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Article

Hybrid 2-Quinolone–1,2,3-triazole Compounds: Rational Design, In Silico Optimization, Synthesis, Characterization, and Antibacterial Evaluation

by
Ayoub El-Mrabet
1,*,
Abderrahim Diane
1,
Rachid Haloui
2,
Hanae El Monfalouti
3,
Ashwag S. Alanazi
4,
Mohamed Hefnawy
5,
Mohammed M. Alanazi
5,
Youssef Kandri-Rodi
1,
Souad Elkhattabi
2,
Ahmed Mazzah
6,
Amal Haoudi
1 and
Nada Kheira Sebbar
7,*
1
Laboratory of Applied Organic Chemistry, Faculty of Sciences and Techniques, Sidi Mohamed Ben Abdellah University, Route d’Imouzzer, P.O. Box 2202, Fez 30000, Morocco
2
Laboratory of Engineering, Systems and Applications, National School of Applied Sciences, Sidi Mohamed Ben Abdellah-Fez University, P.O. Box 2202, Fez 30040, Morocco
3
Laboratory of Plant Chemistry, Faculty of Sciences, Mohammed V University in Rabat, 4 Avenue 9 Ibn Battouta, P.O. Box 1014, Rabat 10000, Morocco
4
Department of Pharmaceutical Sciences, College of Pharmacy, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
5
Department of Pharmaceutical Chemistry, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia
6
University of Lille, CNRS, UAR 3290, MSAP, Miniaturization for Synthesis, Analysis and Proteomics, 59000 Lille, France
7
Laboratory of Heterocyclic Organic Chemistry, Pharmacochemistry Competence Center, Faculty of Sciences, Mohammed V University in Rabat, 4 Avenue 9 Ibn Battouta, P.O. Box 1014, Rabat 10000, Morocco
*
Authors to whom correspondence should be addressed.
Antibiotics 2025, 14(9), 877; https://doi.org/10.3390/antibiotics14090877
Submission received: 9 June 2025 / Revised: 23 August 2025 / Accepted: 26 August 2025 / Published: 30 August 2025
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Abstract

Background/Objectives: The rise in antibiotic resistance presents a serious and urgent global health challenge, emphasizing the need to develop new therapeutic compounds. This study focuses on the design and evaluation of a novel series of hybrid molecules that combine the 2-quinolone and 1,2,3-triazole pharmacophores, both recognized for their broad-spectrum antimicrobial properties. Methods: A library of 29 candidate molecules was first designed using in silico techniques, including QSAR modeling, ADMET prediction, molecular docking, and molecular dynamics simulations, to optimize antibacterial activity and drug-like properties. The most promising compounds were then synthesized and characterized by 1H and 13C NMR APT, mass spectrometry (MS), Fourier-transform infrared (FT-IR) spectroscopy, and UV-Vis spectroscopy. Results: Antibacterial evaluation revealed potent activity against both Gram-positive and Gram-negative bacterial strains, with minimum inhibitory concentration (MIC) values ranging from 0.019 to 1.25 mg/mL. Conclusions: These findings demonstrate the strong potential of 2-quinolone–triazole hybrids as effective antibacterial agents and provide a solid foundation for the development of next-generation antibiotics to combat the growing threat of bacterial resistance.

1. Introduction

The emergence of antibiotic resistance is a global problem and a serious threat [1,2]. Antimicrobial resistance is currently responsible for approximately 700,000 deaths per year, and based on current trends and speculation, this number is expected to rise to approximately 10 million in a few years [3]. These statistics underscore the urgent need to develop novel antibacterial agents that can combat both drug-susceptible and drug-resistant infections [4]. A primary focus of organic, medicinal, and heterocyclic chemistry is the synthesis and design of compounds to address therapeutic points in human health. Heterocyclic chemistry is a prominent branch of chemical sciences that has emerged as a significant area of research, driven by the search for solutions to address adverse conditions associated with human survival problems [5,6]. In this context, quinolone derivatives have reportedly received significant attention due to their remarkable medicinal properties [7]. These scaffolds constitute the structural components of many, if not all, natural products and are crucial in drug development due to their therapeutic efficacy [8,9]. Quinolones and their derivatives share specific characteristics, including a bicyclic aromatic nucleus with a nitrogen atom in the 1-position and a ketone functional group [10]. Quinolones and their derivatives are considered “privileged building blocks” for libraries of bioactive molecules, owing to their simplicity and adaptability in synthetic procedures. Due to their wide range, high availability, and safety profiles, they are highly favored platforms for developing chemical libraries with overwhelming bioactivity [11,12]. Moreover, quinolones are among the most widely prescribed drugs worldwide and are widely accepted due to their broad spectrum of action, high bioavailability, and high safety profile.
Modifications of the parent structure of nalidixic acid, for example, have resulted in a series of compounds with broad therapeutic utility. Extensive biological activity studies have been conducted on important quinolones, including norfloxacin, ciprofloxacin, ofloxacin, and their related derivatives [13,14]. Structure–Activity Relationship (SAR) studies have played a crucial role in providing the chemistry necessary to develop highly potent compounds for testing against various diseases. Quinolone derivatives are known to exhibit a wide range of biological activities, including antibacterial [11,14,15], antifungal [16], analgesic [17], anti-inflammatory [18], larvicidal [12], anticancer [19], antitubercular [20], insecticidal [21], antimalarial [22], anti-HIV [23], anti-hepatitis C virus [24], and anti-Alzheimer activities [25]. Quinolones, a class of antibiotics, are first-line treatments for acute urinary tract infections and bacterial diarrhea. Several analogs can be made with substituted patterns of the quinolone core skeleton, thereby exploring further lead development with better efficacy and pharmacokinetic properties [26]. This study aims to construct QSAR (Quantitative Structure-Activity Relationship) models using an in silico approach to describe and predict the antibacterial inhibitory activity of 2-oxo-1,2-dihydroquinoline derivatives, leveraging diverse molecular descriptors. Additionally, the work involves designing and synthesizing novel compounds based on the 2-quinolone core structure, followed by their characterization through spectroscopic methods (1H NMR, 13C NMR APT, MS, FT-IR, and UV-Vis) and assessment of their antibacterial efficacy. In silico evolution of ADMET properties, molecular docking, and molecular dynamics are employed to investigate the binding interactions of these quinolone derivatives with the active site of the LasR-OC12 HSL receptor, as obtained from the Protein Data Bank (PDB ID: 3IX3), providing insights into their mode of action. These computational analyses guide the rational design of structurally optimized molecules with enhanced biological potency, streamlining the synthesis process by prioritizing promising candidates before experimental validation.

2. Materials and Methods

2.1. Chemical Reactions

In our search for new bioactive compounds based on the quinoline scaffold derived from the isatin molecule [27], we identified four practical synthetic approaches used to produce quinolinic acid, each showing notable features. The Pfitzinger reaction [28], carried out under microwave irradiation with acetic acid and sodium acetate, enables rapid and efficient (90%) formation of the quinolinic acid backbone, demonstrating the benefits of a mild and time-saving method. The microwave-assisted reaction reported by El Ashry et al. [29], which involves treating isatin with hydrazine hydrate in water, yields quinoline-2,3-dione derivatives in excellent amounts. This eco-friendly method underscores the efficiency of microwave irradiation in accelerating heterocyclization under mild aqueous conditions, offering a valuable alternative to traditional heating methods. Finally, the method used by Tsung-Chih Chen [30], involving an α-halo acid in acetic acid at high temperature (200 °C), allows for the addition of chlorine atoms, which are essential in medicinal chemistry, with an 83% yield. Overall, these methods showcase the versatility and synthetic richness of the isatin core in accessing diverse and promising compounds for new drug development [31]. In our lab, we chose the Pfitzinger reaction due to its high efficiency and selectivity in forming new compounds. These compounds were then tested against various bacterial strains (Scheme 1).

2.2. Experimental Databases

A set of 29 compounds based on 2-oxo-1,2-dihydroquinoline carboxylic acid derivatives (Scheme 2), synthesized by Moussaoui et al. [15,32] and Fillali et al. [33,34,35], and tested for their antibacterial activity, served as the basis for the development of a QSAR model. The development method begins with drawing the structure of each compound in ACD/ChemSketch Freeware, version 2022.2 (Advanced Chemistry Development, Toronto, ON, Canada) and saving it in mol format. Then, using the RDKit package (version 2024.9.5), these 29 mol files were loaded into Python (version 3.11) for structural optimization and molecular descriptor computation.
The generated descriptors were structured as a 2D array, with each row representing a chemical and each column containing the value of a specific descriptor for that compound. This array was then aligned with a 1D vector holding the IC50 value of each drug [36].
After the data was prepared, it was manually divided into training and testing subsets, with the data from 20 compounds used to develop the QSAR model and that from the remaining 9 compounds used for model validation. This ensured that the training set included the entire range of IC50 values. A wrapper descriptor selection technique was used to reduce the dimensionality of the data. This technique iteratively evaluated the root mean squared error (RMSE) from 5-fold cross-validation of a multiple linear regression model (provided by scikit-learn v1.6.1) with various descriptor combinations. The tree-structured Parzen estimator provided by the hyperopt (v0.2.7) package was used to optimize the iterative selection process, to minimize RMSE.
Once the selection process is complete, the subset of descriptors used to develop the MLR model with the lowest RMSE is used to create a symbolic regression (SR) model. The goal of this step was to create a model that outperforms the MLR model. The root mean squared error and R-squared were used to evaluate the performance of the developed symbolic model [37]. This thorough evaluation provided more profound insights into the relationship between molecular descriptors and biological activity, contributing to the development of more accurate and reliable QSAR models for future applications [38,39].

2.3. Spectral Data Measurements

The NMR spectra of the synthesized derivatives were recorded on a Bruker Avance DPX300 spectrometer (Billerica, MA, USA). Deuterated chloroform (CDCl3) or DMSO-d6 was used as solvent, depending on the solubility of the compound. Tetramethylsilane (TMS) was used as the internal reference, and all chemical shifts (δ) are reported in parts per million (ppm) relative to TMS. 1H and 13C NMR spectra were recorded under standard conditions and Attached Proton Test (APT) experiments were performed to assign carbon multiplicities (C, CH, CH2, CH3). The interpretation was based on chemical shifts, signal patterns, integrals, and APT phase behavior. The singlet observed at δ = 7.26 ppm in the 1H NMR spectrum of compound 4a corresponds to the residual proton signal of the CDCl3 solvent and was not assigned to the compound.
All other solvents and reagents used were analytical grade. On silica gel plates (60 F254, Merck, Darmstadt, Germany), TLC was performed; compounds were purified using silica gel (230–400 mesh, Merck) [40]. Mass spectrometry was used, which included a 337 nm UV laser (Spectra-Physics, Santa Clara, CA, USA) and a 7 Tesla superconducting magnet (Bruker BioSpin, Billerica, MA, USA). DHB served as the matrix, formulated at 10 mg/mL in a 1:1 (v/v) combination of acetonitrile and water, including 0.1% trifluoroacetic acid. Samples were solubilized in methanol, and a 1 µL aliquot of the sample/matrix combination (1:1, v/v) was applied. Measurements were conducted in positive ion mode with internal calibration using standard references. The data were examined with the instrument’s included software (Data acquisition and analysis were carried out with flexAnalysis, version 3.4 (Bruker Daltonics, Bremen, Germany)) [40]. FT-IR spectra were obtained with a Vertex 70 spectrometer (Bruker, Shanghai, China) at a resolution of 4 cm−1 over 32 scans, spanning the 400–4000 cm−1 range [40]. UV-Vis measurements were conducted using a Bio-based BK-D580 double-beam spectrophotometer. Melting points were determined using a computerized melting point apparatus.

2.4. Synthesis of New Antibacterial Agents Based on the Quinolone Derivatives

2.4.1. Synthesis of 2-Oxo-1,2-dihydroquinoline-4-carboxylic Acid (1)

A mixture of isatin (13 mmol), malonic acid (16 mmol), and sodium acetate (2 mmol) was dissolved in acetic acid as a reaction medium. The mixture was refluxed for 24 h. After cooling to room temperature, water was added to the reaction mixture, resulting in the formation of a precipitate 1. The precipitate was collected by filtration [6].

2.4.2. Esterification and N-Alkylation Reaction (Synthesis of Compounds 2 and 3)

To a solution containing Compound 1 (10 mmol), 15 mL of methanol and 1 mL of sulfuric acid were added. The reaction mixtures were kept under stirring at reflux for 2 h. Pure compound 2 was obtained by precipitation in DMSO/H2O [41].
Methyl 2-oxo-1,2-dihydroquinoline-4-carboxylate (4 mmol) 3 was dissolved in 12 mL of dimethylformamide (DMF), followed by the addition of propargyl bromide (10 mmol), potassium carbonate (K2CO3, 20 mmol), and tetra-n-butylammonium bromide (TBAB, 0.5 mmol). The reaction mixture was stirred at room temperature for 6 h. Afterward, the resulting salts were filtered out, and the solvent was removed under reduced pressure. The remaining residue was then dissolved in dichloromethane [41].

2.4.3. Synthesis of New Hybrid Molecules (4a–4c) Using 1,3-Dipolar Cycloaddition (Click Chemistry)

A solution of compound 3 (1 mmol) and alkyl azide (2 mmol) was prepared in a 1:2 (v/v) water/ethanol mixture at room temperature. Copper (II) sulfate pentahydrate (1 mmol) and sodium ascorbate (1 mmol) were then added. The reaction was stirred at room temperature before the solvent was evaporated under reduced pressure. The residue was extracted three times with a mixture of water and chloroform. The organic phase was recovered, dried over sodium sulfate, filtered and concentrated in vacuo. The crude product was purified by silica gel column chromatography using a mixture of ethyl acetate and hexane (3:1) as eluent. The three newly synthesized compounds were characterized by standard spectroscopic methods, including 1H NMR, 13C NMR, MS, FT-IR and UV-Vis spectroscopy.
  • Methyl 1-((1-(4-Bromobenzyl)-1H-1,2,3-triazol-4-yl)methyl)-2-oxo-1,2-dihydroquinoline-4-carboxylate (4a). Yield (%) = 78%; mp: 439–441 K; NMR 1H (300 MHz, CDCl3): δ (ppm) 8.2 (dd, J = 8.3, 1.5 Hz, 1H, CHAr), 7.9 (d, J = 8.6 Hz, 1H, CHAr), 7.6–7.5 (m, 2H, CHAr), 7.4 (d, J = 8.3 Hz, 2H, CHAr), 7.2 (d, J = 2.8 Hz, 1H, CHAr), 7.1 (s, 1H, CHEthylenic), 7.1 (d, J = 8.3 Hz, 2H, CHAr), 5.6 (s, 2H, CH2), 5.3 (s, 2H, CH2), 3.9 (s, 3H, CH3). 13C NMR APT (75 MHz, CDCl3): δ (ppm)165.85 (C=Oester), 161.3 (C=OAmide), 141.1 (Cq), 139.6 (Cq), 139.6 (Cq), 133.3 (CHAr), 132.4 (CHAr), 131.7 (CHAr), 129.9 (CHAr), 127.2 (CHEthylenic), 123.8 (CHAr), 123.1 (CHAr), 117.6 (Cq), 115.6 (CHAr), 53.7 (CH2), 53.0 (CH2), 38.7 (CH3). MS: 452.50677 [M + 1]+; FT-IR (υ in cm−1): 2961 (Aliphatic C–H), 1731 (C=O Ester), 1644 (C=O Ketone in quinolone), 1433 (Aromatic C=C stretching), 3125 (Aromatic C-H stretching), 1238 (C-N triazole bond); UV–vis: λmax = 375 nm.
  • Methyl 1-((1-(4-Methylbenzyl)-1H-1,2,3-triazol-4-yl)methyl)-2-oxo-1,2-dihydroquinoline-4-carboxylate (4b). Yield (%) = 82%; mp: 488–490 K; NMR 1H (300 MHz, CDCl3): δ (ppm) 8.1–8.0 (m, 2H, CHAr), 7.7 (d, J = 8.6 Hz, 1H, CHAr), 7.6–7.6 (m, 1H, CHAr), 7.3–7.2 (m, 1H, CHAr), 7.1 (d, J = 10.4 Hz, 4H, CHAr), 7.0 (s, 1H, CHEthylenic), 5.5 (s, 2H, CH2), 5.4 (s, 2H, CH2), 3.9 (s, 3H, CH3), 2.2 (s, 3H, CH3). 13C NMR APT (75 MHz, CDCl3): δ (ppm) 165.4 (C=Oester), 159.9 (C=OAmide), 142.6 (Cq), 139.2 (Cq), 137.5 (Cq), 132.9 (CHAr), 131.4 (CHAr), 129.2 (CHAr), 128.0 (CHAr), 126.6 (CHEthylenic), 123.5 (CHAr), 122.7 (CHAr), 122.6 (CHAr), 116.6 (Cq), 115.7 (CHAr), 53.0 (CH2), 52.6 (CH2), 37.78 (CH3), 20.6 (CH3). MS: 388.03659 [M + 1]+; FT-IR (υ in cm−1): 2964 (Aliphatic C–H), 1720 (C=O Ester), 1660 (C=O Ketone in quinolone), 1447 (Aromatic C=C stretching), 3136 (Aromatic C-H stretching), 1244 (C-N triazole bond); UV–vis: λmax = 362 nm.
  • Methyl 2-Oxo-1-((1-(4-(trifluoromethyl)benzyl)-1H-1,2,3-triazol-4-yl)methyl)-1,2-dihydroquinoline-4-carboxylate (4c). Yield (%) = 85%; mp: 503–505 K; NMR 1H (300 MHz, CDCl3): δ (ppm) 8.1 (s, 1H, CHAr), 8.9 (dd, J = 8.1, 1.5 Hz, 1H, CHAr), 7.8–7.6 (m, 5H, CHAr), 7.4 (d, J = 8.0 Hz, 2H, CHAr), 7.3–7.3 (m, 1H, CHAr), 7.0 (s, 1H, CHEthylenic), 5.6 (s, 2H, CH2), 5.5 (s, 2H, CH2), 3.9 (s, 3H, CH3). 13C NMR APT (75 MHz, CDCl3): δ (ppm) 165.3 (C=Oester), 159.9 (C=OAmide), 142.7 (Cq), 140.5 (Cq), 139.7 (Cq), 139.2 (Cq), 131.4 (CHAr), 128.6 (CHAr), 127.8 (CHEthylenic), 126.5 (CHAr), 125.6 (CHAr), 124.0 (CHAr), 122.6 (CHAr), 122.5 (CHAr), 116.5 (CHAr), 53.0 (CH3), 52.1 (CH3), 37.7 (CH3). MS: 441.01463 [M + 1]+; FT-IR (υ in cm−1): 2959 (Aliphatic C–H), 1730 (C=O Ester), 1642 (C=O Ketone in quinolone), 1436 (Aromatic C=C stretching), 3124 (Aromatic C-H stretching), 1323 (C-F stretching), 1242 (C-N triazole bond); UV–vis: λmax = 327 nm.

2.5. Antibacterial Activity

2.5.1. Disc Diffusion Method

In this method, Sterile Whatman No. 1 disks (6 mm diameter) were carefully placed on the agar surface after the medium had rested for 15 min. Each disc was then loaded with 10 µL of the novel synthesized compounds 4a–4c (10 mg/mL in DMSO). The Petri dishes were then incubated at 37 °C for 24 h. The detection of a translucent halo around the disc, indicating inhibited bacterial growth, was used to measure antimicrobial activity after the incubation period. A caliper was used to measure the diameter of the halo in millimeters [42].

2.5.2. Minimum Inhibitory Concentration

The minimum inhibitory concentrations (MIC) of the newly synthesized compounds were determined using a modified method based on the approach by Bouhdid et al. [43] Serial dilutions of the compounds were prepared in 100% pure DMSO, ensuring that the final concentration of DMSO in each well did not exceed 1% (v/v) to prevent any potential interference with bacterial growth. Each well of a sterile 96-well microplate contained 140 µL of Brain Heart Infusion (BHI) medium, 20 µL of a standardized bacterial inoculum, and an appropriate volume of the compound solution prepared in DMSO. This volume was adjusted to maintain the final DMSO concentration at ≤1% (v/v) in the total volume of each well. After incubating the plates at 37 °C for 24 h, bacterial growth was assessed visually based on turbidity. The MIC was defined as the lowest concentration of the compound at which no visible turbidity was observed, indicating complete inhibition of bacterial growth.

2.6. Drug-likeness and ADMET Prediction

Predicting ADMET properties is a critical part of computational drug discovery. This process provides the opportunity to obtain necessary information about the biological efficacy and safety of potential drug candidates, thereby reducing both the time and cost of experimental research [44,45]. By evaluating these parameters, researchers can clarify the likelihood of new drugs with greater confidence.
As part of this work, the SwissADME server and the pkCSM web resource were utilized to evaluate the physicochemical and pharmacokinetic properties of the investigated compounds, thereby predicting their efficacy and potential as viable drug candidates.

2.7. Molecular Docking Studies

The molecules were designed using ChemSketch, followed by geometry optimization using the MM2 force field [46]. These compounds were then tested for antibacterial activity. The LasR-OC12 HSL receptor, obtained from PDB ID: 3IX3 (https://doi.org/10.2210/pdb3IX3/pdb), was used in the antibacterial study. A lattice with dimensions of X = 25.5149 Å, Y = 2.8599 Å and Z = 12.7483 Å was defined for the docking analyses. Molecular docking was conducted using AutoDock tools with a grid size of 63 × 47 × 60 along the x, y, and z axes [47]. Before docking, the molecules and their receptor complexes were carefully prepared, including the removal of water molecules.
The docking protocol simulated potential ligand–protein interactions, and the analysis was refined using Discovery Studio, version 2021 (BIOVIA, Dassault Systèmes, San Diego, CA, USA) [41]. This refinement included removing residual water molecules, addressing incomplete side chain residues, and improving structural accuracy by adding non-polar hydrogens. These improvements increased the accuracy and reliability of the results.

2.8. Molecular Dynamics Simulations

In this study, molecular dynamics simulations (MDS) were conducted using the Desmond software package, version 2021.3 (Schrödinger, LLC, New York, NY, USA), which is part of the academic version of the Schrödinger Suite. The initial step involved energy minimization of the ligand-receptor complexes using the OPLS3e force field [48]. The systems were then solvated in an orthorhombic water box, ensuring a minimum distance of 10 Å between the solute and the box boundaries. Water molecules were represented using the SPC solvent model. Sodium (Na+) and chloride (Cl−) ions were added to balance the overall charge of the system and to adjust the salt concentration to 1.15 M, simulating physiological conditions. A brief additional minimization followed this [49]. Subsequent simulations were carried out in an NPT ensemble using the Nosé–Hoover thermostat. The temperature was gradually raised to 300 K while maintaining a pressure of 1.013 bar. Molecular dynamics simulations were run for 100 ns, with trajectory frames stored at 10 ps intervals and energy data recorded every 1.2 ps [50,51,52]. Utilizing Desmond’s “Simulation Interaction Diagram,” the study thoroughly examined protein–ligand interactions throughout the simulation, providing a deeper understanding of key binding interactions.

3. Results and Discussion

3.1. QSAR Modelling

Using the MLR model, only one descriptor was chosen out of 123 to best describe the variability in the IC50 independent variable: FractionCsP3 (Table 1). The fractionCsP3 is a descriptor used in medicinal chemistry to assess the saturation level of a molecule. It is defined as the ratio of sp3-hybridized carbon atoms to the total number of carbon atoms in the structure. In this study, which focuses on the design of new 2-quinolone derivatives, reducing the CsP3 fraction is crucial for achieving lower IC50 values and thereby enhancing antibacterial activity against various bacterial strains. A lower CsP3 fraction may contribute to increased molecular rigidity, improved target binding through π-π interactions or hydrogen bonding, and better alignment with the structural features of highly active quinolones.
The two-sided Pearson correlation test was used to determine whether a correlation existed between the FractionCSP3 and the IC50 variables, and the results revealed a Pearson product-moment correlation coefficient of 0.69. To assess the significance of the correlation, a Student’s t-test was used, yielding a p-value of 2.87 × 10−5, indicating that the correlation is statistically significant. After determining the significance of the correlation between both variables, a symbolic regression model was created, resulting in the following mathematical equation:
IC50 = 8.41 × exp (2 × FractionCSP3) − 8.94
The R2 and RMSE metrics from training, cross-validation, and testing (Table 2) were calculated to assess the quality of the equation. The R2 value in both training and prediction exceeds the threshold of 0.61. The RMSE values are low and acceptable for both training and prediction.
After evaluating the performance metrics of the developed QSAR model, Figure 1 was used to analyze further the distribution of predicted versus experimental IC50 values across the full range of compounds. The figure highlights the model’s predictive behavior, particularly for compounds exhibiting high IC50 values. Specifically, compounds with IC50 values greater than 8 were systematically underestimated during both the training and cross-validation phases. This underestimation suggests a potential limitation in the model’s ability to accurately learn from or generalize high IC50 data within these datasets. Interestingly, this issue was not observed in the prediction (external test) phase, where all compounds, including those with high IC50 values, were accurately predicted. This improved predictive accuracy on the test set may reflect the absence of experimental noise or data uncertainty in those specific compounds compared to those in the training and validation sets. Indeed, the discrepancy could stem from considerable experimental variability or measurement errors associated with IC50 values in the training and cross-validation datasets, which may have affected the model’s learning process. Despite this observed trend, the model’s overall performance remains robust. The correlation between predicted and reference values, as shown in Figure 1, supports the reliability of the model. The consistent performance across training, validation, and test sets suggests the model is not overfitting and is capable of generalizing to unseen data. Thus, these findings reinforce the model’s validity and demonstrate its readiness for practical application in predicting the IC50 values of new, untested compounds.

3.2. Synthesis of Novel 2-Oxo-1,2-dihydroquinoline Derivatives

Guided by our established QSAR model, we have selected to synthesize a novel series of compounds that incorporate quinolone and triazole frameworks. The synthesis of the new methyl 1-((1x-benzyl-1H-1,2,3-triazol-4-yl)methyl)-2-oxo-1,2-dihydroquinoline-4-carboxylate derivatives (4a–4c) was executed in four stages, as illustrated in Scheme 3. The two intermediates, esterification 2 and alkylation 3, were synthesized following the reported procedure [41]. The synthetic route commenced with the formation of 2-oxo-1,2-dihydroquinoline-4-carboxylic acid (compound 1), which was achieved by condensing malonic acid with isatin in acetic acid under reflux, using sodium acetate as a base. After isolating and purifying the acid product, esterification was performed by treating it with methanol and a few drops of concentrated sulfuric acid, yielding compound 2. This intermediate was then subjected to N-alkylation under phase-transfer conditions using propargyl bromide as the alkylating agent, in the presence of TBAB, potassium carbonate, and DMF at room temperature for 6 h, furnishing the propargylated product 3 in good yield. The synthesis of novel methyl 1-((1x-benzyl-1H-1,2,3-triazol-4-yl)methyl)-2-oxo-1,2-dihydroquinoline-4-carboxylate derivatives (4a–4c) via 1,3-dipolar cycloaddition has been accomplished. The reaction of these derivatives with several alkyl azides (4-bromobenzyl azide, 4-methylbenzyl azide, and trifluoromethylbenzyl azide), which were synthesized beforehand, with the alkylated product 3, was conducted utilizing eco-friendly methods in a 1:1 ethanol/water mixture and a cost-effective catalyst, CuSO4·5H2O, at ambient temperature.
Thin-layer chromatography (TLC) was used to monitor the progress of the reactions. The synthesized compounds underwent purification via liquid chromatography on silica gel, utilizing the gel as the stationary phase. The various chemical structures were elucidated using standard spectroscopic methods.

3.3. UV-Vis Spectrum

UV-Vis analysis of the three synthesized compounds (4a–4c) revealed maximum absorption wavelengths (λmax) at 375, 362 and 327 nm, respectively. The bathochromic shift observed from 4c to 4a indicates an increase in electron conjugation, as well as the presence of electron-donating groups by mesomeric effects (Br and CF3), which enhances π-electron delocalization. This delocalization decreases the HOMO-LUMO energy difference, resulting in absorption at longer wavelengths. These results indicate that the electronic properties of the substituents have a significant influence on the optical properties of the synthesized compounds, particularly on their absorption behavior in the UV region (Figure 2).

3.4. Drug-likeness and ADMET Results

Many drug development failures are attributed to poor pharmacokinetics and bioavailability, in addition to problems with efficacy and toxicity [53,54]. Among the key pharmacokinetic properties to be evaluated, human intestinal absorption (HIA) and the ability to cross the blood–brain barrier (BBB) are particularly critical. To predict these properties, the Egan boiled egg method has been proposed. The model is based on an analysis of the lipophilicity and polarity of small organic molecules [55]. The technique can be applied at various stages of drug discovery, from screening chemical libraries to evaluating drug candidates in the development phase.
The predictive model of the Egan boiled egg shown in Figure 3 indicates that three ligands (Q3, 4a, 4b, and 4c) are located within the yolk portion of the model. This positioning suggests that the ligands have a strong ability to cross the blood–brain barrier (BBB) and can also be effectively cleared from the central nervous system (CNS) via P-glycoprotein activity. This finding supports its potential as a viable pharmaceutical candidate, as efficient intestinal absorption is crucial for systemic drug distribution and overall therapeutic efficacy.
Additionally, the Drug Bioavailability Radar Mapping tool is utilized in the field of drug development and discovery to evaluate the bioavailability and drug-like properties of a compound. This visual tool provides a graphical representation of crucial physicochemical properties, enabling researchers to determine whether a molecule falls within the optimal range for oral bioavailability. The Radar is based on six parameters: lipophilicity (LIPO) for hydrophobicity assessment, Size (SIZE) for molecular weight determination, polarity (POLAR) for polarity analysis, insolubility (INSOLU) for aqueous solubility prediction, unsaturation (INSATU) for saturation level assessment, and flexibility (FLEX) for structural flexibility assessment based on the number of rotatable bonds [56,57,58].
Examining the bioavailability radars shown in Figure 4 for the analyzed and synthesized molecules (Q3, 4a, 4b, and 4c), we observe that the synthesized ligands 4a, 4b, and 4c exhibit good oral bioavailability, with the ligand falling within the pink area. We can consider these molecules as promising candidates for the synthesis of antibiotics. Molecule Q3 is close to being considered a drug candidate, but there is a slight deviation from the white zone at the point of unsaturation. By adding new pharmacophores to this compound, the graphical line can be drawn entirely in the pink zone.
Ten distinct parameters, ranging from molecular weight to molar refractivity, were analyzed by Lipinski’s rule of five. The molecular weights of these four ligands range from 203 to 443 g/mol, as shown in Table 3. Compound Q3 exhibits a lower number of heavy atoms, with only 15 atoms, while molecule 4c has the highest number of heavy atoms, with 32. The number of heavy aromatic atoms of molecules 4a, 4b and 4c ranges from 10 to 21, with molecules 4a, 4b, and 4c exhibiting a maximum of 21 atoms. The Csp3 fraction varies between 0 and 0.19, with molecule Q3 having a value of 0. The number of rotating bonds falls within the range of 1 to 7 for all molecules. Furthermore, all molecules have fewer than 10 hydrogen bond acceptors/donors, whereas the synthesized molecules have no hydrogen bond donors. Molar refractivity values range from 59.30 to 79.01 Å2; the synthesized molecules exhibit the highest value of 79.01 Å2. The partition coefficient (Log P) value ranges from 1.2367 to 3.4950, which indicates strong lipophilic properties. This coefficient is used to evaluate how the drug distributes between two non-miscible phases, blood and tissues. Finally, the TPSA value ranges from 59.23 to 112.27 Å2 (Table 3). All four molecules showed relevant physicochemical properties, particularly the three synthesized molecules. The analysis of the physicochemical properties of the four molecules reveals adaptable characteristics with good oral bioavailability. The molecular weights and log p values indicate a balanced profile between solubility and membrane permeability. The moderately polar surface areas suggest a favorable ability to cross biological barriers. In addition, the low number of hydrogen bond acceptors and donors, coupled with a limited number of rotatable bonds, suggests sufficient molecular flexibility for stable and efficient binding to the target. In particular, the synthesized ligands exhibit high molar refractive index, reinforcing their potential for critical therapeutic applications. Consequently, the results obtained confirm that these new molecules open the door to promising new synthetic routes as drug candidates.
The four bioactive compounds (Q3, 4a, 4b, and 4c) are presently being analyzed via in silico ADMET experiments utilizing pkCSM and ADMEswiss [59]. Their predicted ADMET properties are summarized in Table 4, highlighting numerous significant findings. All compounds demonstrate significant absorption rates (95–99%), suggesting substantial potential for human gastrointestinal absorption, especially compound 4b. Also, they exhibit considerable water solubility, with Q3 (−2.73 Log mol/L) demonstrating the greatest solubility.
The compounds show significant penetration in BBB permeability. However, with Log PS values ranging from −2.206 to −2.686, CNS permeability is moderate. The distribution of these molecules throughout the BBB and central nervous system is greatly influenced by their lipophilic and polar properties, which are influenced by halogens such as Br and CF3 and alkyl substituents.
Furthermore, CYP3A4, a crucial enzyme involved in drug metabolism, may be both a substrate and an inhibitor target of the compounds [60]. By altering elimination rates and therapeutic duration, this dual role may affect their pharmacokinetics. Most compounds have longer retention times according to clearance indices, except 4b (0.749), which may increase their effectiveness at lower dosages.
Toxicity assessments, including the Ames test, hepatotoxicity, and skin sensitization, revealed no mutagenic or skin sensitization risks (Table 4). However, the synthesized compounds (4a, 4b, and 4c) showed hepatotoxicity. Despite this, all compounds met the evaluated pharmacokinetic criteria, highlighting their potential as drug candidates. These findings will guide further optimization and development for therapeutic applications.

3.5. Molecular Docking Results

A molecular docking study was conducted to validate further the results of the QSAR analysis regarding the significant antibacterial activity of the selected compounds, Q3, and its synthesized derivatives (4a, 4b, and 4c). In this study, the protein chosen as a potential target for the quinolone derivatives included the HSL receptor LasR-OC12 (PDB ID: 3IX3). The docking results, including 2D and 3D visualizations, are clearly presented in Figure 5 and further summarized in Figure 6 and Table 5.
The significant results of this study highlight the different interactions between the ligands (Q3, 4a, 4b, and 4c) and their target protein (LasR-OC12), revealing significant differences in binding energy and types of molecular interactions. Among the synthesized ligands, 4a, 4b, and 4c stand out for their exceptional binding affinity (−9.4 Kcal/mol for 4a, −9.2 Kcal/mol for 4b, and −8.2 Kcal/mol for 4c), indicating a powerful interaction with the LasR-OC12 protein. This significant affinity can be attributed to the nature of the interactions established, including various conventional hydrogen bonds with target residues such as Tyr B56 with distances of 2.34 Å (4a), Tyr B64 with a distance of 2.78 Å, Arg B61 with distances of 2.12 Å (4b), and Arg B61 (4c) with distances of 2.07 Å, respectively, as well as hydrophobic contacts such as pi-sigma interactions with: Leu B36 with a distance of 3.93 Å, Ala B127 with a distance of 3.82 Å (4a), Leu B36 with a distance of 3.83 Å, Trp B88 with a distance of 3.91 Å (4b), Leu B36 at a distance of 3.97 Å, Trp B88 at a distance of 3. 82 Å (4c), and other key interactions such as alkyl and pi-alkyl interactions with Leu B110, Tyr B93, Ala B105, Val B76, Ala B127, Ala B70, Val B70, Ile B52, Leu B40, Ala B50, and Cys B79. In addition, specific interactions such as Pi-Sulfur (Cys B79), Pi-Anion (Asp B73), and Halogen (Gly B126 and Val B76). These different interactions play a key role in stabilizing the ligand-receptor complex. In contrast, the ligand Q3 has the lowest binding affinity, with a value of −7.9 kcal/mol, likely due to the fewer stabilizing interactions. However, it should be noted that all ligands, including Q3, interact with key protein residues, such as conventional hydrogen bonds with Ser B129 and Arg B61 at distances of 3.04 Å and 2.22 Å, respectively; Pi-Sigma with Val B76 at distances of 3.73 Å; and other interactions with various residues such as Ile B52, Thr B75, Thr B115, Asp B73, and Gly B126. Highlighting the importance of these residues in molecular recognition. Overall, these findings provide valuable insight into how ligands bind to the target protein. The observed variations in binding strengths and interaction types offer a solid foundation for designing more effective compounds. By focusing on critical residues and optimizing hydrophobic and electrostatic interactions, these results could be instrumental in developing new therapeutic molecules that precisely modulate the protein’s activity.

3.6. Antibacterial Activity (Results and Discussion)

The methyl 1-((1-benzyl-1H-1,2,3-triazol-4-yl)methyl)-2-oxo-1,2-dihydroquinoline-4-carboxylate derivatives (4a–4c) were evaluated for their in vitro antibacterial activity against both Gram-positive and Gram-negative bacteria. The minimum inhibitory concentrations (MICs) were determined and are presented in Table 6 and Figure 7.
All the synthesized compounds exhibited antibacterial activity against Escherichia coli (ATCC 25922), Bacillus cereus (ATCC 9634), and Bacillus subtilis (ATCC 3366). Among them, compound 4a showed the highest activity, with MIC values of 19 µg/mL against B. subtilis, 38 µg/mL against B. cereus, and 155 µg/mL against E. coli. Compound 4b followed, with MICs of 38 µg/mL, 75 µg/mL, and 315 µg/mL against the same respective strains. Compound 4c exhibited lower activity, with MICs of 315 µg/mL for B. subtilis and B. cereus, and 1250 µg/mL for E. coli.
For reference, the MIC of ciprofloxacin, a standard antibiotic used as a control, was 12–15 µg/mL across the tested bacterial strains. It is essential to note that ciprofloxacin remains significantly more potent than our compounds, as it exhibits inhibitory effects at substantially lower concentrations. This comparison was clarified to avoid any misinterpretation due to unit inconsistencies in the previous version.
The observed antibacterial differences among compounds 4a–4c can be attributed to structural variations. These derivatives result from the conjugation of two pharmacologically relevant scaffolds: 2-quinolone, a core structure in many antibiotics, and 1,2,3-triazole, known for its bioactive properties. Additionally, the nature of the substituents at the para-position of the benzyl group plays a key role in modulating activity.
Compound 4b, which carries a methyl group, likely benefits from enhanced hydrophobic interactions with the LasR-OC12 HSL receptor, improving its antibacterial effect. In contrast, compounds 4a and 4c bear electron-withdrawing groups (Br and CF3, respectively), which may contribute to binding affinity and biological activity.
These in vitro results are consistent with the molecular docking findings, which revealed that compound 4a had the most stable interaction with the target receptor (binding energy: −9.4 kcal/mol), compared to compound 4b (−9.2 kcal/mol) and 4c (−8.2 kcal/mol). In conclusion, although less potent than ciprofloxacin, these novel hybrid molecules show promise as lead structures for the development of next-generation antibacterial agents. Their unique scaffolds may help circumvent bacterial resistance mechanisms, including target site mutations and efflux pump activity. Further pharmacological investigations and structural optimization are warranted to assess their therapeutic potential fully.

3.7. Molecular Dynamics Simulation

In aqueous environments, molecular dynamics simulation (MDSs) is a potent computational method for assessing the dynamic stability and behavior of ligand–receptor complexes. In this work, an MDS was performed to investigate the stability and interaction patterns of compounds 4a, 4b, and 4c inside the active site of the Escherichia coli protein (PDB ID: 3IX3) across a 100-nanosecond simulated period. To closely reflect physiological conditions and evaluate the conformational flexibility and binding stability of the ligand–protein complexes throughout the entire trajectory, the simulations were carried out in an explicit solvent environment.
From Figure 8, we observe the variation in RMSD values for proteins with a resolution below 2 Å. These variations are low, indicating that the proteins in the 4a-3IX3, 4b-3IX3, and 4c-3IX3 complexes have undergone minimal division. We also note that the RMSD for the alpha carbon of the protein’s amino acid residues reaches equilibrium after 5 ns, indicating that the proteins do not change in conformation within the studied complexes and exhibit excellent stability. The RMSD of the 4a ligand in the 4a-3IX3 complex underwent small variations and reached its steady state throughout the simulation, indicating that ligand 4a exhibits excellent stability in the 4a-3IX3 complex. The RMSD of the 4b ligand in the 4b-3IX3 varies between 0 Å and 3.6 Å for 100 ns and reaches its steady state between 0 ns and 70 ns, as well as between 80 ns and 100 ns. Indicating that ligand 4b is stable over both time intervals. Between 70 ns and 80 ns, we observe a significant increase in the RMSD curve of the 4a ligand, followed by a return to the steady state until the end of the simulation. This increase does not affect the ligand’s overall stability. The RMSD of ligand 4c in the 4c-3IX3 complexes increases and then stabilizes after 25 ns, indicating that the ligand reaches its stable state after 25 ns.
From Figure 9, the RMSF plot of the alpha carbon of the protein’s amino acid residues in 4a-3IX3, 4b-3IX3, and 4c-3IX3 is almost similar. This figure shows that the majority of amino acid residues in the studied complexes have very low RMSF values, indicating that these residues are less fluctuating, confirming that complexes 4a-3IX3, 4b-3IX3, and 4c-3IX3 are stable. In this figure, we observe a few amino acid residues (residue indices between 1 and 10 and between 36 and 42, as well as between 156 and 162) with slightly elevated RMSD values, indicating that these residues exhibit slightly higher fluctuations, which their presence in flexible regions can explain. The mean RMSF values of complexes 4a-3IX3, 4b-3IX3, and 4c-3IX3 are 0.68 Å, 0.76 Å, and 0.77 Å, respectively. These mean values are very low, indicating that the high fluctuations observed at some amino acid residues do not significantly influence the overall stability of the complexes studied.
From Figure 10, we observe that hydrogen and hydrophobic interactions contribute significantly. In contrast, water interactions contribute weakly to the stability of the 4a, 4b, and 4c ligands in the active pocket of the Escherichia coli protein. These results show that hydrogen and hydrophobic interactions are more favorable to achieving high stability of the studied ligand in the active site of the Escherichia coli protein. From this figure, we also observe that the amino acid residues Trp60, Arg61, and Tyr64 present higher interaction fractions in the 4a-3IX3, 4b-3IX3, and 4c-3IX3 complexes, indicating that these residues play a significant role in the stability of ligands 4a, 4b, and 4c in the active site of the Escherichia coli protein.
Figure 11 illustrates the 2D binding interactions of the three synthesized compounds (4a, 4b and 4c) with the LasR-OC12-HSL receptor. All three compounds formed stable complexes thanks to multiple bonds, namely hydrogen bonds and hydrophobic contacts. Compound 4a interacts mainly with residues such as Tyr, Leu and Ala. For example, compound 4b exhibits a denser interaction network, including polar interactions and π-π stacking, which suggests a stronger binding affinity. And finally, Compound 4c interacts in the same way through hydrogen bonding and hydrophobic forces, with additional stabilization due to its attracting group (CF3). The differences in bond types reflect the influence of substituents on molecular recognition. These results show that 4b may have the highest binding strength, followed by 4c and 4a.

4. Conclusions

In this study, we successfully designed and synthesized a new series of quinolone–triazole (4a–4c) hybrid molecules, demonstrating their strong potential as novel antibacterial agents. Our integrated workflow, which included molecular docking, molecular dynamics simulations, and experimental validation, allowed us to predict and confirm their antibacterial activity along with favorable drug-like characteristics—compared to previously reported quinolone–triazole compounds, our molecules, especially compound 4a, exhibited superior activity, with minimum inhibitory concentration values as low as 0.019 mg/mL against both Gram-positive and Gram-negative bacterial strains. These results, combined with excellent in silico ADMET profiles, underscore the therapeutic potential of our structural designs. Additionally, our QSAR-guided molecular design approach provided valuable insights into the relationship between structure and activity, which can be used to optimize this scaffold further. Future work will focus on conducting detailed cytotoxicity assays, target identification, and evaluation of in vivo efficacy to confirm the pharmacological potential of these compounds. We believe this study lays a strong foundation for the development of next-generation antibacterial agents based on the quinolone–triazole framework.

Author Contributions

Conceptualization, A.E.-M., A.H. and N.K.S.; methodology, A.E.-M., A.D. and A.M.; software, A.E.-M., A.D. and R.H.; validation, Y.K.-R., S.E., N.K.S., H.E.M., A.M. and A.H.; formal analysis, A.E.-M. and R.H.; investigation, A.E.-M., A.H. and N.K.S.; data curation, A.E.-M.; writing—original draft preparation, A.E.-M.; writing—review and editing, A.D., R.H., N.K.S., A.M., M.M.A., M.H., A.S.A. and A.H.; visualization, Y.K.-R., S.E., N.K.S., A.M. and A.H.; supervision, A.H.; project administration, A.E.-M., M.M.A., A.H. and N.K.S.; funding acquisition, M.M.A., M.H. and A.S.A. All authors have read and agreed to the published version of the manuscript.

Funding

This work is supported by Princess Nourah bint Abdulrahman University researcher supporting project number (PNURSP2025R342), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia. This work is also supported by the Ongoing Research Funding program, (ORF-2025-754), King Saud University, Riyadh, Saudi Arabia.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviation

QSARQuantitative Structure–Activity Relationship
ADMETAbsorption, Distribution, Metabolism, Excretion and Toxicity
MICMinimum Inhibitory Concentration
FT-IRFourier Transform Infrared Spectroscopy
UV-VisUltraviolet–Visible Spectroscopy
MDSMolecular Dynamics Simulation
SARStructure–Activity Relationship
NMRNuclear Magnetic Resonance
APTAttached Proton Test
DMFDimethylformamide
RTRoom Temperature
TLCThin Layer Chromatography
TMSTetramethylsilane
BBBBlood–brain barrier
HIAHuman intestinal absorption
CNSCentral nervous system
PDBProtein Data Bank
TBABTetrabutylammonium bromide

References

  1. Chinemerem Nwobodo, D.; Ugwu, M.C.; Oliseloke Anie, C.; Al-Ouqaili, M.T.; Chinedu Ikem, J.; Victor Chigozie, U.; Saki, M. Antibiotic Resistance: The Challenges and Some Emerging Strategies for Tackling a Global Menace. J. Clin. Lab. Anal. 2022, 36, e24655. [Google Scholar] [CrossRef] [PubMed]
  2. Akram, F.; Imtiaz, M.; Haq, I.U. Emergent Crisis of Antibiotic Resistance: A Silent Pandemic Threat to 21st Century. Microb. Pathog. 2023, 174, 105923. [Google Scholar] [CrossRef]
  3. Tarín-Pelló, A.; Suay-García, B.; Pérez-Gracia, M.T. Antibiotic Resistant Bacteria: Current Situation and Treatment Options to Accelerate the Development of a New Antimicrobial Arsenal. Expert Rev. Anti-Infect. Ther. 2022, 20, 1095–1108. [Google Scholar] [CrossRef]
  4. Naghavi, M.; Vollset, S.E.; Ikuta, K.S.; Swetschinski, L.R.; Gray, A.P.; Wool, E.E.; Aguilar, G.R.; Mestrovic, T.; Smith, G.; Han, C.; et al. Global Burden of Bacterial Antimicrobial Resistance 1990–2021: A Systematic Analysis with Forecasts to 2050. Lancet 2024, 404, 1199–1226. [Google Scholar] [CrossRef] [PubMed]
  5. Gan, B.H.; Gaynord, J.; Rowe, S.M.; Deingruber, T.; Spring, D.R. The Multifaceted Nature of Antimicrobial Peptides: Current Synthetic Chemistry Approaches and Future Directions. Chem. Soc. Rev. 2021, 50, 7820–7880. [Google Scholar] [CrossRef]
  6. El-Mrabet, A.; Haoudi, A.; Kandri-Rodi, Y.; Mazzah, A. An Overview of Quinolones as Potential Drugs: Synthesis, Reactivity and Biological Activities. Organics 2025, 6, 16. [Google Scholar] [CrossRef]
  7. Javahershenas, R.; Nikzat, S. Recent Advances in the Multicomponent Synthesis of Heterocycles Using Tetronic Acid. RSC Adv. 2023, 13, 16619–16629. [Google Scholar] [CrossRef]
  8. Gao, F.; Wang, P.; Yang, H.; Miao, Q.; Ma, L.; Lu, G. Recent Developments of Quinolone-Based Derivatives and Their Activities against Escherichia coli. Eur. J. Med. Chem. 2018, 157, 1223–1248. [Google Scholar] [CrossRef]
  9. Dhiman, P.; Arora, N.; Thanikachalam, P.V.; Monga, V. Recent Advances in the Synthetic and Medicinal Perspective of Quinolones: A Review. Bioorg. Chem. 2019, 92, 103291. [Google Scholar] [CrossRef]
  10. Kaur, R.; Kumar, K. Synthetic and Medicinal Perspective of Quinolines as Antiviral Agents. Eur. J. Med. Chem. 2021, 215, 113220. [Google Scholar] [CrossRef] [PubMed]
  11. Rusu, A.; Munteanu, A.C.; Arbănași, E.M.; Uivarosi, V. Overview of Side-Effects of Antibacterial Fluoroquinolones: New Drugs versus Old Drugs, a Step Forward in the Safety Profile? Pharmaceutics 2023, 15, 804. [Google Scholar] [CrossRef]
  12. Dube, P.S.; Legoabe, L.J.; Beteck, R.M. Quinolone: A Versatile Therapeutic Compound Class. Mol. Divers. 2023, 27, 1501–1526. [Google Scholar] [CrossRef]
  13. Saxena, N.; Shankhdhar, S.; Kumar, A.; Srivastava, N. Part 2, Studies on the Synthesis of Quinolone Derivatives with Their Biological Activity. Curr. Org. Chem. 2024, 28, 185–212. [Google Scholar] [CrossRef]
  14. Millanao, A.R.; Mora, A.Y.; Villagra, N.A.; Bucarey, S.A.; Hidalgo, A.A. Biological Effects of Quinolones: A Family of Broad-Spectrum Antimicrobial Agents. Molecules 2021, 26, 7153. [Google Scholar] [CrossRef]
  15. Moussaoui, O.; Bhadane, R.; Sghyar, R.; El Hadrami, E.M.; El Amrani, S.; Ben Tama, A.; Rodi, Y.K.; Chakroune, S.; Salo-Ahen, O.M.H. Novel Amino Acid Derivatives of Quinolines as Potential Antibacterial and Fluorophore Agents. Sci. Pharm. 2020, 88, 57. [Google Scholar] [CrossRef]
  16. Bazine, I.; Bendjedid, S.; Boukhari, A. Potential Antibacterial and Antifungal Activities of Novel Sulfamidophosphonate Derivatives Bearing the Quinoline or Quinolone Moiety. Arch. Pharm. 2021, 354, 2000291. [Google Scholar] [CrossRef]
  17. Ghanim, A.M.; Girgis, A.S.; Kariuki, B.M.; Samir, N.; Said, M.F.; Abdelnaser, A.; Nasr, S.; Bekheit, M.S.; Abdelhameed, M.F.; Almalki, A.J.; et al. Design and Synthesis of Ibuprofen–Quinoline Conjugates as Potential Anti-Inflammatory and Analgesic Drug Candidates. Bioorg. Chem. 2022, 119, 105557. [Google Scholar] [CrossRef] [PubMed]
  18. Zhang, S.S.; Tan, Q.W.; Guan, L.P. Antioxidant, Anti-Inflammatory, Antibacterial, and Analgesic Activities and Mechanisms of Quinolines, Indoles and Related Derivatives. Mini Rev. Med. Chem. 2021, 21, 2261–2275. [Google Scholar] [CrossRef] [PubMed]
  19. Sharma, V.; Das, R.; Mehta, D.K.; Sharma, D.; Sahu, R.K. Exploring Quinolone Scaffold: Unravelling the Chemistry of Anticancer Drug Design. Mini Rev. Med. Chem. 2022, 22, 69–88. [Google Scholar] [CrossRef] [PubMed]
  20. Swain, S.S.; Pati, S.; Hussain, T. Quinoline Heterocyclic Containing Plant and Marine Candidates against Drug-Resistant Mycobacterium tuberculosis: A Systematic Drug-Ability Investigation. Eur. J. Med. Chem. 2022, 232, 114173. [Google Scholar] [CrossRef]
  21. Ghareeb, E.A.; Mahmoud, N.F.; El-Bordany, E.A.; El-Helw, E.A. Synthesis, DFT, and Eco-Friendly Insecticidal Activity of Some N-Heterocycles Derived from 4-((2-Oxo-1,2-Dihydroquinolin-3-yl)Methylene)-2-Phenyloxazol-5(4H)-One. Bioorg. Chem. 2021, 112, 104945. [Google Scholar] [CrossRef]
  22. Patel, O.P.; Beteck, R.M.; Legoabe, L.J. Antimalarial Application of Quinones: A Recent Update. Eur. J. Med. Chem. 2021, 210, 113084. [Google Scholar] [CrossRef] [PubMed]
  23. Hu, S.; Chen, J.; Cao, J.X.; Zhang, S.S.; Gu, S.X.; Chen, F.E. Quinolines and Isoquinolines as HIV-1 Inhibitors: Chemical Structures, Action Targets, and Biological Activities. Bioorg. Chem. 2023, 136, 106549. [Google Scholar] [CrossRef]
  24. Sharma, V.; Das, R.; Mehta, D.K.; Gupta, S.; Venugopala, K.N.; Mailavaram, R.; Nair, A.B.; Shakya, A.K.; Deb, P.K. Recent Insight into the Biological Activities and SAR of Quinolone Derivatives as Multifunctional Scaffold. Bioorg. Med. Chem. 2022, 59, 116674. [Google Scholar] [CrossRef] [PubMed]
  25. Manzoor, S.; Gabr, M.T.; Nafie, M.S.; Raza, K.; Khan, A.; Nayeem, S.M.; Arafa, R.K.; Hoda, N. Discovery of Quinolinone Hybrids as Dual Inhibitors of Acetylcholinesterase and Aβ Aggregation for Alzheimer’s Disease Therapy. ACS Chem. Neurosci. 2024, 15, 539–559. [Google Scholar] [CrossRef]
  26. Mahmood, A.; Liu, X.; Grice, J.; Medley, G.; Roberts, M. The Efficacy of Quinolones and Fluoroquinolones in the Medicine: A Review Study. Iraqi J. Med. Health Sci. 2024, 1, 27–35. [Google Scholar] [CrossRef]
  27. Brandao, P.; Marques, C.; Burke, A.J.; Pineiro, M. The Application of Isatin-Based Multicomponent Reactions in the Quest for New Bioactive and Druglike Molecules. Eur. J. Med. Chem. 2021, 211, 113102. [Google Scholar] [CrossRef] [PubMed]
  28. Kumaraswamy, B.; Hemalatha, K.; Pal, R.; Matada, G.S.P.; Hosamani, K.R.; Aayishamma, I.; Aishwarya, N.V.S.S. An Insight into Sustainable and Green Chemistry Approaches for the Synthesis of Quinoline Derivatives as Anticancer Agents. Eur. J. Med. Chem. 2024, 275, 116561. [Google Scholar] [CrossRef]
  29. El Ashry, E.H.; Ramadan, E.S.; Abdel Hamid, H.; Hagar, M. Microwave-Assisted Synthesis of Quinoline Derivatives from Isatin. Synth. Commun. 2005, 35, 2243–2250. [Google Scholar] [CrossRef]
  30. Chen, T.-C.; Yu, D.-S.; Chen, S.-J.; Chen, C.-L.; Lee, C.-C.; Hsieh, Y.-Y.; Chang, L.-C.; Guh, J.-H.; Lin, J.-J.; Huang, H.-S. Design, Synthesis and Biological Evaluation of Tetracyclic Azafluorenone Derivatives with Topoisomerase I Inhibitory Properties as Potential Anticancer Agents. Arab. J. Chem. 2019, 12, 4348–4364. [Google Scholar] [CrossRef]
  31. Khan, N.A.; Khan, I.; Abid, S.M.; Zaib, S.; Ibrar, A.; Andleeb, H.; Hameed, S.; Iqbal, J. Quinolinic Carboxylic Acid Derivatives as Potential Multi-target Compounds for Neurodegeneration: Monoamine Oxidase and Cholinesterase Inhibition. Med. Chem. 2018, 14, 1. [Google Scholar] [CrossRef]
  32. Moussaoui, O.; Byadi, S.; Hachim, M.E.; Sghyar, R.; Bahsis, L.; Moslova, K.; Aboulmouhajir, A.; Kandri Rodi, Y.; Podlipnik, Č.; EL Hadrami, E.M.; et al. Selective Synthesis of Novel Quinolones-Amino Esters as Potential Antibacterial and Antifungal Agents: Experimental, Mechanistic Study, Docking and Molecular Dynamics Simulations. J. Mol. Struct. 2021, 1241, 130652. [Google Scholar] [CrossRef]
  33. Rodi, Y.K.; Baba, Y.F.; Mague, J.T.; Chraibi, M.; Benbrahim, K.F.; Chahdi, F.O.; Ouzidan, Y.; Essassi, E.M. New Quinolone-Based Compounds: Synthesis, Crystal Structures and Biological Activities. Sci. Study Res. Chem. Chem. Eng. Biotechnol. Food Ind. 2019, 20, 487–500. [Google Scholar]
  34. Baba, Y.F.; Sebbar, N.K.; Elmsellem, H.; Rodi, Y.K.; Chraibi, M.; Benbrahim, K.F.; Essassi, E.M. Effects of 6-Substituted-2-Oxo-1,2-Dihydroquinoline-4-Carboxylic Acid Derivatives on Mild Steel Corrosion in Hydrochloric Acid Solution: Experimental and Theoretical Studies, and Their Antibacterial Activity. Eur. J. Chem. 2017, 1, 1–11. [Google Scholar]
  35. Yassir, F.B. Nouveaux Hétérocycles Possédant un Motif Quinoléine: Synthèse, Caractérisation, Application et Étude Pharmacologique. Ph.D. Thesis, Université Mohammed V, Rabat, Morocco, 2018. [Google Scholar]
  36. Kovács, D.P.; McCorkindale, W.; Lee, A.A. Quantitative Interpretation Explains Machine Learning Models for Chemical Reaction Prediction and Uncovers Bias. Nat. Commun. 2021, 12, 1695. [Google Scholar] [CrossRef]
  37. Lou, S.; Liu, C.; Chen, Y.; Mo, F. Empowering Machines to Think Like Chemists: Unveiling Molecular Structure-Polarity Relationships with Hierarchical Symbolic Regression. arXiv 2024, arXiv:2401.13904. [Google Scholar] [CrossRef]
  38. El-Mrabet, A.; Diane, A.; Haoudi, A.; Kandri-Rodi, Y.; Mazzah, A. Exploration of Quinolone Amides as Promising Candidates for the Treatment of Human African Trypanosomiasis: In Silico Virtual Screening and Molecular Docking. Russ. J. Gen. Chem. 2025, 95, 1817–1832. [Google Scholar] [CrossRef]
  39. El-Mrabet, A.; Haoudi, A.; Capet, F.; Hökelek, T.; Ahmed, M. Crystal Structure and Hirshfeld Surface Analyses, Interaction Energy Calculations, and Energy Frameworks of Methyl 2-[(4-Cyanophenyl) Methoxy] Quinoline-4-Carboxylate. Struct. Rep. 2025, 81, 650–656. [Google Scholar] [CrossRef]
  40. Yang, L.; Li, T.; Yang, L.; Dong, L.; Chen, J. Two-Dimensional Correlation Spectroscopy Indicates the Infrared Spectral Markers of the Optimum Scorching Degree of Rhubarb (Rhei Radix et Rhizoma) to Enhance the Anti-Inflammatory Activity. Spectrochim. Acta A Mol. Biomol. Spectrosc. 2022, 270, 120853. [Google Scholar] [CrossRef]
  41. El-Mrabet, A.; Haoudi, A.; Dalbouha, S.; Skalli, M.K.; Hökelek, T.; Capet, F.; Rodi, Y.K.; Mazzah, A.; Sebbar, N.K. Crystal Structure, Hirshfeld Surface Analysis, Interaction Energy and Energy Framework Calculations, as well as Density Functional Theory (DFT) Computation, of Methyl 2-Oxo-1-(Prop-2-ynyl)-1,2-Dihydroquinoline-4-carboxylate. Acta Crystallogr. Sect. E Crystallogr. Commun. 2023, 79, 883–889. [Google Scholar] [CrossRef]
  42. Ngamsurach, P.; Praipipat, P. Antibacterial Activities against Staphylococcus aureus and Escherichia coli of Extracted Piper betle Leaf Materials by Disc Diffusion Assay and Batch Experiments. RSC Adv. 2022, 12, 26435–26454. [Google Scholar] [CrossRef] [PubMed]
  43. Bouhdid, S.; Skali-Senhaji, N.; Idaomar, M.; Zhiri, A.; Baudoux, D.; Abrini, J. Antibacterial and Antioxidant Activities of Origanum compactum Essential Oil. Afr. J. Biotechnol. 2008, 7, 1563–1570. [Google Scholar]
  44. Swanson, K.; Walther, P.; Leitz, J.; Mukherjee, S.; Wu, J.C.; Shivnaraine, R.V.; Zou, J.; Valencia, A. ADMET-AI: A Machine Learning ADMET Platform for Evaluation of Large-Scale Chemical Libraries. Bioinformatics 2024, 40, btae416. [Google Scholar] [CrossRef]
  45. Karim, E.M.; Abchir, O.; Nour, H.; Yamari, I.; Bennani, L.; El Kouali, M.H.; Talbi, M.; Errougui, A.; Chtita, S. Discovery of a Potential Inhibitor Against Lung Cancer: Computational Approaches and Molecular Dynamics Study. Phys. Chem. Res. 2024, 12, 579–589. [Google Scholar]
  46. Wang, L.; Behara, P.K.; Thompson, M.W.; Gokey, T.; Wang, Y.; Wagner, J.R.; Cole, D.J.; Gilson, M.K.; Shirts, M.R.; Mobley, D.L. The Open Force Field Initiative: Open Software and Open Science for Molecular Modeling. J. Phys. Chem. B 2024, 128, 7043–7067. [Google Scholar] [CrossRef]
  47. Goodsell, D.S.; Sanner, M.F.; Olson, A.J.; Forli, S. The AutoDock Suite at 30. Protein Sci. 2021, 30, 31–43. [Google Scholar] [CrossRef] [PubMed]
  48. Dassault Systèmes BIOVIA. Materials Studio, R2; Dassault Systèmes BIOVIA: San Diego, CA, USA, 2017. [Google Scholar]
  49. Halder, D.; Das, S.; Aiswarya, R.; Jeyaprakash, R.S. Molecular Docking and Dynamics Based Approach for the Identification of Kinase Inhibitors Targeting PI3Kα Against Non-Small Cell Lung Cancer: A Computational Study. RSC Adv. 2022, 12, 21452–21467. [Google Scholar] [CrossRef]
  50. Lee, S.H.; Kim, J. Transport Properties of Bulk Water at 243–550 K: A Comparative Molecular Dynamics Simulation Study Using SPC/E, TIP4P, and TIP4P/2005 Water Models. Mol. Phys. 2019, 117, 1926–1933. [Google Scholar] [CrossRef]
  51. Martyna, G.J.; Tobias, D.J.; Klein, M.L. Constant Pressure Molecular Dynamics Algorithms. J. Chem. Phys. 1994, 101, 4177–4189. [Google Scholar] [CrossRef]
  52. Cheng, A.; Merz, K.M. Application of the Nosé–Hoover Chain Algorithm to the Study of Protein Dynamics. J. Phys. Chem. 1996, 100, 1927–1937. [Google Scholar] [CrossRef]
  53. Sun, D.; Gao, W.; Hu, H.; Zhou, S. Why 90% of Clinical Drug Development Fails and How to Improve It? Acta Pharm. Sin. B 2022, 12, 3049–3062. [Google Scholar] [CrossRef] [PubMed]
  54. Daina, A.; Zoete, V. A BOILED-Egg to Predict Gastrointestinal Absorption and Brain Penetration of Small Molecules. ChemMedChem 2016, 11, 1117–1121. [Google Scholar] [CrossRef]
  55. Zhu, X.; Ding, G.; Ren, S.; Xi, J.; Liu, K. The Bioavailability, Absorption, Metabolism, and Regulation of Glucolipid Metabolism Disorders by Quercetin and Its Important Glycosides: A Review. Food Chem. 2024, 458, 140262. [Google Scholar] [CrossRef]
  56. Nie, Y.; Kong, Y.; Peng, J.; Sun, J.; Fan, B. Enhanced Oral Bioavailability of Cannabidiol by Flexible Zein Nanoparticles: In Vitro and Pharmacokinetic Studies. Front. Nutr. 2024, 11, 1431620. [Google Scholar] [CrossRef]
  57. Nignpense, B.E.; Francis, N.; Blanchard, C.; Santhakumar, A. The Bioavailability of Polyphenols Following Acute Consumption of Pigmented Barley and Wheat. Food Funct. 2024, 15, 9330–9342. [Google Scholar] [CrossRef]
  58. Pires, D.E.V.; Blundell, T.L.; Ascher, D.B. pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures. J. Med. Chem. 2015, 58, 4066–4072. [Google Scholar] [CrossRef]
  59. Loos, N.H.; Beijnen, J.H.; Schinkel, A.H. The Mechanism-Based Inactivation of CYP3A4 by Ritonavir: What Mechanism? Int. J. Mol. Sci. 2022, 23, 9866. [Google Scholar] [CrossRef] [PubMed]
  60. Bassan, A.; Alves, V.M.; Amberg, A.; Anger, L.T.; Auerbach, S.; Beilke, L.; Bender, A.; Cronin, M.T.; Cross, K.P.; Hsieh, J.-H.; et al. In Silico Approaches in Organ Toxicity Hazard Assessment: Current Status and Future Needs in Predicting Liver Toxicity. Comput. Toxicol. 2021, 20, 100187. [Google Scholar] [CrossRef] [PubMed]
Antibiotics 14 00877 sch001
Scheme 1. Synthesis methods for synthesizing 2-oxo-1,2-dihydroquinoline-4-carboxylic acid from isatin.
Scheme 1. Synthesis methods for synthesizing 2-oxo-1,2-dihydroquinoline-4-carboxylic acid from isatin.
Antibiotics 14 00877 sch001
Antibiotics 14 00877 sch002
Scheme 2. The quinolones compounds and their antibacterial activities.
Scheme 2. The quinolones compounds and their antibacterial activities.
Antibiotics 14 00877 sch002
Antibiotics 14 00877 g001
Figure 1. Reference vs. predicted values.
Figure 1. Reference vs. predicted values.
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Antibiotics 14 00877 sch003
Scheme 3. Synthesis of new methyl 1-((1x-benzyl-1H-1,2,3-triazol-4-yl)methyl)-2-oxo-1,2-dihydroquinoline-4-carboxylate derivatives (4a–4c).
Scheme 3. Synthesis of new methyl 1-((1x-benzyl-1H-1,2,3-triazol-4-yl)methyl)-2-oxo-1,2-dihydroquinoline-4-carboxylate derivatives (4a–4c).
Antibiotics 14 00877 sch003
Antibiotics 14 00877 g002
Figure 2. UV-Vis spectrum of compounds 4a–4c.
Figure 2. UV-Vis spectrum of compounds 4a–4c.
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Antibiotics 14 00877 g003
Figure 3. Predictive model of Egan Boiled Egg for the ligands (molecule 1: Q3, molecule 2: 4a, molecule 3: 4b and molecule 4: 4c).
Figure 3. Predictive model of Egan Boiled Egg for the ligands (molecule 1: Q3, molecule 2: 4a, molecule 3: 4b and molecule 4: 4c).
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Antibiotics 14 00877 g004
Figure 4. Bioavailability radar graphs and the stable structure of the four ligands (Q3, 4a, 4b and 4c).
Figure 4. Bioavailability radar graphs and the stable structure of the four ligands (Q3, 4a, 4b and 4c).
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Antibiotics 14 00877 g005
Figure 5. Three-dimensional representation of active compound binding to LasR-OC12 (PDB: 3IX3).
Figure 5. Three-dimensional representation of active compound binding to LasR-OC12 (PDB: 3IX3).
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Antibiotics 14 00877 g006aAntibiotics 14 00877 g006b
Figure 6. Two-dimensional and 3D representations of the resulting interactions between the active compounds (Q3, 4a, 4b, and 4c) upon complexation with the LasR-OC12 HSL receptor (PDB ID: 3IX3).
Figure 6. Two-dimensional and 3D representations of the resulting interactions between the active compounds (Q3, 4a, 4b, and 4c) upon complexation with the LasR-OC12 HSL receptor (PDB ID: 3IX3).
Antibiotics 14 00877 g006aAntibiotics 14 00877 g006b
Antibiotics 14 00877 g007
Figure 7. In vitro antibacterial activities of compounds 4a–4c against E. coli (ATCC 25922), B. cereus (ATCC 9634) and B. subtilis (ATCC 3366), expressed as inhibition zone diameters (mm). Each value corresponds to the mean diameter of the clear zone around the disc. Results indicate that compound 4a exhibited the largest inhibition zones, particularly against B. subtilis (17 mm).
Figure 7. In vitro antibacterial activities of compounds 4a–4c against E. coli (ATCC 25922), B. cereus (ATCC 9634) and B. subtilis (ATCC 3366), expressed as inhibition zone diameters (mm). Each value corresponds to the mean diameter of the clear zone around the disc. Results indicate that compound 4a exhibited the largest inhibition zones, particularly against B. subtilis (17 mm).
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Antibiotics 14 00877 g008
Figure 8. Root mean square deviation (RMSD) of the 4a-3IX3, 4b-3IX3, and 4c-3IX3 complexes over 100 ns.
Figure 8. Root mean square deviation (RMSD) of the 4a-3IX3, 4b-3IX3, and 4c-3IX3 complexes over 100 ns.
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Antibiotics 14 00877 g009
Figure 9. Root mean square fluctuation (RMSF) of the 4a-3IX3, 4b-3IX3, and 4c-3IX3 complexes over 100 ns.
Figure 9. Root mean square fluctuation (RMSF) of the 4a-3IX3, 4b-3IX3, and 4c-3IX3 complexes over 100 ns.
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Antibiotics 14 00877 g010
Figure 10. Histograms of interaction in the 4a-3IX3, 4b-3IX3, and 4c-3IX3 complexes over 100 ns.
Figure 10. Histograms of interaction in the 4a-3IX3, 4b-3IX3, and 4c-3IX3 complexes over 100 ns.
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Antibiotics 14 00877 g011
Figure 11. The 2D structure of formed complexes LasR-OC12 HSL-4a, LasR-OC12 HSL-4b, and LasR-OC12 HSL-4c.
Figure 11. The 2D structure of formed complexes LasR-OC12 HSL-4a, LasR-OC12 HSL-4b, and LasR-OC12 HSL-4c.
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Table 1. Suggested molecules.
Table 1. Suggested molecules.
Molecules4a4b4c
Fraction CSP30.1430.1820.182
Table 2. Created QSAR models with their performance metrics.
Table 2. Created QSAR models with their performance metrics.
MethodModelTrainingTesting
R2RMSER2RMSE
SRIC50 = 8.41 × exp (2 × FractionCSP3) − 8.94 (1)0.611.810.681.75
Table 3. Physicochemical and Drug-Likeness proprieties of the ligands (Q3, 4a, 4b, and 4c).
Table 3. Physicochemical and Drug-Likeness proprieties of the ligands (Q3, 4a, 4b, and 4c).
Comp.Formulan. Heavy Atomsn. Arom. Heavy AtomsFraction Csp3n. Rotatable BondsLog Pn. H-Bond Acceptorsn. H-Bond DonorsTPSAMolar Refractivity
Q3C10H6BrNO315100.0011.98883270.16 Å259.23
4aC21H17BrN4O329210.1463.23875079.01 Å2112.27
4bC22H20N4O329210.1862.78465079.01 Å2109.53
4cC22H17F3N4O332210.1873.49508079.01 Å2109.57
Table 4. Prediction of the ADME-toxicity pharmacokinetic properties of the ligands (Q3, 4a, 4b, and 4c).
Table 4. Prediction of the ADME-toxicity pharmacokinetic properties of the ligands (Q3, 4a, 4b, and 4c).
Compounds/ADMETAbsorptionDistributionMetabolismExcretionToxicity
Cytochromes (CYP)
Substrate CYPInhibitor CYP
Water SolubilityIntestinal Absorption (Human)BBB PermeabilityCNS
Permeability		2D63A41A23A42C92D6Total ClearanceAMES Test of ToxicityHepatotoxicitySkin Sensitization
Unity(Log mol/L)Numeric (% Abs)Numeric (log BB)Numeric (Log PS)Categorical (Yes or No)Log (mL/min/kg)Categorical (Yes or No)
Q3−2.7395.085−0.385−2.206NoNoNoNoNoNo0.089NoNoNo
4a−4.03697.518−1.077−2.686NoYesYesNoYesNo0.416NoYesNo
4b−4.72198.353−1.315−2.297NoYesYesNoYesNo0.241NoYesNo
4c−3.83299.044−0.893−2.457NoYesYesYesYesNo0.749NoYesNo
Table 5. Illustrates the different interactions established between ligands (Q3, 4a, 4b, and 4c) and receptor residues (LasR-OC12 HSL).
Table 5. Illustrates the different interactions established between ligands (Q3, 4a, 4b, and 4c) and receptor residues (LasR-OC12 HSL).
LigandsMolecular FormulaBinding Affinity (Kcal/mol)Interaction Hydrogen-BindingHydrophobic Interaction
Q3C10H6BrNO3−7.9Conventional H-bond: Ser B129, Arg B61Pi-Sigma: Val B76
Alkyl and Pi-Alkyl: Leu B125, Tyr B47, Ala B127, Ala B70, Leu B36
Pi-Pi stacked: Tyr B64
Van der waals: Ile B52, Thr B75, Thr B115, Asp B73, Gly B126
4aC21H17BrN4O3−9.4Conventional H-bond: Tyr B56
Carbon H-bond: Val B76, Tyr B76, Asp B63, Leu B125		Alkyl and Pi-Alkyl: Leu B110, Tyr B93, Ala B105
Pi-Pi stacked: Trp B88, Phe B101
Pi-Sigma: Leu B36, Ala B127
Pi-Sulfur: Cys B79
Van der waals: Leu B39, Leu B40, Gly B126, Ala B50, Gly B38, Ile B52, Arg B61, Trp B60, Ser B129, Thr B75, Thr B155 B120, Gly A120, Glu B124, Tyr B47, Ser B44, Lys B42
4bC22H20N4O3−9.2Conventional H-bond: Tyr B64, Arg B61
Carbon H-bond: Val B76, Asp B73, Ala B127		Alkyl and Pi-Alkyl: Val B76, Ala B127, Ala B70
Pi-Pi stacked: Trp B70
Pi-Sigma: Leu B36, Trp B88
Pi-Sulfur: Cys B79
Pi-Anion: Asp B73
Van der waals: Leu B110, Leu B40, Gly B126, Ala B50, Gly B38, Ile B52, Trp B60, Ser B129, Thr B75, Thr B155 B120, Phe B101, Tyr B75
4cC22H17F3N4O3−8.2Conventional H-bond: Arg B61
Carbon H-bond: Asp B73		Alkyl and Pi-Alkyl: Val B70, Ala B127, Ile B52, Leu B40, Ala B50, Cys B79
Pi-Pi stacked: Trp B47, Trp B64
Pi-Sigma: Leu B36, Trp B88
Halogen: Gly B126, Val B76
Van der waals: Leu B110, Ala B50, Gly B38, Trp B60, Ser B129, Thr B75, Thr B155, Phe B101, Tyr B75
Table 6. In vitro antibacterial activities of methyl 1-((1-benzyl-1H-1,2,3-triazol-4-yl)methyl)-2-oxo-1,2-dihydroquinoline-4-carboxylate derivatives (4a–4c) against Escherichia coli (ATCC 25922), Bacillus cereus (ATCC 9634), and Bacillus subtilis (ATCC 3366). Minimum inhibitory concentration (MIC) values are expressed in both µg/mL and µM. Ciprofloxacin was used as a reference antibiotic (MIC = 12–15 µg/mL ≈ 36–45 µM).
Table 6. In vitro antibacterial activities of methyl 1-((1-benzyl-1H-1,2,3-triazol-4-yl)methyl)-2-oxo-1,2-dihydroquinoline-4-carboxylate derivatives (4a–4c) against Escherichia coli (ATCC 25922), Bacillus cereus (ATCC 9634), and Bacillus subtilis (ATCC 3366). Minimum inhibitory concentration (MIC) values are expressed in both µg/mL and µM. Ciprofloxacin was used as a reference antibiotic (MIC = 12–15 µg/mL ≈ 36–45 µM).
CompoundsE. coli
ATCC 25922 (ZI)		B. cereus
ATCC 9634 (ZI)		B. subtilis
ATCC 3366 (ZI)
MIC (µg/mL)MIC
(µM)		MIC (µg/mL)MIC (µM)MIC (µg/mL)MIC (µM)
4a15534238831942
4b315811751933898
4c12502825315712315712
Ciprofloxacin154512361236
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El-Mrabet, A.; Diane, A.; Haloui, R.; El Monfalouti, H.; S. Alanazi, A.; Hefnawy, M.; Alanazi, M.M.; Kandri-Rodi, Y.; Elkhattabi, S.; Mazzah, A.; et al. Hybrid 2-Quinolone–1,2,3-triazole Compounds: Rational Design, In Silico Optimization, Synthesis, Characterization, and Antibacterial Evaluation. Antibiotics 2025, 14, 877. https://doi.org/10.3390/antibiotics14090877

AMA Style

El-Mrabet A, Diane A, Haloui R, El Monfalouti H, S. Alanazi A, Hefnawy M, Alanazi MM, Kandri-Rodi Y, Elkhattabi S, Mazzah A, et al. Hybrid 2-Quinolone–1,2,3-triazole Compounds: Rational Design, In Silico Optimization, Synthesis, Characterization, and Antibacterial Evaluation. Antibiotics. 2025; 14(9):877. https://doi.org/10.3390/antibiotics14090877

Chicago/Turabian Style

El-Mrabet, Ayoub, Abderrahim Diane, Rachid Haloui, Hanae El Monfalouti, Ashwag S. Alanazi, Mohamed Hefnawy, Mohammed M. Alanazi, Youssef Kandri-Rodi, Souad Elkhattabi, Ahmed Mazzah, and et al. 2025. "Hybrid 2-Quinolone–1,2,3-triazole Compounds: Rational Design, In Silico Optimization, Synthesis, Characterization, and Antibacterial Evaluation" Antibiotics 14, no. 9: 877. https://doi.org/10.3390/antibiotics14090877

APA Style

El-Mrabet, A., Diane, A., Haloui, R., El Monfalouti, H., S. Alanazi, A., Hefnawy, M., Alanazi, M. M., Kandri-Rodi, Y., Elkhattabi, S., Mazzah, A., Haoudi, A., & Kheira Sebbar, N. (2025). Hybrid 2-Quinolone–1,2,3-triazole Compounds: Rational Design, In Silico Optimization, Synthesis, Characterization, and Antibacterial Evaluation. Antibiotics, 14(9), 877. https://doi.org/10.3390/antibiotics14090877

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