De Novo Drug Design, Synthesis, Biological Evaluation, and Structural Examination of Novel Coumarin-Based Pyrimidine Co-Drugs Accompanied by Molecular Docking and DFT Studies

Abstract

In this research, we synthesized novel coumarin-pyrimidine hybrid molecules and studied their antioxidant properties by using the DPPH radical scavenging assay. Different computational techniques (3DQSAR, pharmacophore modeling, molecular docking, MMGBSA, and ADMET) were used to validate the synthesized compounds further. The structures of all synthesized compounds were confirmed by ¹H NMR and ¹³C NMR spectral analysis. The compound 3b showed the best IC₅₀ values of 11.68 μM in DPPH assay and 17.51 μM in H₂O₂ assay. Compound 6b showed the best docking score of −6.49 kcal/mol compared to that of the reference compound. Further, to confirm the formation of a novel linkage, the electronic and structural properties of the lead-most-active compound 6b, reactant 1, and reactant 2 were studied by Density Functional Theory (DFT) calculations on the Gaussian 09 software package. The key findings of the in vitro antioxidant and molecular docking results, as well as the analysis, demonstrated that the synthesized compounds have potential antioxidant activity and can be further optimized to serve as lead compounds. This study reveals that the coumarin-pyrimidine-based molecular hybrid is potent in the DPPH assay and can be further utilized to treat oxidative stress disorders.

1. Introduction

Nitrogen-containing heterocycles are essential not only due to their richness but also due to their biological, chemical, and technological significance. These compounds play a vital role in biological investigations, exhibiting properties such as antidiabetic, antibacterial, anticancer, antitumor, and anti-inflammatory effects. These heterocycles are prevalent in natural products, organic functional materials, medicines, and agrochemicals. These compounds have contributed significantly to the development of numerous new drugs today. Recently, more than 90% of drugs available on the market on a commercial scale have heterocyclic derivatives as their active pharmaceutical ingredients, serving as the core of the drug. Several synthetic and natural medicines, including papaverine, quinine, chlorpromazine, theobromine, diazepam, metronidazole, morphine, and isoniazid, have been derived from heterocyclic molecules. Specifically, nitrogen- and sulfur-containing heterocyclic compounds have made significant contributions to the historical development of drugs through organic synthesis methods.

Several heterocyclic compounds are present in nature as part of the structural units of bioactive molecules, such as hormones, alkaloids, amino acids, vitamins, and hemoglobinthe synthetic heterocyclic compounds pyrimidines, pyridines, and piperidines. Pyrimidines and N-base analogs of uracil, cytosine, and thymine have a significant role in the constitutional part of deoxyribonucleic acid (DNA) and ribonucleic acid (RNA) in the form of nitrogenous bases, as well as in many coenzymes in the human body. The significance of pyrimidine compounds and their derivatives may be established because many drugs contain pyrimidine nuclei as active scaffolds. As a result, many pyrimidine derivatives have gained attention due to enjoyable pharmacological activities, like antihypertensive, antitumor, anti-inflammatory, antioxidant, antifungal, antimalarial, and antibacterial activity. , Universally, coumarins, a class of phenolic compounds found in plant life, have multiple applications in medicine, organic synthesis, and various other fields of everyday life. Coumarins have well-known physiological effects due to their broad-spectrum functionality. The specific structure of the coumarin scaffold involves a conjugated system with electron transport properties and excellent charge. The antioxidant activity of these compounds has been a subject of deep study for at least two decades. A wide variety of synthetic coumarin derivatives have shown promising antiproliferative, anticancer, antitumor, antioxidant, antiviral, anticoagulant, and anti-inflammatory activities. ,

For many years, nitrogen-containing heterocycles have garnered the attention of scientists due to their structural diversity and biological significance. With approximately 60% of unique small molecules containing a nitrogen heterocycle, a glance through the FDA archives demonstrates the structural importance of nitrogen-based heterocycles in drug design. , New FDA-approved nitrogen-containing compounds include daridorexant, mitapivat, pacritinib, abrocitinib, mavacamten, and oteseconazole, all of which are used in the treatment of various diseases. Mitapivat has been used to treat hemolytic anemia in adults with pyruvate kinase insufficiency. This medication has been highly effective in treating hemolytic anemia. Pfizer developed abrocitinib, a selective Janus kinase inhibitor used to treat atopic dermatitis. Daridorexant has been used to treat adult patients who have insomnia characterized by problems with sleep. The 85% FDA-approved drugs contain heterocycles in their basic skeleton, and most importantly, several heterocyclic molecules have been shown to indicate potential benefits against a range of malignancies.

Heterocyclic compounds serve as the scaffold for synthetic drugs, and therefore, novel heterocyclic motifs that act as promising inhibitors are being investigated to control postprandial hyperglycemia. Gisbert Schneider coined the term “scaffold” in 1999. Scaffolding is a process that identifies isofunctional structures with different molecular backbones. The scaffold-hopping approach has been increasingly used in drug discovery. It offers a rational drug design approach, utilizing molecular backbone replacements to form novel molecules with potentially improved properties and generating new chemical entities by modifying the core structure of the molecule.

The pyrimidine ring is an integral part of several molecules. Additionally, various clinically used drugs, including methotrexate and risperidone, contain a pyrimidine heterocyclic scaffold as well. Pyrimidine derivatives exhibit diverse biological activities, including antioxidant and anticancer properties, and can be considered privileged scaffolds in drug discovery for the treatment of various diseases. About 13% of the deaths of human beings all over the world are caused by cancer, which is characterized by invasion, uncontrolled cell growth, and metastasis. The most commonly occurring fatal cancers are breast cancer, stomach cancer, colon cancer, lung cancer, and liver cancer. Among potential anticancer agents, heterocyclic compounds represent an outstanding type of anticancer drug moiety.

Numerous coumarin derivatives are well-known for their broad use in inhibiting and treating venous thromboembolism, strokes, and myocardial infarction. All of these properties of coumarin derivatives have been extensively explored, resulting in an increasing number of new coumarin derivatives being discovered and exhibiting multifaceted biological activities. When the coumarin nucleus is combined with the heterocyclic entity, by either the fusion of rings or with different substituents at various positions, it shows promising and exceptional properties. Coumarins were also tested to characterize their antioxidant properties in multiple systems, specifically targeting reactive oxygen species. Researchers found that various phenolic coumarins have important antioxidant capability by scavenging peroxyl radicals produced by phagocytes during the inflammatory process.

Heterocyclic compounds are capable of various supramolecular interactions, including the ability to bind enzymes, hydrogen bonding, pi-stacking, forming coordination bonds with metals, self-assembly, van der Waals interactions, and hydrophobic interactions. Several studies have found that heterocycles can treat cancer or inhibit ROS production, thereby disrupting the continuity of ROS and cancer progression. ROS (reactive oxygen species) are formed during oxidative metabolism in aerobic organisms. Under normal conditions, ROS production and elimination are in a comparatively balanced state. However, they are exposed to internal and external environmental stressors, such as high glucose levels or UV radiation. ROS production can increase remarkably, leading to oxidative stress. Excessive ROS production damages biomolecules and is closely linked to the pathogenesis of several diseases. Oxidative reactions of biological molecules contribute to various disorders, including cancer, aging, inflammation, atherosclerosis, immunosuppression, diabetes, and neurodegenerative disorders, which are caused by free radicals. Therefore, there is great interest in discovering natural or synthetic antioxidants to prevent free radical damage in the body.

2. Materials and Methods

All chemicals and solvents used for synthesis were obtained from Daejhung (Korea) and Alfa Aesar (Germany) and were purchased from Spectrum Sciences, Lahore, Pakistan. All the reagents were of analytical grade. The products’ formation and purity were analyzed by thin-layer chromatography (TLC) using precoated silica gel (60 F254), with fluorescence visualized under a UV lamp (254 and 336 nm). NMR spectra were evaluated on a BRUKER spectrometer (500 MHz, 125 MHz). Chemical shifts were reported in parts per million (ppm) using CD3OD as the solvent for solubility and TMS as the internal standard. ¹H NMR coupling constants are described in Hz, and multiplicity was represented as a singlet (s), doublet (d), triplet (t), and multiplet (m), respectively.

2.1. General Procedure for Synthesis

2.1.1. Synthesis of Pyrimidine Derivatives

A one-pot, multicomponent procedure synthesized the target compound with minimal amendments to the grindstone method (Scheme ). In this procedure, all reagents in equimolar ratio (see Table for quantitative ratio) were mixed in a round-bottom flask. The reagents ethyl acetoacetate, methylacetoacetate, urea, thiourea, substituted benzaldehyde, and CuCl₂·H₂O (in catalytic amounts)were stirred for 15 min. Then, 3 mL of HCl was added to the mixture and refluxed for 30 min to achieve ring formation. Then, the pyrimidine derivative mixture was obtained and left overnight to achieve maximum yield. Then, the mixture was washed with cold water to remove the acid and the color of the catalyst, dried, washed, and recrystallized with polar-solvent-appropriate solvents to obtain crystals. TLC confirmed the formation of the product.

1. Synthetic Route for the Synthesis of Target Pyrimidine Molecules.

1. Quantitative Amounts (g and mole) of All Chemical Compounds Used in Synthesis.

Sr#	Chemicals used	Quantity used in g (Mole)	S#	Chemicals used	Quantity used in g (Mole)
1	4-flourobenzaldehyde	1.24 (0.01)	6	Thio-Urae
2	2-chlorobenzaldehyde	1.40 (0.01)	7	Urea	0.60, (0.01)
3	3-methoxybenzaldehyde	1.30 (0.01)	8	Con. HCl	0.76 (2 mL)
4	2-methoxybenzaldehyde	1.30 (0.01)	9	Methylacetoacetate	1.16, (0.01)
5	CuCl2.2H2O	Catalytic amount	10	Ethyl acetoacetate	1.30, (0.01)

Chemicals used		Quantity used in g (Mole)
1	ethyl 4-(4-fluorophenyl)-6-methyl-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate 1b	0.70
2	methyl 4-(4-fluorophenyl)-6-methyl-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate for 2b	0.66
3	ethyl 4-(2-chlorophenyl)-6-methyl-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate for 3b	0.74
4	ethyl 4-(2-chlorophenyl)-6-methyl-2-thioxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate for 4b	0.80
5	methyl 4-(2-chlorophenyl)-6-methyl-2-thioxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate for 5b	0.74
5	methyl 4-(2-chlorophenyl)-6-methyl-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate for 6b	0.70
6	methyl 4-(3-methoxyphenyl)-6-methyl-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate for 7b	0.70

2.1.2. Synthesis of Coumarin Derivative

The solution, consisting of 5.5 g of resorcinol dissolved in 10 mL of ethanol, 6.5 g of ethyl acetoacetate, and 6 mL of H₂SO₄, was added dropwise at 0 °C. After complete addition, the mixture was stirred for 5 h at 40 °C. Then, 100 mL of cold water was added to the mixture and stirred for 2 h. After 2 h, the ppt was formed. The ppt was filtered off, washed, dried, and recrystallized to obtain white crystals (Scheme ).

2. Synthetic Route for the Synthesis of Coumarin Molecules.

2.1.3. Synthesis of Coumarin-Based Pyrimidine Molecular Hybrid (General Procedure)

Compounds A and B were mixed to achieve a new compound, C (coumarin-based pyrimidine molecular hybrid). To prepare C, a solution of 4-methylumbelliferone (0.44 g) and a pyrimidine derivative (see Table for the quantity of respective pyrimidine derivatives) in a round-bottom flask was stirred at 40–60 °C, and 3 mL of conc. HCl was added after 15 min. After the addition of conc. HCl, the solution was stirred for 2 h, and the ppt was formed. The precipitate was filtered, washed, dried, and recrystallized to obtain product crystals (Scheme ).

3. Synthetic Route for the Synthesis of Coumarin-Based Pyrimidine Hybrid Molecules (R₂ = -C₂H₅, −CH₃; X= −C  O, −C  S).

C-substitution of coumarins and chromones has been observed in both rings under strong acidic conditions (like conc. HCl), in which it is a hydroxyl cation that is attacked. Substitution occurs at C-6 (like nitration, depending upon conditions) and at C-3 through charge delocalization, as in our case. This is because HCl first protonates the carbonyl oxygen, producing a phenolic hydroxyl, which resonates to the double bond (C3–C4) and the nucleophilic carbon at C-3, generating a species that, upon reaction with a substituted pyrimidine, attacks the ortho-chloro group (like 3b). This C–C bond formation is supported through literature (Scheme and Table ). ,

4. Synthetic Reaction Mechanism for the Synthesis of Coumarin-Based Pyrimidine Hybrid Molecules (1b–7b).

2. Structure Information of Synthesized Compounds (1b–7b).

Compound	R 2	R 1	X
1b	Ethyl acetoacetate	p-Flouro	Oxo
2b	Methyl acetoacetate	p-Flouro	Oxo
3b	Ethyl acetoacetate	O-chloro	Oxo
4b	Ethyl acetoacetate	O-chloro	Thioxo
5b	Methyl acetoacetate	O-chloro	Thioxo
6b	Methyl acetoacetate	O-chloro	Oxo
7b	Methyl acetoacetate	m-OCH 3	Oxo

2.2. Spectral Characterization of Synthesized Compounds

2.2.1. Ethyl4-(4-(7-hydroxy-4-methyl-2-oxo-2H-chromen-3-yl)­phenyl)-6-methyl-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate­(1b)

White crystals, Melting point: 175; Molecular weight: 434.45, Molecular formula: C₂₄H₂₂N₂O₆, Yield: 80%. ¹H NMR (500 MHz, CD₃OD): δ 1.19 (3H, t, 16-H), 2.32 (3H, d, J = 1.1 Hz, CH₃, 17-H), 2.88 (3H, s, CH₃, 11’-H), 4.19 (2H, m, 15-H), 4.19 (1H, d, J = 7.1 Hz, 1-NH), 5.42 (1H, d, J = 7.5 Hz, 6-H), 6.34 (1H, dd J = 7.5 Hz, 6’-H), 6.83 (1H, m, H-8’), 7.33 (1H, m, 12-H), 7.33 (1H, m, 8-H), 7.48 (1H, m, 5’-H), 7.64 (1H, m, 9-H), 7.64 (1H, m, 11-H), 8.44 (1H, s, 3-NH), 9.83 (1H, s, H-7’, OH); ¹³C NMR (CD₃OD, 125 MHz): 14.39 (C-16), 17.30 (C-11’), 19.04 (C-17), 51.28 (C-6), 60.15 (C-15), 102.66 (C-8’), 103.61 (C-5), 113.21 (C-6’), 115.84 (C-9’), 127.77 (C-5’), 127.59 (C-8), 127.59 (C-12), 131.30 (C-9), 131.30 (C-11), 131.30 (C-3’), 133.46 (C-10), 142.23 (C-7), 144.07 (C-4’), 148.18 (C-4), 154.04 (C-10’), 154.09 (C-2), 161.64 (C-7’), 164.73 (C-2’), 167.51 (C-13).

2.2.2. Methyl4-(4-(7-hydroxy-4-methyl-2-oxo-2H-chromen-3-yl)­phenyl)-6-methyl-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate (2b)

White crystals, Melting point: 241; Molecular weight: 420.42, Molecular formula: C₂₃H₂₀N₂O₆, Yield: 75%. ¹H NMR (500 MHz, CD₃OD): δ 2.32 (3H, d, J = 1.1 Hz, CH₃, 16-H), 2.88 (3H, s, CH₃, 11’-H), 3.48 (3H, s, 15-H), 5.42 (1H, d, J = 7.7 Hz, 6-H), 6.34 (1H, d J = 7.7, 6’-H), 6.83 (1H, m, 1-NH), 6.83 (1H, m, H-8’), 7.33 (1H, m, 12-H), 7.33 (1H, m, 8-H), 7.48 (1H, m, 5’-H), 7.64 (1H, m, 9-H), 7.64 (1H, m, 11-H), 8.44 (1H, s, 3-NH), 9.83 (1H, s, H-7’, OH); ¹³C NMR (CD₃OD, 125 MHz): 17.30 (C-11’), 19.04 (C-16), 51.17 (C-6), 51.76 (C-15), 102.66 (C-8’), 103.61 (C-5), 113.21 (C-6’), 115.84 (C-9’), 127.77 (C-5’), 127.59 (C-8), 127.59 (C-12), 131.84 (C-9), 131.84 (C-11), 131.30 (C-3’), 133.46 (C-10), 142.23 (C-7), 144.07 (C-4’), 147.83 (C-4), 154.04 (C-10’), 154.09 (C-2), 161.64 (C-7’), 164.73 (C-2’), 167.39 (C-13).

2.2.3. Ethyl4-(2-(7-hydroxy-4-methyl-2-oxo-2H-chromen-3-yl)­phenyl)-6-methyl-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate (3b)

White crystals, Melting point: 202; Molecular weight: 434.45, Molecular formula: C₂₄H₂₂N₂O₆, Yield: 80%. ¹H NMR (500 MHz, CD₃OD): δ 1.19 (3H, t, 16-H), 2.32 (3H, d, J = 1.1 Hz, CH₃, 17-H), 2.93 (3H, s, CH₃, 11’-H), 4.19 (2H, q, 15-H), 6.26 (1H, d, J = 7.8 Hz, 6-H), 6.43 (1H, d, J = 7.9 Hz 1-NH), 6.83 (1H, m, 6’-H), 6.83 (1H, m, H-8’), 7.35 (1H, d, J = 8.1 Hz, 11-H), 7.55 (1H, m, 9-H), 7.55 (1H, m, 10-H), 7.55 (1H, m, 5’-H), 7.55 (1H, m, 12-H), 8.44 (1H, s, 3-NH), 9.83 (1H, s, H-7’, OH); ¹³C NMR (CD₃OD, 125 MHz): 14.39 (C-16), 17.49 (C-11’), 19.04 (C-17), 49.65 (C-6), 60.15 (C-15), 102.66 (C-8’), 105.15 (C-5), 113.21 (C-6’), 116.10 (C-9’), 126.98 (C-12), 127.76 (C-5’), 130.26 (C-10), 131.38 (C-11), 132.12 (C-9), 132.56 (C-3’), 133.77 (C-8), 140.38 (C-7), 143.82 (C-4’), 148.17 (C-4), 153.96 (C-2), 154.04 (C-10’), 153.96 (C-2), 161.64 (C-7’), 164.41 (C-2’), 167.50 (C-13).

2.2.4. Ethyl 4-(2-(7-hydroxy-4-methyl-2-oxo-2H-chromen-3-yl)­phenyl)-6-methyl-2-thioxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate (4b)

Off White crystals, Melting point: 186; Molecular weight: 450.51, Molecular formula: C₂₄H₂₂N₂O₅S, Yield: 77%. ¹H NMR (500 MHz, CD₃OD): δ 1.19 (3H, t, 16-H), 2.38 (3H, d, J = 1.0 Hz, CH₃, 17-H), 2.93 (3H, s, CH₃, 11’-H), 4.19 (2H, m, 15-H), 6.53 (1H, d, J = 6.8 Hz, 6-H), 6.83 (1H, m, 1-NH), 6.83 (1H, m, 8’-H), 7.55 (1H, m, H-5’), 7.35 (1H, d, J = 8.1 Hz, 11-H), 7.55 (1H, m, 9-H), 7.55 (1H, m, 10-H), 7.55 (1H, m, 5’-H), 9.83 (1H, s, H-7’, OH); ¹³C NMR (CD₃OD, 125 MHz): 14.39 (C-16), 17.49 (C-11’), 19.04 (C-17), 51.23 (C-6), 60.15 (C-15), 102.66 (C-8’), 106.19 (C-5), 113.21 (C-6’), 116.10 (C-9’), 127.76 (C-5’), 130.26 (C-10), 131.39 (C-11), 132.13 (C-9), 132.51 (C-3’), 133.67 (C-12), 133.67 (C-8), 140.46 (C-7), 143.82 (C-4’), 147.72 (C-4), 154.04 (C-10’), 161.64 (C-7’), 164.41 (C-2’), 167.73 (C-13), 174.19 (C-2).

2.2.5. Methyl 4-(2-(7-hydroxy-4-methyl-2-oxo-2H-chromen-3-yl)­phenyl)-6-methyl-2-thioxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate (5b)

White crystals, Melting point, 208; Molecular weight 436.48, Molecular formula C₂₃H₂₀N₂O₅S, Yield 77%. ¹H NMR (500 MHz, CD₃OD): δ 2.38 (3H, d, J = 0.9 Hz, CH₃,16-H), 2.93 (3H, s, CH₃, C-11’), 3.48 (3H, s, 15-H), 6.52 (1H, d, J = 6.7 Hz, 6-H), 6.83 (1H, m, H-8’), 6.83 (1H, m, 1NH), 6.91 (1H, d, J = 6.8 Hz, 12-H), 7.35 (1H, d, J = 8.1 Hz, 11-H), 7.55 (1H, m, 5’-H), 7.55 (1H, m, 6’-H), 7.55 (1H, m, 10-H), 7.55 (1H, m, 9-H), 9.83 (1H, s, OH, H-7’); ¹³C NMR (CD₃OD, 125 MHz): δ 17.49 (C-11’), 18.99 (C-16), 51.16 (C-6), 51.76 (C-15), 102.66 (C-8’), 106.13 (C-5), 113.21 (C-6’), 127.02 (C-12), 127.21 (C-5’), 130.26 (C-10), 131.39 (C-11), 132.13 (C-9), 132.51 (C-3’), 133.67 (C-8), 147.34 (C-4), 140.46 (C-7), 143.82 (C-4’), 154.04 (C-10’), 161.64 (C-7’), 164.41 (C-2’), 167.42 (C-13), 174.21 (C-2).

2.2.6. Methyl 4-(2-(7-hydroxy-4-methyl-2-oxo-2H-chromen-3-yl)­phenyl)-6-methyl-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate (6b)

White crystals, Melting point: 225; Molecular weight 420.42, Molecular formula C₂₃H₂₀N₂O₆, Yield 75%, ¹H NMR (500 MHz, CD₃OD): δ 2.32 (3H, d, J = 1.1 Hz, CH₃, 16-H), 2.93 (3H, s, CH₃, C-11’), 3.48 (3H, s, 15-H), 6.43 (1H, d, J = 7.9 Hz, 1NH), 6.26 (1H, d J = 8.5, 6’-H), 6.83 (1H, m, 6-H), 6.83 (1H, m, H-8’), 7.35 (1H, d, J = 8.1 Hz, 11-H), 7.55 (1H, m, 12-H), 7.55 (1H, m, 5’-H), 7.55 (1H, m, 10-H), 7.55 (1H, m, 9-H), 8.44 (1H, s, 3NH), 9.83 (1H, s, OH, H-7’); ¹³C NMR (CD₃OD, 125 MHz): δ 17.49 (C-11’), 19.04 (C-16), 49.17 (C-6), 51.76 (C-15), 102.66 (C-8’), 104.98 (C-5), 113.21 (C-6’), 116.10 (C-9’), 126.98 (C-12), 127.76 (C-5’), 130.26 (C-10), 131.38 (C-11), 132.12 (C-9), 132.51 (C-3’), 133.77 (C-8), 140.38 (C-7), 143.82 (C-4’), 147.79 (C-4), 153.96 (C-2) 154.04 (C-10’), 161.64 (C-7’), 164.41 (C-2’), 167.31 (C-13).

2.2.7. Methyl 4-(3-(7-hydroxy-4-methyl-2-oxo-2H-chromen-3-yl)­phenyl)-6-methyl-2-oxo-1,2,3,4-tetrahydropyrimidine-5-carboxylate (7b)

White crystals, Melting point: 230; Molecular weight: 420.13, Molecular formula: C₂₃H₂₀N₂O₆, Yield: 75%. ¹H NMR (500 MHz, CD₃OD): δ 2.32 (3H, d, J = 1.1 Hz, CH₃,16-H), 2.88 (3H, s, CH₃, C-11’), 3.48 (3H, s, 15-H), 5.93 (1H, d, J = 7.7 Hz, 6-H), 6.44 (1H, d J = 7.5, 6’-H), 6.83 (1H, m, 1NH), 6.83 (1H, m, H-8’), 7.36 (1H, m, 12-H), 7.40 (1H, m 11-H), 7.49 (1H, m, 5’-H), 7.57 (1H, d, J = 2.4 Hz, 8-H), 7.65 (1H, d, J = 7.7 Hz, 10-H), 8.44 (1H, s, 3NH), 9.83 (1H, s, OH, H-7’); ¹³C NMR (CD₃OD, 100 MHz): δ 17.32 (C-11’), 19.04 (C-16), 50.87 (C-6), 51.76 (C-15), 102.66 (C-8’), 103.56 (C-5), 113.21 (C-6’), 115.85 (C-9’), 128.19 (C-12), 127.76 (C-5’), 131.22 (C-10), 128.33 (C-11), 133.90 (C-9), 131.39 (C-3’), 130.32 (C-8), 141.70 (C-7), 143.49 (C-4’), 147.84 (C-4), 154.09 (C-2) 154.04 (C-10’), 161.64 (C-7’), 164.68 (C-2’), 167.40 (C-13).

2.3. DPPH Assay

All synthesized compounds were evaluated for their antioxidant potential by preparing various concentrations to inhibit free radical activity. DPPH solution was prepared by dissolving 0.004 g of DPPH in 100 mL of methanol, which corresponds to a final concentration of 40 μg/mL. A stock solution of ascorbic acid was prepared by dissolving 1.712 mg of ascorbic acid in 100 mL of distilled water, giving a final concentration of 17.12 μg/mL. This stock solution was used to prepare appropriate dilutions to obtain the desired working concentrations (50, 100, 150, 200, 250 μg/mL). , These concentrations were used for activity measurement. All readings were recorded in triplicate.

2.4. Hydrogen Peroxide Scavenging Activity

H₂O₂ scavenging activity of synthesized compounds was determined by checking the reduction of H₂O₂. Concisely, in a typical experiment, 0.4 mL of the sample (synthetic compounds, ascorbic acid, and trolox) solution was added to 0.6 mL of 40 mM H₂O₂ solution, and made up to 2 mL using 50 mM sodium phosphate buffer (pH 7.4), and incubated for 40 min at 30 °C. The absorbance was read at 230 nm. The % inhibition of H₂O₂ was calculated as follows.

$$\mathrm{\%\; of\; hydrogen\; peroxide\; radical\; scavenging\; assay}=[({\mathit{A}}_0{\mathit{-\; A}}_1){/\mathit{A}}_0]\times 100$$

2.5. Statistical Analysis of DPPH Data

The data collected from the experiments DPPH and H₂O₂ assays, underwent a two-way ANOVA analysis within each type of compound and their respective concentrations. To determine whether there were any significant differences between the factors, GraphPad Prism 9.0 software was used to do a two-way ANOVA on the absorbance values of all concentrations. The Tukey test (p< 0.05) was applied to the means in the event of a significant F-ratio to identify the differences between the mean values (Table S1a and S1b: Supporting Information).

2.6. In Silico Studies

Over the last few decades, computer-aided drug design has emerged as a powerful technique crucial in the development of new drug molecules. Generally, different molecular modeling software and various methods are employed in computer-aided drug design. These are becoming useful in inventing novel, high-quality, pharmaceutical active molecules necessary for several pharmaceutical industries at a low cost and without the need for traditional, time-consuming manual screening. The research work, including protein preparation, ligand preparation, field-based 3D-QSAR, ADME, and pharmacophore modeling, was conducted using Maestro 13.4 Schrodinger’s software (Schrodinger Release, 2022–4). In our study, we utilized Schrodinger’s software (Schrodinger Release, 2022–4) to generate pharmacophore models of all synthesized compounds, accompanied by ligand-based design of active molecules, which was subsequently followed by structure-based drug design, including flexible molecular docking.

2.6.1. 3D-QSARs

Field-based 3D-QSAR was utilized to generate models using the phase module of Schrödinger from a prepared library of 56 ligands. Fifty-six IC₅₀ values of heterocyclic compounds were selected and converted to pIC₅₀ as activity. Models were generated by randomly distributing ligands into a training set (70%) and a test set (30%). , Partial Least Squares factors were utilized to linearly fit 5 Gaussian parameters: Steric, electrostatic, hydrophobic, H-bond acceptor, and H-bond donor. Linear regression statistics used these five parameters for the highest number of PLS factors. Analysis of scores for standard deviation (SD), cross-regression coefficient Q, regression coefficient R ², R ² cross-validation (R ²CV), root-mean-square error RMSE, and Pearson-r led us to the best QSAR model.

2.6.2. Data Set Generation for 3D-QSARs

All synthesized compounds, after being employed in the antioxidant assay, were accumulated based on IC₅₀ values expressed in μM. A total of 7 synthesized compounds have IC₅₀ values ranging from 11.68 to 56.44 μM, which were converted into molar values, along with their conversion into pIC₅₀ by employing formula (i).

$$\mathbf{p}\mathbf{I}{\mathbf{C}}_{\mathbf{5}0}=-\log [\mathrm{I}{\mathrm{C}}_{\mathbf{5}0}]$$ 1

For reference, the compound ascorbic acid was used. Smiles IDs of all compounds were uploaded to a CSV file from ChemDraw (PerkinElmer, v19). The CSV file contained compound ID, SMILES ID, IC₅₀, and pIC₅₀ that were exported to the Maestro builder panel for generating 3D structures. All these compounds were optimized by using the Ligprep Module Version 64134 (Schrodinger 2022–24 build 134). By using structures from the project table, the maximum ligand size was set to 500 atoms, and the OPLS_2005 force field was employed for minimization. The pH of these compounds was set at 7.0, and a desalter state was generated. Stereoisomers were labeled with specified chiralities, with one per ligand. All 56 compound libraries (seven synthesized, one reference, and forty-eight selected from the literature) were treated for further performance of modeling studies.

2.6.3. Flexible Ligand Alignment

Phase Pharma (version 5.2) was used for shape screening, flexible ligand superposition, and alignment. Ligands prepared from ligPrep were imported into the ligand alignment interface. The reference compound, ascorbic acid, was taken into consideration, and 100 additional conformers were generated.

2.6.4. Pharmacophore Screening

Phase (v6.4) was used to generate pharmacophore and 3D-QSAR models as antioxidant agents with reference. Aligned ligands, obtained after a successful combination in shape screening, were imported into Phasepanel_gui for creating a pharmacophore model by selecting multiple ligands. Based on the activity properties, all ligands were categorized as active and inactive. Out of 55 compounds, 35 were active, and 8 were inactive. A threshold was set for active ligands having pIC₅₀ > 4.1, and an inactive threshold was set to pIC₅₀ < 3.90 correspondingly. The remaining compounds were considered moderately active. For pharmacophore model generation, the features in the hypothesis were set to 4, with 77% matching 20 actives, and the hypothesis difference condition was set to 0.5. The number of screened molecules was 23. A total of 2087 conformers were generated, and 7355 matches were found. The number of molecules that produced matches was 20, and the number of hits processed was 20.

For pharmacophore model generation features, the hypothesis was set to 4, matching 18 actives, and the hypothesis difference standard was set to 0.5. Features included a maximum of three hydrogen bond donors, three hydrogen bond acceptors, three hydrophobic rings, and three aromatic ringspharmacophore matching tolerance set to 2 Å. Five variants were generated by keeping the minimum and maximum numbers of sites at 0 and 3, respectively.

2.6.5. 3D-QSARs Model Generation

A total of 11 common pharmacophore models were generated. Only 1 AADHH_1 model was chosen based on the best survival score for 3D-QSAR generation using PLS (Partial Least Squares) regression analysis. For dominant model generation with good statistical parameters, the number of PLS was set to 5. A field-based 3D-QSAR was generated, where the ligand file was selected from the pharmacophore project and uploaded to the field-based QSAR interface in Mestro, classified based on pIC₅₀ values. Only active compounds chosen from pharmacophore screening were laboriously built to build a 3D-QSAR model. The training set comprised a total of 11 compounds, and the test set comprised 7 compounds, with a training set percentage of 77. The model was then generated. Its results were detected in QSAR statistics. Results were transferred to a spreadsheet for generating a predicted vs experimental graph using GraphPad Prism 9.

2.6.6. Molecular Docking

Molecular docking is a powerful tool to identify molecular biology and computer-assisted drug design. A molecular docking assessment was conducted using the Glide module in Schrödinger, employing the standard protocol to dock the protein (oxidant) and synthesized compounds, aiming to identify the interaction between the active sites of the receptor protein and the active derivatives of the synthesized compounds. The chemical structure of the synthesized compounds was drawn using the ChemOffice tool (ChemDraw 19.1), assigned a proper 2D orientation, and the energy of every compound was minimized using Chem3D 19. The energy-minimized ligand compounds were then used as input for Schrödinger to carry out the docking simulation. The main objective of molecular docking is to computationally simulate the molecular identification method and acquire an optimized conformation. Moreover, a compound is more biologically potent if there is a large negative value of binding affinity. To match the inhibitory potential of the in vitro activity for the DPPH potential of synthesized compounds, molecular studies were conducted.

2.6.6.1. Protein Preparation

Protein preparation in computational biology involves converting macromolecular structures into more suitable forms for molecular docking. It is essential to employ methods such as eliminating atomic clashes and water molecules from the protein crystal structure, adding hydrogen bonds, and optimizing the structure before docking. The ligand-binding site for docking using the Glide application protein receptor grid was assigned. The 3D crystal structure of the human 1OQ5 protein was obtained from the Protein Data Bank. 1OQ5 comprised good values for stabilizing parameters, including resolution 1.50 Å, free R-value = 0.189, and observed R-value = 0.129. Maestro’s protein preparation (Schrödinger) was utilized to prepare the protein. This tool refined the protein-modified bond orders and charges, added missing hydrogens, and removed water molecules. Finally, the OPLS4 2005 force field was used to decrease and optimize the protein structure.

2.6.6.2. Ligand Preparation

The 3D structures of all seven ligands were prepared using Schrödinger Maestro 13.4 software. Minimization of all ligands was carried out by using the OPLS-2005 force field module.

2.6.6.3. Binding Site Prognosis

Regarding this study, to check the antioxidant potential of the synthesized molecules, along with

the inhibitor was downloaded from the Protein Data Bank in Europea, the PDB.

2.6.7. ADME Calculations

Bioavailability prediction is a vital stage in drug discovery and development, as it prevents many medications from succeeding in the preliminary phases of clinical trials due to their poor pharmacokinetic properties. The ADME properties, including absorption, distribution, metabolism, and excretion of the identified hits, were evaluated using SWISS ADME and Protox, online tools for determining pharmacokinetic properties such as hydrophobicity, water solubility, human oral absorption, blood–brain barrier permeability, and gastrointestinal permeability. Parameters like polar surface area, lipophilicity, including n-octanol and water partition coefficient, water solubility, drug-likeness, Lipinski’s RO5, boiled egg method for blood–brain barrier and gastrointestinal tract absorption, along with more descriptors necessary for the biological system, were calculated and compared with optimal values of each.

2.6.8. DFT Studies on the Reaction Mechanism of Coumarin-Based Pyrimidine Co-Drugs

The electronic and structural properties of compound 6b, reactant 1, and reactant 2b were studied by Density Functional Theory (DFT) calculations on the Gaussian 09 software package. Optimizations of the geometries were conducted at the B3LYP/6–311G level to produce the most stable conformations. The B3LYP/6–311G theory was selected because it is an accurate and efficient theory used to model organic molecules. The B3LYP functional can be used to strongly calculate the exchange and correlation effects, the 6–311G basis set, a triple-ζ split-valence set of basis functions that includes polarization functions, is good to calculate electron distribution and bonding in heteroatom-based systems. The results of the output files were decomposed into the HOMO (Highest Occupied Molecular Orbital), LUMO (Lowest Unoccupied Molecular Orbital), and the total electronic energies. Global quantum chemical descriptors were then computed on the basis of Koopmans theorem using these FMO energies, such as electron affinity, ionization potential, chemical potential, electronegativity, chemical hardness, chemical softness, electrophilicity index, and nucleophilicity index, in addition to other electronic charge transfer. The calculated energies are in electron volts (eV). GaussView software was used to visualize and perform Molecular Electrostatic Potential (MEP) analyses.

3. Results and Discussion

3.1. Chemistry

All compounds were synthesized, purified, dried, and collected in crystalline form. Their formation was confirmed through structural determination and evaluated by their ¹H NMR and ¹³C NMR spectra. The new molecules were obtained as crystalline solids. The ¹H and ¹³C NMR spectra showed characteristic peaks that helped identify and confirm the structures of all new complex molecules (Figure S1, Supporting Information).

Compound Series 1b–7b (Table ) had the same NMR values. In these compounds, nitrogen was not involved in the bond formation. The ¹H NMR and ¹³C NMR spectra of (1b to 7b) displayed characteristic peaks that facilitated the identification and justification of the corresponding compounds. In this series, the amine group was not involved in linkage formation because both signals of the amine protons were present in the spectra at δ 8.44 and 4.19, and there was no downfield signal observed in the benzene spectra of pyrimidine, which confirms that no substituent is present in the ring. One hydroxyl peak was observed at δ 9.83, which confirms that the hydroxyl group of the coumarin ring did not participate in the linkage. Further confirmation of the −OH group is the peak of the carbon signal at δ 161.64. The double bond of coumarin is involved in bond formation. 1b and 2b have an oxo group in the pyrimidine basic core, and the difference lies in the side chain. The side chain of 2b has the methyl acetoacetate group, and 1b has the ethyl acetoacetate group in the side chain. Compound 1b exhibits an ethoxy signal at δ 1.19 (t) and 4.19 (q) in the side chain, as well as an oxo group in the pyrimidine ring, which is signaled at δ 154.4. Compound 2b has the same signal as 1b but differs only in the side chain, which shows a methoxy signal.

In compounds 3b and 6b, coumarin formed a linkage of pyrimidine benzene at the ortho-position. The compounds 4b and 5b feature a thioxo group in the pyrimidine basic core, with signals appearing at δ 174.19 and 174.21, respectively; however, the difference lies in the side chain. The side chain of 5b has the methyl acetoacetate group, and 4b has the ethyl acetoacetate group in the side chain. Similarly, spectral data from 6b and 3b were observed to contain signal peaks for the oxo (C  O) group in the pyrimidine core, and differing from both 4b and 5b whereas 6b contains signals for the methyl acetate, confirming the side chain, and spectral data of 3b has signals of an ethyl acetate group in the side chain of the pyrimidine core. In compounds 1b and 2b, coumarin formed a linkage of pyrimidine benzene at the para-position. In compounds 3b, 4b, 5b, and 6b, coumarin formed a linkage of pyrimidine benzene at the ortho-position. In compound 7b, coumarin formed a linkage of pyrimidine benzene at the meta-position. 7b has oxo in the pyrimidine basic core and the methyl acetoacetate group in the side chain. Therefore, all spectral analyses including ¹H NMR, ¹³C NMR, and mass spectrometry confirm the structres of synthesized molecules­(Figures ,, and ).

1.

¹H NMR spectral analysis of synthesized novel coumarin-based pyrimidine hybrids (1b–7b).

2.

¹³C NMR spectral analysis of synthesized novel coumarin-based pyrimidine hybrids (1b–7b).

3.

Mass fragmentation of synthesized novel coumarin-based pyrimidine hybrid molecules.

4.

(a) IC₅₀ values of antioxidant activity of synthesized compounds (1b–7b) and (b)­Effect of concentration on the activity of synthesized compounds (1b–7b) by two-way ANOVA.

3.2. Biological Evaluation of Title Compounds in DPPH Assay

Free radicals are highly reactive species formed when a molecule gains or loses one or more electrons. They are formed naturally in the body through several biological processes and play a crucial role in cellular functions. However, higher concentrations can be detrimental to biological processes and harm cellular components such as cell membranes, DNA, and proteins. The damage to DNA by free radicals may lead to the development of cancer and other health conditions. Free radicals, which comprise oxygen elements, are also known as reactive oxygen species and are responsible for oxidative stress. DPPH is the simplest and most commonly reported method. DPPH radical scavenging test is a standard and fast technique for screening of radical scavenging activity. It measures the ability to donate hydrogen or electrons, indicating radical scavenging activity, for the assessment of antioxidant activity. In this method, the range of bleaching of the purple color of the methanolic DPPH solution is used to demonstrate the antioxidant activity of the test compounds. The absorbance of the observed color is established at a wavelength of 517 nm using a spectrophotometric method. DPPH is a stable free radical due to delocalization of an additional electron on it and does not permit its dimerization like other free radicals. ,

$$\mathrm{Percentage\; of\; scavenging\; activity}=[({\mathit{A}}_{\mathrm{control}}-{\mathit{A}}_{\mathrm{sample}})/{\mathit{A}}_{\mathrm{blank}}]\times 100$$

All synthesized compounds (1b–7b) were screened for their free radical scavenging potential using DPPH, ascorbic acid, and trolox as standards Figure (part a and 4b). All the compounds demonstrated remarkable antioxidant activity. Compounds 1b and 2b (IC₅₀ 56.44 μM, IC₅₀ 52.54 μM) were found to exhibit low antioxidant activity. Compounds that exhibit high antioxidant activity are 3b–6b (IC₅₀ 11.68 μM, IC₅₀ 13.82 μM, IC₅₀ 19.68 μM, IC₅₀ 12.96 μM). It has been observed that the activity is related to the structural skeletons of compounds with different substituents (Figure ). The attachment of two different rings to the benzene ring plays a major role in enhancing the antioxidant activity, as the rings contain heteroatoms in their skeleton. Molecules containing both nitrogen and oxygen, such as pyrimidinones, hydrazones, and oximes, often show enhanced antioxidant activity due to multitarget interactions.

3.3. Biological Evaluation of Title Compounds as Hydrogen Peroxide Activity and Comparison with DPPH Scavenging Capacity

The antioxidant capacity of the synthesized molecules was further calculated using the H₂O₂ (hydrogen peroxide) scavenging assay and compared to their DPPH radical scavenging activity. H₂O₂, although a weak oxidizing agent, plays a critical role in oxidative stress, as it can easily penetrate biological membranes and generate highly reactive hydroxyl radicals in the presence of metal ions. Therefore, the ability of molecules to scavenge H₂O₂ is considered biologically significant (Figure part a).

5.

(a) IC₅₀ values of antioxidant activity of synthesized compounds (1b–7b) as H2O₂ scavenger and b) Effect of concentration on H₂O₂ activity of synthesized compounds (1b–7b) by two-way ANOVA.

In our study, all tested molecules exhibited concentration-dependent hydrogen peroxide scavenging activity (Figure part b). The reduction in absorbance at 230 nm indicated the effective decomposition of H₂O₂ by the molecules. Among the tested compounds, those that exhibited strong DPPH radical scavenging activity also confirmed comparatively higher H₂O₂ scavenging potentials, suggesting a positive correlation between the two antioxidant assays. However, the overall scavenging effectiveness observed in the H₂O₂ assay was relatively lower than that obtained in the DPPH assay. This difference can be attributed to the distinct reaction mechanisms involved in both assays. The DPPH assay is primarily based on hydrogen or electron donation to a stable nitrogen-centered radical, which favors compounds with a strong reducing capacity. In contrast, the H₂O₂ scavenging assay measures the ability of molecules to neutralize a nonradical reactive species, which requires efficient peroxide decomposition rather than simple electron transfer. As a result, some molecules that performed exceptionally well in the DPPH assay showed moderate activity in the H₂O₂ assay.

Standard antioxidants, including ascorbic acid and trolox, showed superior scavenging activity in both assays, confirming the experimental design. Nonetheless, many synthesized molecules displayed comparable H₂O₂ scavenging activity at higher concentrations, representing their potential as effective antioxidants. The presence of electron-donating functional groups and conjugated systems in these molecules may contribute to their enhanced antioxidant behavior by stabilizing reactive oxygen species. As a result, the comparative analysis of DPPH and H₂O₂ assays highlights that, while DPPH provides rapid screening of free radical scavenging capacity, the H₂O₂ assay offers insight into the biological significance of antioxidant activity. The combined results confirm that the synthesized molecules have promising antioxidant potential and can effectively neutralize both free radicals and ROS species, supporting their possible therapeutic applications against oxidative stress-related disorders. In the hydrogen peroxide assay, the compound 3b showed the best IC₅₀ value of 17.51 μM.

3.4. Structure–Activity Relationship of Target Molecules (1b–7b)

The basic design of the compounds (1b–7b) is based on the already existing FDA-approved and commercially used drug core (Figure ). The target molecules were designed from two key active moieties: coumarin and pyrimidine. As we know very well, both coumarin and pyrimidine are biologically active sites used in drugs. Therefore, based on the structure–activity relationship of the scaffold used, the structure–activity relationship of the target molecules was observed and followed. Compounds with phenolic substituents at the 7-position of the coumarin ring showed an enhancement in activity due to the electron-donating contribution of the −OH groups.

6.

Structure–activity relationship of target synthesized molecules (1b–7b).

In synthetic compounds, the core chromonepyrimidine scaffold was key to antimicrobial and enzyme-inhibiting activity. The chromone moiety enhances π–π interactions with biological targets, while the thioxopyrimidine engages through hydrogen bonding via the C  S and NH groups. In the coumarin moiety, the 7-OH group enhances the antioxidant activity through free radical scavenging. The 4-methyl group increases the lipophilicity, potentially improving cell membrane penetration. The presence of a methyl group in the pyrimidine ring increases the lipophilicity, enhancing bioavailability, but might cause steric hindrance. The presence of the ester moiety (ethyl or methyl) had only minor effects on activity. Compounds 1b to 7b serve as a versatile base structure exhibiting diverse bioactivities and antioxidant enzyme inhibition depending on aryl substituents. The ortho substitution pattern enables conjugation and planarity between the coumarin and dihydropyrimidione rings, facilitating electron delocalization and thereby enhancing the antioxidant potential. The tetrahydropyrimidine-5-carboxylate core in series 1b to 7b consistently shows moderate to good radical scavenging. −

3.5. Computational Methods

3.5.1. Pharmacophore Modeling

The pharmacophore modeling approach, which utilizes ligand- and structure-based screening techniques, enables the rapid screening of millions of chemical entities. It is investigated as a responsible lead-hopping method that can extract a larger range of actives than the traditional structure-based pharmacophore method. The synthesized compounds showed significant activity. In the current study, seven compounds with a coumarin scaffold were synthesized, with IC₅₀ values ranging from 11.68 to 56.44 μM.

3.5.2. Alignment and Pharmacophore Model Validation

The benefits of pharmacophore modeling from ligand-based and structure-based screening techniques allow to rapidly screen millions of chemical entities. The Phase module of Schrödinger was used to build the pharmacophore model, using ascorbic acid as a reference compound. All seven compounds were employed to align with ascorbic acid. The experimental and predicted IC₅₀ values are shown in Table S2 (Supporting Information and Figure S2).

3.5.3. 3D-QSAR Model Certification

After obtaining the alignment data set (Tables S3–S5: Supporting Information), the pharmacophore model was used to generate a field-based 3D-QSAR model (Figure and Table S5: Supporting Information). Therefore, the compounds were divided into two sets: a training set and a test set with a 77:33 ratio. Table S2 presents the training set and test set compounds, along with their predicted and experimental activities, for evaluating the antioxidant potential of the compounds.

7.

Intersite (A) angles and (B) distances between pharmacophoric features of model AADHH_1.

A pharmacophoric hypothesis was developed for these aligned molecules. Therefore, a total of 11 predictive models were generated. Four pharmacophoric features, namely aromatic (R), donor (D), acceptor (A), and hydrophobic (H), were incorporated for model generation. The active molecules with pharmacophoric features were developed in the best-fit model AADHH_1, which is shown in Figure . This model was selected based on its survival score, site score, and volume score, as it has the best ability to differentiate between active and inactive ligands using aligned conformers. The intersite angles and intersite distances depict the arrangement of features present in the model AADHH_1 (Tables S3–S5: SI). The hydrophobicity (H) was mapped due to the presence of two methyl groups: one attached to a pyrimidine ring and the other to a coumarin ring. One hydrogen bond donor (D) feature was mapped to the amino group present in the pyrimidine ring. Two hydrogen bond acceptors were identified as being mapped to the oxygen group present in the coumarin ring.

3.5.4. Field-Based 3D QSAR Modeling

To better understand the correlations between chemical structural features and the biological activity of the ligands, a field-based QSAR model was generated. PLS linear regression analysis was utilized to create QSAR models, and the best model was achieved at PLS factor 5 with SD 0.19, R ² 0.73, R² CV 0.61, R² Scramble 0.78, Stability −0.53, F 4.8, P 0.02, RMSE 0.1, Q² 0.88, Pearson-r 0.97 (Table S6: Supporting Information).

3.5.5. Molecular Docking Analysis for DPPH Assay

Molecular docking aims to predict the experimental binding types and affinities of small molecules within the binding site of precise receptor targets and is currently used as a standard computational tool in drug design for lead compound optimization and virtual screening studies to discover novel biologically active molecules. , Ascorbic acid was used as a standard to analyze the strength or antioxidant potential of synthesized molecules. The selected protein 1OQ5 is for the antioxidant DPPH potential. The docked view of ascorbic acid with 1OQ5 scored −5.07663 kcal/mol (Figure and Table ). The oxygen group of the THR 199 amino acid residue formed one hydrogen bond with the −OH group of the reference compound at a distance of 2.08 Å. The −OH group of the reference had a metal coordination bond (Zn 600) at a distance of 2.13 Å. In ascorbic acid, hydrogen and carbon–hydrogen bond interactions were observed.

8.

(a) 2D interaction of ascorbic acid with 1OQ5and b) 3D interaction of ascorbic acid.

3. Receptor Ligand Interaction of Coumarin-Based Pyrimidine Molecular Hybrid and Standard Using Discovery Studio Software Through the Molecular Docking Analysis.

Compounds
Amino acid residue	Distance	Category	Type	Docking score
Ascorbic acid
A: GLN92	2.99828	Hydrogen Bond	Conventional Hydrogen Bond	–5.08
A: GLN92	2.13382	Hydrogen Bond	Conventional Hydrogen Bond
A: THR199	2.75192	Hydrogen Bond	Conventional Hydrogen Bond
A: THR199	2.72116	Hydrogen Bond	Conventional Hydrogen Bond
A: THR200	2.93832	Hydrogen Bond	Conventional Hydrogen Bond
A: THR200	2.04485	Hydrogen Bond	Conventional Hydrogen Bond
A: HIS119	2.90491	Hydrogen Bond	Conventional Hydrogen Bond
A: THR200	2.88861	Hydrogen Bond	Carbon Hydrogen Bond
A: HIS94	2.57625	Hydrogen Bond	Carbon Hydrogen Bond
2b
A: THR199	1.71119	Hydrogen Bond	Conventional Hydrogen Bond	–5.40
A: ILE91	2.77001	Hydrogen Bond	Carbon Hydrogen Bond
A: HIS94	4.19647	Hydrophobic	Pi-Pi T-shaped
A: HIS94	4.48329	Hydrophobic	Pi-PiT-shaped
A: VAL121	4.29454	Hydrophobic	Alkyl
A: ILE91	4.26639	Hydrophobic	Alkyl
A: PHE131	4.73053	Hydrophobic	Pi-Alkyl
A: VAL121	5.33097	Hydrophobic	Pi-Alkyl
A: LEU198	4.79574	Hydrophobic	Pi-Alkyl
7b
A: THR199	1.94726	Hydrogen Bond		–5.64
A: HIS94	4.34204	Hydrophobic	Pi-Pi T-shaped
A: HIS94	4.49121	Hydrophobic	Pi-Pi T-shaped
A: VAL121	4.13378	Hydrophobic	Alkyl
A: LEU60	5.23588	Hydrophobic	Alkyl
A: ILE91	4.57448	Hydrophobic	Alkyl
A: PHE131	4.5517	Hydrophobic	Pi-Alkyl
A: VAL121	5.07371	Hydrophobic	Pi-Alkyl
A: LEU198	4.82398	Hydrophobic	Pi-Alkyl
1b
A: THR199	1.72869	Hydrogen Bond	Conventional Hydrogen Bond	–5.69
A: ILE91	2.66963	Hydrogen Bond	Carbon Hydrogen Bond
A: HIS94	4.52198	Hydrophobic	Pi–Pi T-shaped
A: HIS94	4.25962	Hydrophobic	Pi–PiT-shaped
A: ILE91	4.36663	Hydrophobic	Alkyl
A: VAL121	4.12832	Hydrophobic	Alkyl
A: PHE131	4.72004	Hydrophobic	Pi-Alkyl
A: VAL121	5.29893	Hydrophobic	Pi-Alkyl
A: LEU198	4.75835	Hydrophobic	Pi-Alkyl
5b
A: ASN62	2.68682	Hydrogen Bond	Conventional Hydrogen Bond	–6.09
A: GLN92	2.99716	Hydrogen Bond	Conventional Hydrogen Bond
A: THR200	2.61306	Hydrogen Bond	Conventional Hydrogen Bond
A: THR200	2.51343	Hydrogen Bond	Conventional Hydrogen Bond
A: THR200	3.74322	Hydrogen Bond	Conventional Hydrogen Bond
A: THR199	1.72265	Hydrogen Bond	Conventional Hydrogen Bond
A: LEU198	3.79886	Hydrophobic	Pi-Sigma
A: TRP5	5.63211	Other	Pi-Sulfur
A: HIS94	4.69722	Hydrophobic	Pi-Pi T-shaped
A: PHE131	5.25676	Hydrophobic	Pi-Pi T-shaped
A: VAL121	3.91224	Hydrophobic	Alkyl
A: LEU60	5.11299	Hydrophobic	Alkyl
A: PHE131	4.44266	Hydrophobic	Pi-Alkyl
A: VAL121	4.84455	Hydrophobic	Pi-Alkyl
3b
A: ASN62	2.43995	Hydrogen Bond	Conventional Hydrogen Bond	–6.32
A: GLN92	2.87206	Hydrogen Bond	Conventional Hydrogen Bond
A: THR200	2.75754	Hydrogen Bond	Conventional Hydrogen Bond
A: THR199	1.92049	Hydrogen Bond	Conventional Hydrogen Bond
A: LEU198	3.79318	Hydrophobic	Pi-Sigma
A: LEU198	3.93337	Hydrophobic	Pi-Sigma
A: HIS94	4.89918	Hydrophobic	Pi-Pi T-shaped
A: PHE131	5.09685	Hydrophobic	Pi-Pi T-shaped
A: VAL121	3.86686	Hydrophobic	Alkyl
A: ILE91	5.01851	Hydrophobic	Alkyl
A: LEU60	4.94263	Hydrophobic	Alkyl
A: PHE131	4.4162	Hydrophobic	Pi-Alkyl
A: VAL121	5.03053	Hydrophobic	Pi-Alkyl
A: VAL143	5.4659	Hydrophobic	Pi-Alkyl
4b
A: ASN62	2.64428	Hydrogen Bond	Conventional Hydrogen Bond	–6.34
A: GLN92	2.22797	Hydrogen Bond	Conventional Hydrogen Bond
A: HIS119	1.96793	Hydrogen Bond	Conventional Hydrogen Bond
A: GLU69	2.42133	Hydrogen Bond	Carbon Hydrogen Bond
A: HIS94	5.26454	Hydrophobic	Pi-PiT-shaped
A: HIS94	4.91628	Hydrophobic	Pi-Pi T-shaped
A: PHE131	5.2459	Hydrophobic	Pi-PiT-shaped
A: ILE91	4.67768	Hydrophobic	Alkyl
A: LEU198	4.46701	Hydrophobic	Alkyl
A: LEU60	4.59288	Hydrophobic	Alkyl
A: VAL121	4.94842	Hydrophobic	Pi-Alkyl
A: LEU198	4.73279	Hydrophobic	Pi-Alkyl
A: LEU198	4.41251	Hydrophobic	Pi-Alkyl
6b
A: ASN62	2.36162	Hydrogen Bond	Conventional Hydrogen Bond	–6.49
A: THR200	2.88965	Hydrogen Bond	Conventional Hydrogen Bond
A: THR199	1.9075	Hydrogen Bond	Conventional Hydrogen Bond
A: GLU69	2.71352	Hydrogen Bond	Carbon Hydrogen Bond
A: LEU198	3.88551	Hydrophobic	Pi-Sigma
A: HIS94	4.90267	Hydrophobic	Pi-Pi T-shaped
A: PHE131	5.14407	Hydrophobic	Pi-Pi T-shaped
A: VAL121	3.93204	Hydrophobic	Alkyl
A: LEU60	4.58234	Hydrophobic	Alkyl
A: PHE131	4.44407	Hydrophobic	Pi-Alkyl
A: LEU198	4.31388	Hydrophobic	Pi-Alkyl
A: VAL121	5.10809	Hydrophobic	Pi-Alkyl

The compound 5b had a docking score of −6.09028, compared to the reference, which has a docking score of −5.07663 kcal/mol. It was observed that hydrogen bond, carbon–hydrogen bond, pi-sulfur, pi-sigma, pi-pi, t-shaped, alkyl, and pi-alkyl interactions occurred between compound 5b and the 1OQ5 protein (Figure (a–b) and Table ).

9.

(a) 2D interaction of 5b (targeted synthesized molecule) with antioxidant protein 1OQ5; b) 3D interaction of 5b (targeted synthesized molecule) with antioxidant protein 1OQ5.

The compound 3b had a docking score of −6.32409 compared to the reference, which has a docking score of −5.07663 kcal/mol. It was observed that hydrogen bond, carbon–hydrogen bond, pi-sigma, pi-pi t-shaped, alkyl, and pi-alkyl interactions occurred between compound 3b and the 1OQ5 protein (Figure (a–b) and Table ). The compound 4b had a docking score of −6.3378 compared to that of the reference, which has a docking score of −5.07663 kcal/mol. It was observed that hydrogen bond, carbon–hydrogen bond, pi-donor hydrogen bond, pi-sigma, pi-pi t-shaped, alkyl, and pi-alkyl interactions occurred between compound 8a and the 1OQ5 protein (Figure (c–d) and Table ).

10.

(a) 2D interaction of 3b (targeted synthesized molecule) with antioxidant protein 1OQ5, b) 3D interaction of 3b (targeted synthesized molecule) with antioxidant protein 1OQ5, c) 2D interaction of 4b (targeted synthesized molecule) with antioxidant protein 1OQ5,and d) 3D interaction of 4b (targeted synthesized molecule) with antioxidant protein 1OQ5.

Compound 6b has the best docking score among all of the synthesized compounds. The compound 6b had a docking score of −6.49871 compared to the reference, which has a docking score of −5.07663 kcal/mol. It was observed that H-bond, carbon H-bond, pi-sulfur, pi-pi stacked, pi-pi T-shaped, alkyl, and pi-alkyl interactions are present between 6b and the 1OQ5 protein (Figure (a, b) and Table ).

11.

(a) 2D interaction of 6b (targeted synthesized molecule) with antioxidant protein 1OQ5 and b) 3D interaction of 6b (targeted synthesized molecule) with antioxidant protein 1OQ5.

In compound 7b, the ammonium group of the ASN 62 amino acid residue formed one H-bond with the carbonyl group of the ligand at a distance of 1.84 Å. The oxygen group of the THR 200 amino acid residue formed an H-bond with -NH at a distance of 2.15 Å. The His-94 residue formed a Pi-Pi stacking interaction with the aromatic ring of the ligand at a distance of 4.79 Å. The carbonyl oxygen of the backbone formed one metal coordination bond (Zn 600) at a distance of 1.98 Å (Figure S3: Supporting Information ). The compound 7b had a docking score of −5.64465 kcal/mol, compared to the reference, which had a docking score of −5.07 kcal/mol.

Docking score and molecular interaction analysis revealed that all synthesized compounds exhibit affinity and fit well within the binding pocket. Docking molecules in the protein binding site is a robust approach to elucidate the correct binding pose among several predicted compound poses. However, the approach cannot correctly rank the affinities of the small molecules to the target protein. To better rank the ligands and determine predictive binding energies, MM-GBSA calculations were performed.

After docking, all compounds show the best results compared to the reference compound. 6b has the best docking result (−6.49 kcal/mol) compared to other compounds.

3.5.6. MMGBSA (Molecular Mechanics/Generalized Born Surface Area)

MMGBSA, or molecular mechanics/generalized born surface area, is a computational method used to estimate the binding free energy of molecular interactions, such as those between a ligand and a receptor. MMGBSA is a valuable computational chemistry tool for assessing the binding free energies of molecular complexes, with applications ranging from drug discovery to protein engineering and beyond. The absolute value of free energy was considered in relation to several factors, including the total entropy of the system, gas-phase energy, and free energy of solvation obtained from molecular docking simulations. To understand the structural modifications that occur when the ligand and target protein bind, MMGBSA was performed to fully comprehend the energy consequences (). The values of dG Bind_ vdW, dG bind, dG Bind Covalent, and dG Bind Hbond were shown in Table S7 Supporting Information.

3.5.7. ADME Studies

The human radiolabeled absorption, distribution, metabolism, and excretion analysis provides a quantitative and comprehensive picture of the disposition of a drug, including excretion patterns and metabolite profiles in circulation and excreta. The data gathered from the ADME analysis are highly instructive for developing a uniform strategy for clinical pharmacology studies. The Swiss ADME tool of the Swiss Institute of Bioinformatics was accessed via a web server that displayed the submission page of Swiss ADME in Google and was used to predict the individual ADME behaviors of compounds. Swiss ADME is a tool designed to predict small-molecule pharmacokinetics, drug-likeness, and medicinal chemistry. Pharmacokinetic parameters include gastrointestinal absorption, blood–brain barrier permeation, P-gp substrate, CYP1A2 inhibitor, CYP1A2 inhibitor, CYP19 inhibitor, CYP2C9 inhibitor, CYP2D6 inhibitor, and CYP3A4 inhibitor, as well as Log Kp. The bioavailability radar (Figure S4a and S4b: Supporting Information) showed that the colored zone represents a suitable physicochemical space for oral bioavailability, considering the following properties: flexibility, lipophilicity, saturation, size, polarity, and solubility. The lipophilicity of the compound, Log p, can range from −0.31 to 3.68. The molecular weight can range from 176.12 to 434.51. The number of rotatable bonds was 2 to 5, the number of hydrogen bond donors was 2 to 4, and the number of hydrogen bond acceptors was 5 to 7. The topological polar surface area ranges from 107 to 132.89 Å. Our candidate molecules were relatively successful in adhering to Lipinski rules with zero violations, as shown in Table S8 Supporting Information. For each description, a physicochemical range is displayed as a pink band, inside which the molecules’ radar plot must fall to be categorized as drug-like. The use of radars allows a fast evaluation of drug-likeness guidelines. The red line of the molecule needs to be restricted entirely inside the pink region to be classified as drug-like; any aberration outside the pink region shows a negative physicochemical characteristic. The pharmacokinetic properties were analyzed using the boiled egg model (Figure S5, Supporting Information), which enables an intuitive evaluation of passive gastrointestinal absorption and brain penetration in relation to the compound’s position in the WLOGP versus TPSA.

The white region indicates a high probability of passive absorption by the gastrointestinal tract, and the yellow area indicates a high likelihood of brain penetration. The drug-likeness parameter is high, as it adheres to the Lipinski rule, with a bioavailability score of 0.55 (Tables S8 and 10, Supporting Information). Based on the above-predicted pharmacokinetic trend, these validated hit molecules were not excellent blood–brain barrier permeates and were also nonsubstrates of p-glycoprotein, as it is key to the efflux of the drug through biological membranes, therefore ruling out the CNS depressant drug, which was described by a binary descriptor of yes/no. The ADMET study was performed on the docked compounds that showed a good docking score with reference.

The boiled-egg analysis is used to estimate gastrointestinal absorption (HIA) and brain penetration (BBB). The white portion of the egg indicates highly passive gastrointestinal absorption, while the yolk represents high brain penetration. Additionally, red spots indicate the non-P-gp substrate PGP- and blue spots represent the actively refluxed P-gp (PGP+). Compounds 4b, 5b, and the reference showed a colored spot, which confirmed that these are not P-gp substrates, whereas all other molecules were found to be blue-colored spots, confirming their active efflux by P-gp (PGP+) (Figure S5: Supporting Information).

The ProTox-II, an online server, was used to interpret and analyze the results of toxicity classes and toxicity probabilities for hepatotoxicity, neurotoxicity, nephrotoxicity, respiratory toxicity, cardiotoxicity, carcinogenicity, immunotoxicity, mutagenicity, and cytotoxicity of seven compounds (Figure S6: Supporting Information). Five compounds were inactive for nephrotoxicity, cardiotoxicity, carcinogenicity, mutagenicity, and cytotoxicity, except for ascorbic acid, which was active for nephrotoxicity. Compound 6b was inactive in terms of hepatotoxicity, cardiotoxicity, carcinogenicity, mutagenicity, and cytotoxicity. All in all, the computational assessment reveals that the synthesized molecules possess good physicochemical characteristics that comply with the Lipinski rule and exhibit a favorable oral bioavailability profile, which supports their further biological characterization.

3.5.8. DFT Analysis of Reaction Mechanism Involved in Synthesis of Coumarin-Based Pyrimidine Co-Drugs

Calculations at the B3LYP/6–311G level in DFT gave profound information concerning the structure stability and the electronic properties of Product 6b, reactant 1 (coumarin), and reactant 2 (2-chloro-pyrimidine). Figure S7 Supporting Information showed the optimized geometries of reactant 1 (coumarin), reactant 2 (2-chloro-pyrimidine), and compound 6b at the B3LYP/6–311G level. The overall electronic energies of these structures were calculated as −611.54, −1298.58, and −1449.31 hartree, respectively, and thus, it can be confirmed that all the systems had the lowest energy states. Despite the dependence of total electronic energy on the size of the molecules, the extremely negative value of compound 6b denotes a constant arrangement and optimized geometry that can be apportioned for further analysis of the electronics. Dipole moments of reactants 1, 2, and 6b were 4.60 D, 3.90 D, and 5.17, respectively (Table ). The value of the dipole moment for corresponding reactants and products determines the direction of the reaction. This greater dipole moment of compound 6b implies a higher polarity of the molecule, which in turn makes the compound and polar residues in the active site of a biological receptor interact more strongly by electrostatic and hydrogen-bonding forces. This attribute plays a vital role in molecular recognition and adds good value to the docking and binding abilities of the compound. The frontier molecular orbitals (FMOs), as in Figure , displayed energies of HOMOs of reactants 1, 2, and compound 6b at energies of −0.26, −0.25, and −0.25 eV, respectively, and LUMO of reactants 1, 2, and compound 6b at −0.02361, −0.02928, and −0.02931 eV, respectively. The HOMO–LUMO energy gaps were found to be 0.23709, 0.22037, and 0.22109 eV, respectively. A smaller energy gap typically corresponds to a more reactive chemical and less kinetically stable, with ease of charge transfer and easy activation of electrons. In this regard, reactant 2 and compound 6b have lower energy gaps, indicating more reactivity than reactant 1. Further evidence of successful charge delocalization to facilitate the electron flow is the delocalization of HOMO and LUMO orbitals throughout the conjugated system of the coumarin-pyrimidine scaffold (Figure ), which might be beneficial to possible biological activity.

4. HOMO, LUMO, Energy Gap, and the Global Quantum Reactivity of Compound 6b and Its Reactants 1 and 2 Calculated at DFT/B3LYP/6-311G Level.

	Formula	reactant 1	reactant 2	6b compound
Dipole moment (Debay)		4.603362	3.904276	5.169703
Electronic energy (Hartree)		–611.534916	–1298.585396	–1449.314835
LUMO		–0.02361	–0.02931	–0.02928
HOMO		–0.26070	–0.25040	–0.24965
Energy gap (eV)	E HOMO – E LUMO	0.23709	0.22109	0.22037
Electron Affinity (A, eV)	A = -E LUMO	0.02361	0.02931	0.02928
Ionization Potential (I, eV)	I = -E HOMO	0.26070	0.25040	0.24965
Chemical potential (μ, eV)	μ = 1/2 (I + A)	0.1422	0.1399	0.1395
Electronegativity (χ, eV)	χ = −1/2 (I + A)	–0.1422	–0.1399	–0.1395
Chemical hardness (η, eV)	η = 1/2 (I – A)	0.1185	0.1105	0.1102
Chemical softness (S, eV– 1)	S = 1/η	8.437	9.049	9.075
Electrophilicity index (ω, eV)	ω = 2(μ2/η)	0.341	0.355	0.354
Neucleophilicity index (N, eV– 1)	N = 1/ω	2.93	2.82	2.83
Additional electronic charge	= −μ/η	–1.20	–1.27	–1.27

12.

HOMO, LUMO, and energy gap of compound 6b and its reactant 1 and 2.

The descriptors of global reactivity based on the FMO energies give a more in-depth understanding of the electronic behavior of the molecules. The electron affinity (A) was between 0.02361 and 0.02931 eV, meaning that all the molecules have moderate electron-accepting ability, and compound 6b had the highest one. I values were between 0.24965 to 0.26070 eV, with the lower I of compound 6b suggesting that it is the least energetically demanding to remove an electron, and as such, it was more reactively active. The values of chemical potential were found to be 0.142 eV for reactant 1 and around 0.140 eV for reactant 2 and compound 6b, indicating a similar propensity toward electron exchange.

Chemical hardness (η), which is the measure of resistance to charge transfer, was determined as 0.1185, 0.1102, and 0.1105 eV of reactant 1, reactant 2, and compound 6b, respectively. The lower η values of reactant 2 and compound 6b show that they are softer and more active, which is quite expected based on the smaller energy differences. In line with this, the values of chemical softness (S) were 8.437, 9.075, and 9.049 eV^–1, which indicates that compound 6b and reactant 2 were more chemically flexible. The values of the electrophilicity index (0.341, 0.354, and 0.355 eV) indicate that compound 6b has the highest electrophilic nature and can accommodate electron density of nucleophilic residues in a biological target to a greater extent. Nucleophilicity Index (N) is negatively associated with ω, and the values are slightly less, in the case of compound 6b, which is characteristic of good electrophilic ligands. The extra electron charge transfer (ΔN) values of −1.20, −1.27, and −1.27 also demonstrate that the charge-donating potentials of compound 6b and reactant 2 are similar and better than that of reactant 1.

Evidence of electron density distribution can be visually represented, as shown in Figure in the form of molecular electrostatic potential (MEP) maps. Red color spots around oxygen and nitrogen atoms are associated with the high density of electrons, which are the possible sites of electrophilic attack or the formation of hydrogen bonds. The blue areas are electropositive areas, usually related to hydrogen atoms. Of the three molecules, compound 6b has the largest electrostatic potential differentiation, which proves its active involvement in polar interactions with the environment of the receptors.

13.

MEP structure and scale of compound 6b and its reactants 1 and 2.

The general finding of the DFT study is that compound 6b has the best electronic profile of the three molecules studied. Its large dipole moment, low HOMO–LUMO gap, low hardness, and high electrophilicity are all signs of good reactivity and a high propensity to form stable interactions with biological targets. Although reactant 2 exhibits the same level of electronic softness, compound 6b is more promising as a binding and inhibitory agent due to its higher level of polarity and electrophilic strength. These theoretical findings align with the molecular docking findings, in which compound 6b was the compound with the highest binding affinity to the chosen protein target, therefore confirming the electronic interpretations of the DFT.

4. Conclusion

This study revealed the design and synthesis of heterocyclic compounds containing coumarin as a scaffold. Spectroscopy analysis was utilized to confirm the hypothesized product structures. After analysis, all synthesized compounds were evaluated for their antioxidant activity using the DPPH method. The IC₅₀ value was obtained through a DPPH assay, and the IC₅₀ values were validated using a 3D QSAR ligand-based design protocol along with the generation of pharmacophore screening models. For further validation of the inhibitory potential of these compounds, a structure-based drug design protocol was evaluated through molecular docking using the 1OQ5 protein. Based on the careful evaluation of the results of molecular docking studies, antioxidant screening, and H₂O₂ assay results, we conclude that the most potent compound 6b was found to show a docking score of −6.49 kcal/mol and an IC₅₀ of 12.96 μM, indicating that wet lab results endorsed the results of dry lab. Compound 4b was found to be the third most potent antioxidant compound (docking score −6.33 kcal/mol, IC₅₀ 13.82 μM) compared with the standard antioxidant, ascorbic acid. Compounds 3b and 5b also showed the best docking results compared to the standard or reference compound. Furthermore, the compounds were calculated in ADMET studies to determine pharmacological, drug-like, and physicochemical properties. Protox studies provided insight into toxicity within the biological system, which represents one more step in the optimization of lead compounds in the future. Therefore, this study demonstrates that the reported coumarin-pyrimidine hybrid molecules are potential inhibitors in the DPPH and H₂O₂ assays, paving the way for the synthesis of additional compounds based on the reported scaffold, which could eventually lead to the development of efficient bioactive compounds that play a vital role in the DPPH and H₂O₂ assays. The latest study will also motivate medicinal chemists to discover new, more effective antioxidant molecules among these structures.

All data files are available as Supporting Information.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.5c12395.

• Two-way ANOVA table (DPPH); two-way ANOVA table (H₂O₂)’ Generated Pharmacophore Models with Bond Angle; Experimental and predicted IC₅₀ of synthesized compounds; Generated Pharmacophore Models with Bond Angle; Generated Pharmacophore Models with Bond Distance; Generated Pharmacophore Models with Scoring Parameters; Field-based 3D- QSAR modeling parameters; MMGBSA of docked molecules in complex with 1OQ5; Drug-Likeness Properties of Best Docked Compounds; Structure of synthesized molecules (1b–7b); Comparison between predicted and experimental IC₅₀ values; 2D interaction of 7b with 1OQ5; 3D interaction of 7b with 1OQ5; Swiss ADMET radar pictures of the molecules (a visual tool used in drug development and discovery, the bioavailability radar permits researchers to quickly determine a compound’s bioavailability and drug-likeness); Swiss ADMET radar pictures of 6b and 7b compounds with reference compound; Boiled-egg results of lead optimized, Toxicity Radar of Lead Compound, Network Chart of Active and Inactive Probability; Optimized structures of compound 6b and its reactants 1 and 2; and ¹H NMR spectra of Compounds (1b, 2b, 3b, 4b, 5b, 6b); ¹³C NMR spectra of Compounds (1b, 2b, 3b, 4b, 5b, 6b) (PDF)

N.N.: Library preparation, Drafting, Molecular Docking, ADMET; S.P.: Supervision, editing, Evaluation of results; N.S.: Conceptualization, Paper Drafting, write-up, data analysis, Literature and Funding; Z.-i.-H.N.: Helped reviewing and data analysis

Funding to support Study was provided by the Pakistan Science Foundation under Grant CRP/PSF/TH-22.

Not applicable, as no human or animal trials were involved.

The authors declare no competing financial interest.