader FEL minima. Compound 6, containing the dichlorobenzylidene group, formed clusters in PCA and displayed a clear energy minimum in the FEL, reflecting hydrophobic interactions.

For compounds 5 and 7, cross-correlation graphs revealed cooperative movements between residues in the binding pocket, indicating both stability and flexibility. Compound 5, which is thermodynamically stable, maintained structural compactness with minimal solvent exposure, as reflected in its Rg and SASA values. Compound 7, with slightly higher SASA values, was compact yet flexible, facilitating dynamic interactions. These findings, supported by UV-visible, NMR, and thermal data, demonstrate that substituents such as methoxy, sulfur, and trifluoromethyl influence ligand dynamics, binding efficiency, and protein adaptability. MD simulations of ligand-induced stability and flexibility align with these results and contribute valuable insights for drug design [75-77].

The electronic characteristics of the synthetic compounds (4-7) were evaluated using MEP mapping and FMO studies. Both the gas and methanolic phases were considered to assess parameters such as ΔEGap, dipole moments, ionization potential, electron affinity, and electrophilicity. Compound 5, featuring a methoxy group, exhibited the highest dipole moment (7.7898 Debye) and a moderate energy gap (0.16686 eV), reflecting optimal stability and reactivity. The negative MEP regions of compound 5 supported hydrogen bonding, which aligns with its strong docking and MD simulation results, UV absorption (λ_max = 350 nm), and thermal stability.

Compound 7, containing a thiophene ring, showed a moderate dipole moment (5.9530 Debye) and a low energy gap (0.16914 eV), indicating adaptability and reactivity. The high electron density around its sulfur atom facilitated π-π stacking and hydrophobic interactions, consistent with its strong docking scores for estrogen and androgen receptors. Compound 6, featuring a dichlorobenzylidene group, demonstrated a balanced stability-reactivity profile (dipole moment = 7.3026 Debye, ΔEGap = 0.17333 eV). MEP analysis revealed hydrophobic interactions near the chlorine atoms, supporting its docking and thermal findings.

Compound 4, characterized by a trifluoromethyl group, exhibited the lowest dipole moment (4.4505 Debye) and the highest energy gap (0.17428 eV), correlating with weaker interactions, lower UV absorption (λ_max = 345 nm), and moderate thermal stability. Overall, the electronic properties highlighted the significance of substituents in influencing reactivity, binding efficiency, and stability. The methoxy group in compound 5 enhanced hydrogen bonding, while the sulfur in compound 7 enabled dynamic hydrophobic interactions, underscoring their potential as bioactive agents. Hence, DFT studies corroborated the results from molecular docking, thermal, and spectral analyses [78-80].

The synthesized compounds (4-7) were evaluated for physicochemical and pharmacokinetic properties, focusing on Lipinski's Rule of Five, which assesses drug-likeness based on molecular weight, log P, hydrogen bond donors, and acceptors. All compounds met these criteria, suggesting oral bioavailability. The most promising drug-like compound was compound 5, which contains a methoxy group. It has a molecular weight of 312.30 g/mol, a log P of 2.5, and favorable hydrogen bonding parameters (5 acceptors and 1 donor). Compound 5 also exhibited outstanding intestinal absorption (83.5%), excellent solubility, and attractive UV-Vis spectral characteristics (λ_max = 350 nm). Strong receptor binding, demonstrated in docking and MD simulations, correlated with its high dipole moment and moderate HOMO-LUMO energy gap, promoting π-π stacking interactions and hydrogen bonding with active site residues such as serine, threonine, and arginine.

Compound 7, containing a sulfur-based thiophene ring, also exhibited strong drug-like properties, with a molecular weight of 288.20 g/mol, a log P of 2.431, and favorable hydrogen bonding parameters (4 acceptors and 2 donors). Its significant receptor binding, particularly with androgen receptors, was attributed to its dynamic hydrophobic and π-π stacking interactions, as well as its moderate dipole moment and reactivity in electronic analysis. Compounds 6 and 4 met Lipinski's requirements, but their stability and bioactivity differed. Compound 6, with a dichlorobenzylidene group, exhibited strong hydrophobic interactions and a moderate dipole moment (7.3026 Debye), resulting in considerable receptor binding. However, its slightly elevated toxicity indices (e.g., skin irritation) suggest that optimization is needed. Compound 4, containing a trifluoromethyl group, exhibited the lowest dipole moment (4.4505 Debye) and the greatest HOMO-LUMO energy gap, indicating weaker interactions and lower reactivity. This was reflected in its reduced absorption rate, lower thermal stability, and moderate UV absorption (λ_max = 345 nm).

Overall, the electronic, docking, and MD simulation data demonstrated that substituent effects significantly influence bioactivity, with compounds 5 and 7 emerging as the most promising due to their stability, flexibility, and receptor interactions. These results underscore the synergistic optimization of physicochemical characteristics and pharmacokinetics in enhancing the bioactivity of these compounds [81,82].

The synthesized compounds (4-7) exhibited a range of biological activities, including enzyme inhibition, anticancer potential, antioxidant properties, and antimicrobial effects, which align with previously identified spectral and computational findings. Compound 5 emerged as the most promising candidate, demonstrating superior inhibition of β-glucosidase (IC₅₀ = 0.37 ± 3.06 μg/mL), acetylcholinesterase (IC₅₀ = 1.228 ± 1.63 μg/mL), and peroxidase (IC₅₀ = 2.009 ± 3.19 μg/mL). These results were consistent with the robust docking scores and stable binding interactions observed in MD simulations. The methoxy group in compound 5 facilitated hydrogen bonding and π-π stacking interactions with critical residues, as shown in in silico studies [83]. The compound's activity was further supported by spectral data, which indicated increased electron delocalization, as evidenced by its UV-visible absorption.

Compound 7, with the highest inhibition of tyrosinase (IC₅₀ = 0.70 ± 2.30 μg/mL) and significant activity against α-amylase and lipase, emerged as the most potent inhibitor among the compounds. This was attributed to hydrophobic and π-electron interactions, which were validated through MEP mapping and docking experiments. The sulfur-containing thiophene group in compound 7 played a key role in these interactions [84]. Additionally, compound 7 demonstrated strong antioxidant potential (IC₅₀ = 17.16 ± 2.641 μg/mL), emphasizing its ability to scavenge free radicals and complement its overall biological activity profile.

Compound 5 exhibited the most consistent antibacterial activity, particularly against Escherichia coli and Bacillus subtilis, as demonstrated by antimicrobial assessments. Its zone of inhibition was comparable to that of Cefixime. In contrast, compound 6 displayed moderate antifungal activity against Aspergillus flavus but showed limited antibacterial effects. These results align with the electron-withdrawing properties of the dichlorobenzylidene group, as indicated by the FMO analysis, which affected its reactivity and potential for interaction. Compound 4 demonstrated moderate activity across all assays, with notable antioxidant properties (IC₅₀ = 40.37 ± 4.486 μg/mL) and enzyme inhibition. However, the trifluoromethyl group in compound 4 resulted in weaker binding interactions, as supported by spectral data and docking studies.

The combined test results emphasized the critical role of functional groups in modulating biological activity. The most promising candidates were compound 5 and compound 7, which exhibited robust docking interactions, significant anticancer potential (as observed in HeLa and PC3 cell lines), and potent enzyme inhibition. These findings underscore the therapeutic potential of these compounds, which was influenced by the structural insights gained from both spectral and computational analyses [85,86].

The SAR analysis of benzodioxane carboxylic acid-based hydrazones, as depicted in Figure 21, revealed the significant impact of substituents on biological activity. Compound 4, featuring a trifluoromethyl group, exhibited moderate antioxidant (IC₅₀ = 40.37 ± 4.486 µg/mL), enzyme inhibitory, and antibacterial activities, which were limited by steric hindrance and reduced interaction potential. Compound 5, containing a methoxy (-OCH₃) group, demonstrated potent antioxidant activity (IC₅₀ = 17.49 ± 2.641 µg/mL), effective enzyme inhibition, particularly against β-glucosidase (IC₅₀ = 0.37 ± 3.06 µg/mL) and acetylcholinesterase (IC₅₀ = 1.228 ± 1.63 µg/mL), as well as significant anticancer activity (50.17% inhibition against HeLa cells), driven by enhanced hydrogen bonding and π-π stacking interactions. Compound 6, with dichlorobenzylidene (-Cl) substituents, exhibited moderate activity, hindered by steric effects that limited binding adaptability. Compound 7, featuring a sulfur-based thiophene ring, demonstrated potent α-amylase inhibition (IC₅₀ = 2.81 ± 8.69 µg/mL), antioxidant activity (IC₅₀ = 17.16 ± 2.641 µg/mL), and anticancer potential (37.11% inhibition against PC3 cells), but showed limited antimicrobial efficacy, likely due to its hydrophobic nature. In summary, methoxy and sulfur-containing substituents in compounds 5 and 7 enhanced their bioactivity, making them promising candidates for therapeutic applications [87,88].

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Figure 21.

Benzodioxane carboxylic acid-based hydrazones' structural activity relationship (SAR) analysis represented the way various substituted molecules influenced their biological roles.

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The findings from the structural, computational, and biological studies of compounds 4-7 offer significant insights into their potential for therapeutic applications. Compound 5, with its methoxy group, demonstrated superior bioactivity, including potent enzyme inhibition, antioxidant properties, and anticancer potential, making it a promising candidate for further development as a multi-target therapeutic agent. Its favorable pharmacokinetic profile, enhanced receptor binding, and stability through hydrogen bonding and π-π stacking interactions suggest it could be an effective treatment for conditions like cancer, neurodegenerative diseases, and metabolic disorders. Compound 7, with its sulfur-containing thiophene ring, exhibited strong α-amylase inhibition and antioxidant properties, suggesting its potential as an anti-diabetic and anti-inflammatory agent. Its ability to interact dynamically with biological targets and its anticancer activity also support its inclusion in therapeutic strategies. The results emphasize the importance of substituent effects, such as methoxy and sulfur groups, in optimizing the biological efficacy and stability of compounds, offering a pathway for designing targeted therapies for various diseases. Further clinical development and optimization of these compounds could lead to effective, multi-target treatments with broad applications in oncology, enzymatic disorders, and metabolic diseases.