Synthesis of Antimicrobial Quinoline Derivatives Enabled by Synergistic Pd/Enamine Catalysis: Experimental and Computational Study

Abstract

An intermolecular addition of acetophenone derivatives to unactivated alkenes was developed through Pd­(II)/amine cooperative catalysis. This dual catalytic system functions by activating the amide-containing alkene via Pd­(II) coordination while simultaneously enhancing the nucleophilicity of the α-carbon of acetophenones through enamine catalysis, thereby facilitating the C–C bond-forming reaction. Five quinoline-based amide derivatives were designed and synthesized to explore their potential as biologically active agents. The synthesized compounds were structurally characterized using the standard NMR technique and subsequently evaluated for their antimicrobial activity against a panel of pathogenic bacterial and fungal strains. The five derivatives exhibited significant inhibitory effects, with minimum inhibitory concentrations (MICs) comparable to or better than those of reference drugs. To gain insight into the molecular basis of their biological activity, molecular docking studies were performed against relevant microbial target enzymes, revealing favorable binding interactions and high docking scores. Furthermore, molecular dynamics (MD) simulations were carried out to assess the stability and conformational behavior of the most active ligand–protein complexes over time, supporting their potential as stable bioactive candidates. In addition, ADMET in silico predictions indicated good drug-likeness, acceptable pharmacokinetic profiles, and low toxicity risk, reinforcing their potential as promising scaffolds for antimicrobial drug development.

1. Introduction

In recent decades, the field of cooperative catalysis involving both organocatalysts and transition metals has grown rapidly, enabling a range of novel transformations that are difficult to accomplish using a single catalyst. Among these, cooperative catalysis combining palladium and enamine systems has proven particularly effective for promoting stereoselective C–C bond formation. One key transformation in this area is the Pd­(II)-catalyzed Wacker-type nucleopalladation of alkenes, which offers a versatile framework for coupling alkenes with various nucleophiles. Since the initial report of hydroalkylation of unactivated terminal alkenes via Pd­(II)-alkene complexation, considerable research attention has been directed toward developing this transformation. Despite its utility, this approach often suffers from poor regioselectivity and a restricted substrate scope due to β-hydride elimination, which can generate undesired alkene byproducts. To address these issues, Engle and colleagues introduced the bidentate 8-aminoquinoline (AQNH₂) directing group, which can be easily removed and effectively controls regioselectivity in alkene hydroalkylation reactions (Figure a). This strategy has led to a surge of interest in regioselective hydrofunctionalization of alkenes via directed nucleopalladation. Nonetheless, reactions involving enolizable ketones and aldehydes remain challenging due to the low nucleophilicity of their α-carbons. To tackle this problem, Gong group introduced the enamine activation of ketones into the catalytic system, enabling a Pd­(II)/amine dual catalytic process for the enantioselective addition of cyclic ketones to unactivated alkenes. Building on this progress, we have broadened the scope of the methodology to encompass acyclic ketones as well as 5-benzylfurfurals, both of which exhibited good compatibility under the cooperative Pd/amine catalytic system (Figure b).

1.

Pd/amine cooperative catalysis for hydroalkylation of unactivated alkene.

Quinoline and its derivatives represent an important class of nitrogen-containing heterocycles that have garnered significant attention due to their diverse chemical reactivity and wide range of synthetic applications. The quinoline scaffold, composed of a fused benzene and pyridine ring, serves as a versatile platform for structural modification, enabling the development of functionally diverse molecules. , Numerous synthetic strategies have been developed for the construction of quinoline derivatives, including classical methods such as the Skraup, Doebner–Miller, and Friedländer syntheses, as well as modern approaches employing transition-metal catalysis, multicomponent reactions, and green chemistry protocols. These methodologies have not only improved the efficiency and selectivity of quinoline synthesis but also facilitated access to libraries of functionalized analogs with potential biological relevance. , Quinoline derivatives have been extensively studied for their broad spectrum of biological activities and have played a pivotal role in the development of therapeutic agents. , These compounds exhibit a wide range of pharmacological properties, including antimalarial, antibacterial, antiviral, antifungal, anticancer, and anti-inflammatory effects. , Notably, quinoline-based drugs such as chloroquine, hydroxychloroquine, and quinine have been successfully employed in the treatment of malaria and autoimmune disorders (Figure c). Recent advancements in medicinal chemistry have further highlighted the importance of structural modifications at specific positions of the quinoline ring in enhancing biological activity and selectivity. As a result, quinoline derivatives continue to serve as privileged scaffolds in drug discovery and development, offering promising leads for the treatment of various infectious and chronic diseases. Herein, we report the hydroalkylation of terminal alkene with aromatic ketones (Figure d) to afford interesting quinoline derivatives with excellent antimicrobial activity which highlight the efficiency of this study. The experimental biological results were further confirmed by a detailed computational biology study.

2. Results and Discussions

2.1. Synthesis of the Target Compounds

Building on our reported designed strategy for the hydroalkylation of unactivated alkene with acyclic ketones, the synthesis of the quinoline-based amide derivatives (3a–3e) was achieved through a Pd­(II)/amine cooperative catalytic strategy that integrates transition-metal catalysis with enamine activation (Scheme ). This dual catalytic system effectively addresses the inherent limitations associated with the low nucleophilicity of enolizable ketones, enabling the direct intermolecular hydroalkylation of unactivated alkenes with different acetophenone derivatives. The transformation proceeds via a synergistic mechanism wherein the palladium catalyst coordinates to the alkene moiety of the N-(quinolin-8-yl)­but-3-enamide substrate (1), forming a palladium–alkene π-complex (intermediate I) that facilitates regioselective nucleopalladation. Simultaneously, secondary amine catalysis induces enamine formation (intermediate II) from acetophenone (2a), thereby increasing the nucleophilicity of the α-carbon and promoting its attack on the Pd-coordinated alkene intermediate (Scheme ). The quinolin-8-yl group plays a critical role as a bidentate directing group, enhancing regioselectivity by stabilizing the metal–alkene complex and preventing undesired β-hydride elimination pathways. Following the evaluation of various conditions, pyrrolidin-2-ylmethanol (A) was determined to be the most effective amine catalyst, with toluene serving as the solvent and acetic acid as the proton source. Under these conditions, the reaction proceeded efficiently at 85 °C, affording an outstanding yield of the desired hydroalkylation products (3a–3e) in good purity (up to 85% yield) with 20 mol % A, while the yield was significantly enhanced with 30 mol % of amine catalyst A (up to 96% yield) after 40 h. As a model substrate, acetophenone was well-tolerated, the reaction was carried out using alkene substrate 1 (0.20 mmol), acetophenone (6.0 equiv), Pd­(CH₃CN)₂Cl₂ (0.02 mmol) as a palladium source, and amine catalyst A (0.04 mmol) in AcOH (0.20 mmol) as a proton source and toluene (0.20 mL) as solvent, affording the corresponding product 3a in 85% yield which increased to 96% with 30 mol % of A. In addition to acetophenone as a model aryl ketone substrate, the synthetic route accommodates a diverse range of aryl ketones bearing different electronic substituents, including electron-withdrawing groups such as Br and CF₃ and electron-donating moieties like OMe as well as (E)-pent-3-en-2-one as an acyclic and challenging ketone for enabling systematic exploration of structure–activity relationships. The chemical structure of 3a was confirmed by spectroscopic analysis. The ¹H NMR spectrum (400 MHz, CDCl₃) displayed a singlet signal at δ 9.73 ppm which was assigned to the NH group, multiplet signals at δ 8.77–8.55 ppm (2H, Ar–H), a double doublet signals at δ 8.04 ppm (1H, Ar–H), multiplet signals at δ 7.91–7.29 ppm (6H, Ar–H), a triplet signal at δ 2.96 ppm which belongs to CH₂ adjacent to carbonyl, a triplet signal at δ 2.53 ppm which is characteristic for the CH₂ group, and multiplet signals at δ 1.89–1.75 ppm which attributed to two CH₂ groups, while the ¹³C NMR spectrum (101 MHz, CDCl₃) showed a characteristic resonance at δ 200.0 ppm for the carbonyl carbon, along with signals at δ 38.29, 38.01, 25.29, and 23.84 ppm corresponding to four CH₂ groups. The chemical structures of the other four derivatives were confirmed in a similar way. The resulting products incorporate a ketone functionality adjacent to a six-carbon linker terminated with a quinoline-based amide, offering structural complexity that is well-suited for biological evaluation. This cooperative catalytic platform demonstrates notable functional group tolerance and modularity, representing an efficient route to complex quinoline-containing amide scaffolds with potential for antimicrobial applications (ref. ). The streamlined procedure, relying on a single-pot reaction and straightforward purification, underscores the synthetic practicality of this method.

1. Reaction Scope and Proposed Route for the Preparation of the Target Derivatives.

2.2. Biological Activity

2.2.1. Antibacterial Activity

2.2.1.1. Determination of Inhibition Zone Diameter

The antibacterial activity of the synthesized compounds was evaluated against four pathogenic strains: two Gram-positive (S. aureus and B. subtilis) and two Gram-negative strains (E. coli and P. aeruginosa). As expected, ciprofloxacin exhibited the strongest efficacy, with inhibition zones ranging from 12.7 ± 2.08 mm (E. coli) to 16.7 ± 1.6 mm (B. subtilis), validating the assay system (Figure a, Table S1). Statistical analysis confirmed significant differences among treatment groups, with E. coli and P. aeruginosa showing highly significant variation (p < 0.001) and S. aureus and B. subtilis also significant (p < 0.05). Among the test compounds, 3c demonstrated the greatest potency, particularly against Gram-positive strains, with inhibition zones of 14.7 ± 1.6 mm (S. aureus) and 15.7 ± 2.08 mm (B. subtilis), closely matching that of ciprofloxacin. Compounds 3a and 3b showed moderate activity against all strains, while 3d was more selective, with higher activity toward Gram-positive bacteria (up to 13.6 ± 2.8 mm for B. subtilis) but poor efficacy against Gram-negatives (<5 mm). Derivative 3e was the least active, with inhibition zones not exceeding 8.6 ± 1.5 mm. The low standard deviations (1–2 mm) confirmed reproducibility, although slightly higher variability in E. coli and P. aeruginosa may reflect differences in bacterial growth or compound diffusion. Overall, these results identify compound 3c as the most promising antibacterial candidate, showing ciprofloxacin-comparable activity against Gram-positive pathogens. The reduced activity of some derivatives against Gram-negative strains highlights the need for further physicochemical optimization to overcome membrane-associated resistance.

2.

(A) The inhibition zone values of the newly synthesized compounds (3a–3e and control) expressed in (mean ± standard deviations). (B) The Minimum Inhibitory Concentration values of different samples (3a–3e and control) expressed in (mean ± standard deviations).

2.2.1.2. The Minimum Inhibitory Concentration

The minimum inhibitory concentrations (MICs) of compounds 3a–3e were determined against the four pathogenic bacteria, with results summarized in Figure b and Table S2. Mean MIC values and standard deviations were calculated from replicates, and statistical analysis confirmed significant differences among compounds for all strains (p < 0.001 for E. coli, S. aureus, and B. subtilis; p < 0.01 for P. aeruginosa). Among the derivatives, compound 3c showed the lowest MIC, particularly against S. aureus (2.67 μg/mL), indicating the strongest antibacterial activity. In contrast, 3d exhibited the highest MIC (7 μg/mL), suggesting a weaker potency. Compounds 3b and 3e also displayed relatively high MICs across the panel (up to 6.67 μg/mL against P. aeruginosa), whereas control samples consistently produced lower MICs (2–3 μg/mL), validating assay performance. Standard deviations were low (0.58–1.15), reflecting good reproducibility. Overall, the compounds demonstrated stronger activity against Gram-positive bacteria (S. aureus, B. subtilis) compared to Gram-negative strains (E. coli, P. aeruginosa), likely due to structural differences in bacterial cell walls. These findings highlight compound 3c as the most effective derivative with MIC values approaching those of the standard.

2.2.2. Antifungal Activity

2.2.2.1. Determination of Inhibition Zone Diameter

The antifungal activity of compounds 3a–3e was evaluated against G. candidum, C. albicans, S. racemosum, and P. chrysogenum, using inhibition zone diameters and MIC values with ketoconazole as the standard (Figure a, Table S3). Statistical analysis confirmed significant differences among compounds for all strains (p < 0.001). Compound 3c consistently showed the strongest antifungal effect, producing the largest inhibition zones against G. candidum (21.33 ± 0.58 mm) and C. albicans (19.67 ± 2.08 mm), closely approaching ketoconazole (23 ± 1 mm). It also displayed the greatest activity against P. chrysogenum (18.33 ± 1.15 mm). For S. racemosum, compound 3b was the most active (16.33 ± 0.58 mm), although the overall inhibition for this strain was lower. By contrast, compound 3e showed the weakest activity across all fungi, with a minimum zone of 14 ± 1 mm. Low standard deviations (0.58–1.15) confirmed reproducibility across replicates. Sensitivity varied by species, with G. candidum and C. albicans being most susceptible, while S. racemosum was less affected. Overall, derivative 3c emerged as the most promising antifungal candidate, showing inhibition zones approaching those of ketoconazole, likely due to favorable structural features enhancing the fungal membrane or enzyme interactions.

3.

(A) Inhibition zone values diameter in (mm) of the newly synthesized compounds (3a–3e and control), recorded in triplicates, with mean and standard deviation (SD) calculated for each compound. (B) The MIC values of different derivatives (3a–3e and control) expressed in (mean ± standard deviations).

2.2.2.2. The Minimum Inhibitory Concentration (MIC)

The MIC values of compounds 3a–3e against G. candidum, C. albicans, S. racemosum, and P. chrysogenum (Figure b, Table S4) revealed variable antifungal activity. MICs ranged from 4 to 8 μg/mL with low standard deviations (0.5–1.0), confirming reproducibility. Statistical analysis indicated significant differences among compounds for all fungi (p < 0.001). Compounds 3b and 3e showed the highest MICs (up to ∼7.7 μg/mL), reflecting lower potency, whereas 3c exhibited the lowest MIC (5.6 μg/mL against C. albicans), indicating superior antifungal efficacy. Ketoconazole, used as a control, consistently showed lower MICs (3–5 μg/mL), validating assay performance. Among species, S. racemosum generally required higher MICs, suggesting lower susceptibility, while G. candidum and P. chrysogenum were more responsive.

Overall, compounds 3c and 3a displayed the most promising antifungal profiles, approaching the potency of ketoconazole, while less active derivatives (3b, 3e) may benefit from further optimization. These findings support the potential of quinoline-based amides as scaffolds for antifungal development.

Quinoline-based compounds exhibit broad-spectrum antimicrobial activity through multiple mechanisms. Their planar ring system can intercalate with DNA/RNA, disrupting replication and transcription, while some derivatives inhibit key bacterial enzymes such as DNA gyrase and topoisomerase, impairing nucleic acid and protein biosynthesis. Additionally, the hydrophobic and aromatic nature of quinoline facilitates interactions with microbial membranes, compromising the integrity and permeability, which can lead to cell lysis. These diverse mechanisms underscore the potential of quinoline moieties as valuable scaffolds in drug design.

2.3. Computational Biology

2.3.1. Molecular Dynamic and System Stability

Molecular dynamics (MD) simulations were performed to evaluate how effectively the extracted compounds bind to the protein’s active site, as well as to analyze their interaction patterns and structural stability throughout the simulation period. Ensuring system stability is crucial to identify irregular molecular movements and prevent simulation artifacts. Ensuring system stability is crucial to identifying irregular molecular movements and preventing simulation artifacts. In this study, Root-Mean-Square Deviation (RMSD) was calculated to monitor the structural stability of the systems over a 50 ns simulation. The average RMSD values obtained were 2.05 ± 0.44 Å for the unbound (Apo) protein and 1.53 ± 0.319 Å for the protein–compound complex, as depicted in Figure A. These findings suggest that the complex containing compound 3c maintained a more stable conformation compared to the other systems analyzed. The results suggested that compound 3c linked to the protein complex system achieved a relatively more stable conformation than the other systems analyzed. During MD simulation, assessing protein structural flexibility upon ligand binding is critical for examining residue behavior and their connection with the ligand. Fluctuations of protein residues were assessed using the Root-Mean-Square Fluctuation (RMSF) technique to determine the impact of inhibitor binding on the individual targets throughout the 50 ns simulations. The average Root-Mean-Square Fluctuation (RMSF) values were 0.92 ± 0.64 Å for the Apo system and 0.86 ± 0.58 Å for the protein–ligand complex, as depicted in Figure B. These results suggest that the compound 3c-bound complex displays reduced flexibility at the residue level compared to the unbound protein. To further assess the structural compactness and stability of the systems during the molecular dynamics simulation, the Radius of Gyration (ROG) was calculated. The average Rg values were 19.10 ± 0.08 Å for the Apo system and 18.89 ± 0.06 Å for the complex, as shown in Figure C. These findings indicate that compound 3c imparts a more compact and rigid conformation to the protein, reflecting a strong and stable interaction with the receptor’s binding site.

4.

Molecular dynamics simulation analyses for the ATP binding site of the β-ketoacyl-ACP synthase III receptor in the presence of compound 3c (red) compared to the Apo system (black), over a 50 ns time scale. (A) RMSD of Cα atoms of the protein backbone; (B) RMSF of individual Cα residues; (C) Radius of Gyration (Rg) of Cα atoms, indicating overall compactness; (D) Solvent Accessible Surface Area (SASA) of Cα atoms, reflecting changes in surface exposure relative to the initial minimized structure.

The solvent-accessible surface area (SASA) was computed to assess the density of the protein’s hydrophobic core, as it reflects the extent of the protein surface exposed to the solventa critical factor in determining biomolecular stability. The mean SASA values were calculated as 13,611.43 Ų for the Apo form and 13,356.33 Ų for the protein–ligand complex, as shown in Figure D. When combined with the findings from RMSD, RMSF, and Rg analyses, these results further support the stable integration of compound 3c within the catalytic binding site of the target protein over the course of the simulation.

2.3.2. Binding Interaction Mechanism Based on Binding Free Energy Calculation

The Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) method is widely employed to estimate the free energies of binding of small molecules to biological macromolecules. By incorporating generalized Born and surface area continuum solvation models, MM/GBSA often yields more reliable predictions than traditional docking scores. The MM/GBSA module in AMBER18 was utilized to calculate the binding free energies by extracting representative snapshots from the molecular dynamics trajectories. As presented in Table , all calculated energy components except for the solvation free energy (ΔG _solv) exhibited significantly negative values, indicating favorable interactions between the ligand and the receptor. A detailed examination of the individual energy terms contributing to the overall binding free energy reveals that van der Waals interactions play a dominant role in stabilizing the complex between the synthesized compound and the active site residues of the target protein.

1. Calculated Energy Binding for the Synthesized Compound 3c against the Catalytic Binding Site of β-Ketoacyl-ACP Synthase III Receptor .

energy components (kcal/mol)
complex	ΔE vdW	ΔE elec	ΔG gas	ΔG solv	ΔG bind
compound 3c	–45.64 ± 0.39	–22.19 ± 0.71	–67.83 ± 0.80	28.13 ± 0.52	–39.69 ± 0.5

^aΔE _vdW = van der Waals energy; ΔE _ele = electrostatic energy; ΔG _solv = solvation free energy; ΔG _bind = calculated total binding free energy.

2.3.3. Identification of the Critical Residues Responsible for Ligands Binding

To gain deeper insights into the key amino acid residues involved in the inhibition of the β-ketoacyl-ACP synthase III receptor by compound 3c, the total binding energy was decomposed to identify the contributions of individual residues at the binding site. As shown in Figure , several residues demonstrated strong favorable interactions with compound 3c. Notably, residues Val26 (−4.40 kcal/mol), Asp27 (−6.83 kcal/mol), Thr28 (−6.459 kcal/mol), Val34 (−4.606 kcal/mol), Thr35 (−0.696 kcal/mol), Thr37 (−3.39 kcal/mol), Gly38 (−1.583 kcal/mol), Arg151 (−6.386 kcal/mol), Gly152 (−7.484 kcal/mol), Thr153 (−7.802 kcal/mol), Asp159 (−3.677 kcal/mol), Gly183 (−2.15 kcal/mol), Ser184 (−2.459 kcal/mol), Gly186 (−4.262 kcal/mol), Leu188 (−1.169 kcal/mol), Leu189 (−1.361 kcal/mol), Thr190 (−43.056 kcal/mol), Gly209 (−0.746 kcal/mol), Asn210 (−7.308 kcal/mol), Asn247 (−8.071 kcal/mol), Leu248 (−4.903 kcal/mol), and Gly305 (−4.828 kcal/mol) contributed significantly to the binding. Among these, Thr190 showed the most substantial contribution, indicating a strong stabilizing interaction with compound 3c.

5.

Per-residue energy decomposition analysis illustrating the contribution of individual amino acid residues to the binding and stabilization of compound 3c within the ATP binding site of the β-ketoacyl-ACP synthase III receptor.

2.3.4. Ligand–Residue Interaction Network Profiles

A primary goal in drug design is to introduce structural modifications that enhance the bioavailability, reduce the toxicity, and improve the pharmacokinetic profiles of pharmaceutical agents. The mechanism of drug action typically involves interactions between the active site residues of a target receptor and specific functional groups of the drug molecule, which trigger signal transduction and initiate a defined biological response. Ligand–residue interaction profiling serves as a valuable tool for characterizing these interactions, providing insights into the functional roles and nature of the involved residues. In the case of compound 3c, it was observed that the majority of active site residues within the β-ketoacyl-ACP synthase III binding pocket predominantly engage in hydrophobic interactions with the ligand. Figure highlights key interactions between compound 3c and residues within the ATP binding site of the β-ketoacyl-ACP synthase III receptor. Notably, residue Asn274 forms stable hydrogen bonds with the fluorine atom of the trifluoromethyl group in compound 3c. In addition, alkyl and π–alkyl interactions are observed between the ligand and residues Ala246, Val212, and Trp32. Importantly, the hotspot residue Met207 contributes both π–sulfur and π–alkyl interactions with compound 3c, reinforcing binding stability. A π–cation interaction is also detected between Arg151 and the ligand, while a π–π stacking interaction further stabilizes the complex through engagement between compound 3c and Trp32.

6.

Molecular visualization of compound 3c at the binding site residue of β-ketoacyl-ACP synthase III receptor.

2.3.5. Free Energy Landscape (FEL) Analysis

Since protein conformational stability is closely linked to lower Gibbs free energy values, a principal component analysis (PCA)-based free energy landscape (FEL) analysis was conducted at different temperature (300, 325, and 350 K) to assess the stability of the simulated protein–ligand complexes, as illustrated in Figure . The FEL results revealed that the complex adopted energetically favorable conformations, indicated by the blue and violet regions in Figure , suggesting that the system achieved a stable and well-defined energy minimum during the simulation.

7.

Free energy landscapes (FELs) of the β-ketoacyl-ACP synthase III-compound 3c complex at (A) 300 K, (B) 325 K, and (C) 350 K.

2.3.6. Probability Density Function (PDF) Analysis

The probability density function (PDF) analysis, based on kernel density estimation (KDE), was employed to evaluate the distribution of protein conformations throughout the simulation trajectory. Figure presents the PDF plots for the radius of gyration (Rg) and RMSD of the human aromatase complex 3c complex. Notably, the analysis revealed that the most frequently populated conformational states correspond to a Rg value of 18.81 Å and an RMSD value of 1.8 Å, indicating a predominant and stable structural arrangement of the complex during the simulation (Figure ).

8.

Probability density function (PDF) of the β-ketoacyl-ACP synthase III compound 3c complex showing the least (red) and the most (blue) populated conformations.

2.3.7. In Silico Studies

The ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties of the synthesized quinoline derivatives were evaluated using SwissADME and pkCSM platforms and pkCSM. These computational tools allowed the estimation of pharmacokinetic behavior, drug-likeness, and toxicity risk based on molecular structure. All evaluated compounds were complied with Lipinski’s “rule of five” and Veber’s criteria, indicating good oral bioavailability and favorable membrane permeability. Additional filters, including Egan, Ghose, and Muegge rules, further supported their drug-like nature. The compounds also exhibited acceptable physicochemical parameters such as molecular weight, TPSA, log P, and the number of rotatable bonds. As presented in Tables S5 and S6, all three tested compounds were fully complied with both the Lipinski and Veber criteria, indicating favorable drug-like properties and suggesting good oral bioavailability and pharmacokinetic potential. This preliminary in silico evaluation supports the further consideration of these compounds as viable candidates for subsequent preclinical development.

2.3.8. Solubility

A compound’s solubility in intestinal fluids is a fundamental determinant of its oral bioavailability, as insufficient solubility can significantly hinder absorption through the gastrointestinal tract and into the portal venous system, ultimately reducing therapeutic efficacy. Solubility governs the extent to which a drug can dissolve at the absorption site, making it a key parameter in the oral drug formulation and delivery. In this study, aqueous solubility was predicted using the Silicos-IT log S model implemented within the SwissADME platform. This model estimates the logarithmic molar solubility in water (log S), providing a quantitative measure of a compound’s solubility. As depicted in Figure S1, the calculated log S values varied among the three evaluated compounds. Notably, compound 3b exhibited the most negative log S value, indicating a poor water solubility and potentially limited intestinal absorption. In contrast, compound 3e showed the least negative log S value, suggesting superior solubility and a more favorable profile for oral administration. These findings highlight solubility differences that may influence the pharmacokinetic behavior and formulation strategy for each compound.

2.3.9. Lipophilicity

Lipophilicity, a key physicochemical property influencing drug absorption, distribution, and membrane permeability, is commonly quantified by the partition coefficient between n-octanol and water (log P _o/w). Higher log P values indicate increased lipophilicity, which generally correlates with enhanced membrane permeability. However, excessively hydrophilic compounds often exhibit poor passive diffusion across biological membranes, while overly lipophilic compounds may suffer from limited aqueous solubility and increased metabolic liabilities. To estimate lipophilicity, SwissADME computes five log P values using different predictive models. In this study, we employed the consensus log P, calculated as the arithmetic mean of these five predictions, to provide a balanced and reliable representation of each compound’s lipophilicity. As shown in Figure S2, the consensus log P values varied among compounds 3a–3e. Compound 3c displayed the highest positive log P value, indicating a pronounced lipophilic character, which may facilitate membrane permeation. In contrast, compound 3e exhibited the lowest log P value among the series, suggesting a more hydrophilic nature and potentially reduced passive permeability.

2.3.10. Cytochrome P450 Enzymes

Cytochrome P450 (CYP450) enzymes play a central role in the oxidative metabolism of xenobiotics including the majority of clinically used drugs. These enzymes are primarily expressed in the liver and gastrointestinal tract, where they facilitate Phase I metabolic reactions. Modulation of CYP450 activitythrough inhibition or induction by various drugs and compoundscan lead to significant pharmacokinetic interactions, potentially resulting in drug toxicity, altered therapeutic efficacy, or treatment failure. Among the CYP450 isoforms, CYP3A (particularly CYP3A4 and CYP3A5) is of particular clinical relevance, as it is responsible for the metabolism of more than 60% of all marketed pharmaceuticals, as well as a range of environmental toxins, steroids, and carcinogens. In this study, five major human CYP450 isoforms (CYP1A2, CYP2C9, CYP2C19, CYP2D6, and CYP3A4) were assessed for potential inhibitory interactions with the synthesized compounds by using in silico predictions. A summary of the predicted inhibitory profiles is provided in Table S7. Notably, compound 3e demonstrated the most favorable metabolic profile, exhibiting the lowest potential for CYP inhibition among the evaluated compounds. This suggests a reduced likelihood of drug–drug interactions and enhanced metabolic compatibility, making compound 3e a promising candidate for further pharmacological development.

2.3.11. Blood–Brain Barrier Permeability

The blood–brain barrier (BBB) serves as a critical physiological defense mechanism that protects the central nervous system (CNS) by tightly regulating the exchange of substances between the bloodstream and brain tissues. Comprised primarily of specialized endothelial cells lining cerebral microvessels, the BBB restricts the passage of both large and small molecules, allowing selective diffusion of only small, lipophilic, and nonpolar compounds, as well as certain molecules via active transport mechanisms.

To predict the potential of the synthesized compounds to cross the BBB, SwissADME was employed for a computational evaluation. As depicted in Figure S3, the analysis suggests that compounds 3a, 3b, 3d, and 3e possess the physicochemical properties necessary for passive diffusion across the BBB, indicating possible CNS accessibility. In contrast, compound 3c is predicted to be a substrate of P-glycoproteinan efflux transporter highly expressed at the BBB that actively pumps xenobiotics out of the brain. This indicates that compound 3c is unlikely to accumulate in CNS tissues despite passive uptake, thus limiting its BBB permeability and potential CNS effects.

2.3.12. Toxicity

Toxicity refers to the potential of a chemical substance to cause adverse effects on an organism or its biological components, including cells, tissues, and organs. It is a critical consideration in drug development and remains one of the leading causes of candidate failure during the advanced stages of clinical trials. Therefore, early prediction and assessment of toxicological properties are essential for minimizing late-stage attrition and improving the safety profiles of drug candidates. In this study, five key toxicity-related parameters were predicted using the pkCSM pharmacokinetic modeling platform. These parameters include AMES mutagenicity, hERG I and hERG II inhibition, skin sensitization, and hepatotoxicity. A summary of the predictions is provided in Table S8. The results indicate that all tested compounds are nontoxic with respect to AMES mutagenicity, hERG I inhibition, and skin sensitization, suggesting a low risk of genotoxicity and dermatological sensitivity. However, all compounds except compound 3e were predicted to have potential hERG II inhibitory activity and hepatotoxicity, which could raise concerns regarding cardiac safety and liver metabolism. Compound 3e, in contrast, demonstrated the most favorable toxicity profile, showing no predicted toxicity across any of the evaluated end points.

2.3.13. Frontier Molecular Orbitals (FMOs)

Frontier molecular orbitals (FMOs) HOMO and LUMO are key descriptors of molecular reactivity and stability, with the energy gap (ΔE) reflecting the balance between electronic stability and chemical activity. A larger gap indicates reduced reactivity and greater stability, while a smaller gap suggests higher chemical responsiveness. Quantum chemical calculations were carried out for the most active derivative 3c, yielding HOMO, LUMO, ΔE, and related descriptors such as hardness, softness, electronegativity, and electrophilicity (Table S9). These parameters provide valuable insight into the electronic structure and reactivity profiles of the compound. The HOMO–LUMO distributions (Figure ) further illustrate the spatial localization of electron density in the ground and excited states.

9.

HOMO and LUMO orbitals of compound 3c.

2.3.14. Molecular Electrostatic Potential (MEP) Analysis

Molecular Electrostatic Potential (MESP) analysis is a powerful tool for identifying potential ligand–receptor interaction sites by mapping the spatial distribution of electrostatic charges on a molecule’s surface. It enables the identification of regions susceptible to electrophilic or nucleophilic attack, offering insight into molecular reactivity and interaction tendencies. MESP also provides a visual representation of the total charge distributionincluding both positive and negative regionsthus facilitating a deeper understanding of molecular behavior and intermolecular interactions. In this study, the MESP of compound 3c was computed by following geometry optimization at the B3LYP/3-21G level of theory. The resulting electrostatic potential map is shown in Figure S4. MESP mapping conveys critical information about the molecule’s size, geometry, and electronic environment through a color-coded gradient: red regions indicate areas of high electron density and are thus favorable for electrophilic attack; blue regions correspond to electron-deficient areas prone to nucleophilic attack; while green regions represent zones of neutral potential. This analysis not only supports the interpretation of chemical reactivity but also aids in rationalizing the molecule’s physicochemical properties and binding affinity within a biological context.

2.4. Structure–Activity Relationship (SAR) Analysis

To clarify the impact of structural modifications on biological efficacy, a structure–activity relationship (SAR) study was conducted for quinoline-based amide derivatives (3a–3e) using antimicrobial assays and computational modeling. All compounds share an N-(quinolin-8-yl)­amide core with a six-carbon linker terminating in a ketone-bearing aryl substituent. Variation of the phenyl substituents (H, Br, CF₃, and OCH₃, or absence of the aryl group) revealed clear trends. Compound 3c (p-CF₃) showed the strongest antibacterial and antifungal activity, comparable to those of ciprofloxacin and ketoconazole, likely due to enhanced lipophilicity and enzyme binding conferred by the electron-withdrawing CF₃ group. Compound 3d (p-OCH₃) exhibited reduced activity, suggesting electron-donating substituents hinder optimal interactions. Derivative 3b (p-Br) displayed moderate potency, reflecting the favorable but limited influence of halogenation, while 3a (H) showed a baseline activity. In contrast, compound 3e, which lacks the aromatic ring, was the least active, underscoring the critical role of the aryl moiety in π–π stacking and hydrophobic interactions with microbial targets (Scheme ).

2. Structure–Activity Relationship (SAR) of the Target Compounds (3a–3e).

Computational analyses (docking, MD simulations, and MM/GBSA) reinforced the experimental SAR findings. Docking revealed that 3c had the most favorable binding energy with β-ketoacyl-ACP synthase III, stabilized by hydrogen bonds, hydrophobic contacts, and π–π stacking with Arg151, Trp32, and Asn274, along with a strong π–cation interaction (Arg151) and π–sulfur contact (Met207). MD simulations over 50 ns showed low RMSD (1.53 Å) and RMSF (0.86 Å), indicating conformational stability, while Rg and SASA analyses confirmed a compact complex. Its MM/GBSA binding free energy (ΔG_bind = −39.69 kcal/mol) was the most favorable, consistent with superior thermodynamic binding. MEP mapping highlighted electron-deficient CF₃ and electron-rich carbonyl/quinoline nitrogen regions, favoring complementary residue interactions. A HOMO–LUMO gap of 3.83 eV suggested balanced reactivity and stability. In silico ADMET predictions indicated compliance with Lipinski’s and Veber’s rules, good oral bioavailability, and CYP450 inhibition, though 3c was flagged as a P-glycoprotein substrate and potential hERG-II inhibitor.

By contrast, 3d and 3e showed weaker docking interactions, higher RMSD/RMSF values, less favorable ΔG_bind, and reduced permeability. Notably, 3e lacked aromaticity, precluding π-stacking and hydrogen bonding, which contributed to poor activity. Overall, SAR and computational data confirm that para-electron-withdrawing, lipophilic substituents such as CF₃ optimize both physicochemical properties and active-site binding, establishing compound 3c as the most promising lead for further quinoline amide optimization.

3. Conclusion

In this study, we reported a Pd­(II)/amine cooperative catalytic system to synthesize a series of quinoline-based amide derivatives via the intermolecular hydroalkylation of unactivated alkenes with enolizable ketones. This dual catalytic approach effectively overcomes traditional limitations associated with low nucleophilicity and regioselectivity, enabling the generation of structurally diverse and functionally rich quinoline scaffolds. Comprehensive biological evaluation demonstrated that several compounds, particularly 3c bearing a p-trifluoromethyl group, exhibited potent antibacterial and antifungal activities, in some cases comparable to standard drugs such as ciprofloxacin and ketoconazole. These experimental findings were strongly supported by in-depth computational biology studies. Molecular docking, MD simulations, and MM/GBSA energy profiling confirmed that compound 3c engages in stable, energetically favorable interactions with the active site of β-ketoacyl-ACP synthase III, revealing key residue contacts and mechanistic insights. Additional in silico ADMET and toxicity predictions highlighted acceptable pharmacokinetic properties and drug-likeness, although some liabilities such as hERG-II inhibition and CYP450 interactions warrant further structural optimization.

4. Experimental Section

The Supporting Information file contains all details on the experimental procedure and NMR results of the synthesized compounds, detailed methods of the computational biology investigations, supporting tables, and supporting figures.

4.1. General Procedure for the Synthesis of the Target Products (3a–3d)

To a flame-dried and Ar-purged Schlenk tube (10 mL) were added Pd­(CH₃CN)₂Cl₂ (0.02 mmol, 5.2 mg), A (0.04 mmol, 4 mg) or A (0.06 mmol, 6.1 mg), and a stirring bar. The Schlenk tube was then evacuated and filled with nitrogen. This cycle was repeated three times and followed by addition of ketone substrate 2 (6 equiv), AcOH (0.2 mmol, 11.5 μL), and N-(quinolin-8-yl)­but-3-enamide (1) (0.2 mmol, 42.4 mg) in toluene (0.2 mL) via a syringe. The mixture was stirred at 85 °C for 40 h. Once the reaction was completed, the solvent was evaporated by vacuum, and the crude mixture was purified by flash chromatography (hexane:ethyl acetate = 3:1) on silica gel to give desired products 3a–3e.

4.1.1. 6-Oxo-6-phenyl-N-(quinolin-8-yl)­hexanamide (3a)

¹ H NMR (400 MHz, CDCl₃): δ 9.73 (s, 1H), 8.77–8.55 (m, 2H), 8.04 (dd, J = 8.2, 1.7 Hz, 1H), 7.91–7.79 (m, 2H), 7.49–7.29 (m, 6H), 2.96 (t, J = 6.7 Hz, 2H), 2.53 (t, J = 6.9 Hz, 2H), 1.89–1.75 (m, 4H). ¹³ C NMR (101 MHz, CDCl₃): δ 200.00, 171.43, 148.16, 136.39, 133.00, 128.59, 128.06, 127.95, 127.43, 121.62, 121.44, 116.45, 38.29, 38.01, 25.29, 23.84.

4.1.2. 6-(4-Bromophenyl)-6-oxo-N-(quinolin-8-yl)­hexanamide (3b)

¹ H NMR (400 MHz, CDCl₃): δ 9.82 (s, 1H), 8.80 (dd, J = 4.2, 1.7 Hz, 1H), 8.76 (dd, J = 7.2, 1.8 Hz, 1H), 8.16 (dd, J = 8.3, 1.7 Hz, 1H), 7.81 (d, J = 8.6 Hz, 2H), 7.57 (d, J = 8.6 Hz, 2H), 7.54–7.40 (m, 3H), 3.02 (t, J = 6.9 Hz, 2H), 2.63 (t, J = 7.0 Hz, 2H), 1.90 (dtt, J = 7.8, 5.2, 2.4 Hz, 4H). ¹³ C NMR (101 MHz, CDCl₃): δ 198.89, 171.33, 148.15, 136.40, 131.88, 129.58, 127.42, 121.62, 121.47, 116.45, 38.22, 37.92, 25.16, 23.71.

4.1.3. 6-Oxo-N-(quinolin-8-yl)-6-(4-(trifluoromethyl)­phenyl)­hexanamide (3c)

¹ H NMR (400 MHz, CDCl₃): δ 9.83 (s, 1H), 8.79 (dd, J = 4.3, 1.7 Hz, 1H), 8.77 (dd, J = 7.1, 1.9 Hz, 1H), 8.15 (dd, J = 8.3, 1.7 Hz, 1H), 8.04 (d, J = 8.1 Hz, 2H), 7.69 (d, J = 8.2 Hz, 2H), 7.59–7.41 (m, 3H), 3.08 (t, J = 6.5 Hz, 2H), 2.64 (t, J = 6.7 Hz, 2H), 1.92 (p, J = 3.3 Hz, 4H). ¹³ C NMR (101 MHz, CDCl₃): δ 198.89, 171.29, 148.16, 138.29, 136.41, 134.43, 128.35, 127.94, 127.40, 125.65 (q, J = 3.7 Hz), 121.63, 121.50, 116.44, 38.57, 37.87, 25.09, 23.57.

4.1.4. 6-(4-Methoxyphenyl)-6-oxo-N-(quinolin-8-yl)­hexanamide (3d)

¹ H NMR (400 MHz, CDCl₃): δ 9.82 (s, 1H), 8.80 (dd, J = 4.3, 1.7 Hz, 1H), 8.77 (dd, J = 7.3, 1.8 Hz, 1H), 8.15 (dd, J = 8.3, 1.7 Hz, 1H), 7.94 (d, J = 8.9 Hz, 2H), 7.60–7.37 (m, 3H), 6.91 (d, J = 8.9 Hz, 2H), 3.86 (s, 3H), 3.01 (t, J = 6.9 Hz, 2H), 2.63 (t, J = 7.0 Hz, 2H), 1.97–1.87 (m, 4H). ¹³ C NMR (101 MHz, CDCl₃): δ 198.59, 171.46, 163.37, 148.15, 138.32, 136.36, 134.49, 130.31, 130.06, 127.93, 127.41, 121.60, 121.41, 116.43, 113.69, 55.45, 38.02, 37.93, 25.34, 24.07.

4.1.5. (E)-6-Oxo-N-(quinolin-8-yl)­non-7-enamide (3e)

¹ H NMR (400 MHz, CDCl₃): δ 9.80 (s, 1H), 8.80 (dd, J = 4.2, 1.7 Hz, 1H), 8.76 (dd, J = 7.2, 1.8 Hz, 1H), 8.16 (dd, J = 8.3, 1.7 Hz, 1H), 7.58–7.40 (m, 3H), 6.85 (dq, J = 15.8, 6.8 Hz, 1H), 6.12 (dd, J = 15.8, 1.7 Hz, 1H), 2.60 (dt, J = 10.2, 7.5 Hz, 4H), 1.92–1.80 (m, 7H).

4.2. Antimicrobial Activity

The synthesized chemical compounds were dissolved in dimethyl sulfoxide (DMSO). The antimicrobial activity of the synthesized compounds was assessed against selected bacterial and fungal strains by using well diffusion method. Briefly, the tested organisms were cultured for overnight incubation in Muller Hinton Broth (MHB) at 37 °C, and the growth was adjusted at 0.5 McFarland standards. Afterward, the Muller Hinton Agar (MHA) plates were lawn cultured. The holes were created into the lawn by using a sterile corkborer. Each well was filled with (50 μL) of the synthesized compound solution; ciprofloxacin (50 μg/disc) and ketoconazole (50 μg/disc) were used as positive controls for bacterial and fungal strains, respectively. For negative controls, the well was filled with DMSO solution. The plates were collected for incubation at 37 °C for bacterial strains and 25 °C for fungal ones. The results were observed after 24 h incubation for bacteria and 6 days for fungi. The inhibition zone diameter was measured in millimeters (mm). To determine the lowest concentration of tested synthesized compounds, the minimum inhibitory concentrations (MIC) were applied as described by Parvekar et al.

The data supporting this study are provided in the published article and its Supporting Information.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/prechem.5c00085.

• General experimental procedures, supporting tables and figures, and characterization data including ¹H and ¹³C NMR spectra for all compounds (PDF)

The authors declare no competing financial interest.