In Silico Molecular Docking And ADMET Evaluation Of Coumarin Derivatives As Potential Therapeutic Lead Compounds

Organic chemical compounds with a ring-like structure that contain one or more heteroatoms are referred to as heterocyclic compounds, or heterocycles. Both cyclic and acyclic heterocycles are possible. Although the overall structure of heterocycles is similar to that of cyclic organic compounds, which only contain one carbon atom, heterocycles differ from all carbon ring analogs in their physico-chemical characteristics due to the substitution of one or more carbon atoms with heteroatoms. Heterocycles are used in many different fields, such as veterinary, pharmaceutical, and agrochemical. These substances are also utilized in copolymers, corrosion inhibitors, antioxidants, and sanitizers. More than 85% of all physiologically active chemical compounds contain heterocycles, according to research. Pharmacological action is seen by all heterocycles, both natural and manufactured. Because of their diverse structures and adaptable characteristics, heterocyclic molecules are essential in medical chemistry. They are essential in the design and development of drugs because the addition of heteroatoms like nitrogen, oxygen, or sulfurs to their ring topologies increases their interaction with biological targets. Examples of heterocyclic compounds with unique features include pyridine, pyrrole, furan, and thiophene. Each of these compounds makes a unique contribution to the creation and manufacturing of pharmaceuticals.

Compound	Name	Mol. Wt. (gm/mol)	2D Structure
1	7H-furo[3,2-g]-2H-chromen-7-one	186.16
2	4-phenyl-2H-chromen-2-one	222.24
3	7-methoxy-8-(3-methylbut-2-enyl)-2H-chromen-2-one	244.28
4	7-hydroxy-6-methoxy-2H-chromen-2-one	192.17
5	4-hydroxy-3-(1-phenylpropyl)-2H-chromen-2-one	280.3
6	3-(2-oxo-2H-chromen-4-yl)-2H-chromen-2-one	290.3
7	8-methoxy-4-methyl-2h-benzo[g]-2H-chromen-2-one	240.25
8	6,7-dihydroxy-4-methyl-2H-benzopyran-2-one	192.17
9	7-methoxy-2H-chromen-2-one	176.17
10	2,5,9-trimethylfuro[3,2-g]-2H-chromen-7-one	228.24
11	7-methoxychromen-2-one	176.17
12	9-methoxy-7H-furo[3,2-g]chromen-7-one	216.19
13	3,4-Dimethyl-7methoxychromen-2-one	228.24
14	8,8-dimethyl-2H,8H-benzol[1,2-b:3,4-b]dipyran-2-one	228.24
15	6-Hydroxy-5,7-dimethoxy coumarin	222.19
16	4-hydroxy-3-(3-oxo-1-phenylbuty l)-2H-chromen-2-one	308.33
17	7-hydroxy-4-methylcoumarin	176.17
18	5-hydroxy-3,4-dimethyl-2H-chromen-2-one	216.19
19	4-methoxy-7methyl-5H-furo[3,2-g]chromen-5-one	267.08
20	4,9-dimethoxy-7H-furo[3,2-g] chromen-7-one	186.16
21	2-phenyl-4H-chromen-4-one	228.24
22	6-(dimethylamino)-8-methoxy-10 H-chromen[5,4,3]isoqunoline-10-one	206.19
23	7-H-furo (3,2, -9) chromen-7-one	222.19
24	9-(isopentyloxy)-7H-furo[3,2-g] chromen-7-one	290.31
25	8-hydroxy-6,7-dimethoxychromen-2-one	222.19

Table 1: Information about ligands

2. MATERIALS AND METHODS

Molecular Docking: To assess the possible binding behavior, all of the compounds undergo molecular docking using the Auto Dock 4.2 docking tool. For docking studies, the X-ray crystal structure of 1,3,4-oxadiazole (PDBID: 1OJA) was made available. ChemDraw Ultra 7.0 was first used to sketch the 2D structures of the ligands. Chem3D Ultra 7.0 was then used to transform these structures into three-dimensional arrangements. The PyMol program was used to save each structure in an a.pdb file. In order to utilize the structures with Auto Dock Vina 4.2, they were saved as PDBQT files. Docking simulations using AutoDock Vina produced nine distinct ligand-receptor conformations. The Discovery Studio Visualizer was used to analyze the final conformations.

Target Selection – The protein (target) related to the disease is selected. Target selection is the first step in molecular docking studies, where a suitable biological target protein related to the disease is chosen. The target is selected based on its role in disease progression and the availability of its 3D crystal structure. A well-characterized target with a known active site helps in accurately predicting ligand–protein interactions.

Target Preparation – The protein structure is cleaned by removing water molecules, adding hydrogens, and optimizing it. Before docking, the protein structure is optimized by adding hydrogen atoms, removing water molecules, and correcting atomic clashes. The 3D crystal structure of protein (PDB ID: 1OJA) was obtained from the Protein Data Bank (RCSB).

Ligand Selection – Small molecules (e.g., coumarin derivatives) are chosen for docking. Ligand selection involves choosing small molecules or compounds that are expected to interact with the selected target protein. The ligands are selected based on their biological activity, chemical structure, and drug-like properties. These molecules are then prepared and used in docking studies to evaluate their binding affinity with the target.

Ligand Preparation – Ligands are energy minimized and optimized for proper geometry. Mol file formats were used as input for drug structures in computational studies. Therefore, the canonical SMILES of each drug were manually obtained from the PubChem database to generate the corresponding mol files. PubChem provides detailed information on drugs, including pharmacological data, clinical trial status, molecular weight, molecular formula, and both 2D and 3D structures.

Docking – Prepared ligands are placed into the active site of the target protein to predict binding. Docking is a computational technique used to predict how a ligand binds to the active site of a target protein. It helps in understanding the binding mode, binding affinity, and interactions between the ligand and protein. Docking results are analyzed to identify the most stable and effective ligand–protein complexes.

Evaluating Docking Results – Docking scores, binding energies, and interactions (H-bonds, hydrophobic, π–π) are analyzed to identify best hit compounds.

Pharmacokinetic Drug Likeliness: The ADMET properties of substances are linked to pharmacokinetic processes in the human body. Recently, computer-based drug development has shown increased interest in ADMET prediction by computational techniques studies. ADMET analyses are carried out to determine pharmacological structure from the standpoint of drug discovery. The SwissADME module of the SIB web server, accessible at https://www.sib.swiss, was used to examine the synthesized compounds for pharmacokinetic features, such as drug similarity, partition coefficient, solubility, and several other metrics. Each of the substances has substantial absorption in the gastrointestinal system. Every substance showed the ability to pass through the blood-brain barrier (BBB).

TPSA, drug likeness, and pharmacokinetic properties: The total number of hydrogen bonds, comprising both donors and accepters are significant indicators of this characteristic. Log P for the partition coefficient and Molecular weight, and hydrogen bonds in the molecule are a few examples of these descriptors. Rule of Five by Lipinski illustrates a substance's oral bioavailability by indicating the degree of permeability or absorption in lipid bilayers found within the human body. An additional characteristic that can be used to assess a drug's permeability is its ability to form hydrogen bonds. When a molecule has more than ten Hydrogen bond acceptors, five Hydrogen bond donors, the likelihood of poor penetration or absorption increases.
(TPSA) has been used to describe drug consumption, including abdomen consumption, bioavailability, CaCO2 permeability, blood brain barrier permeability.

Boiled EGG PLOT analysis: ADMET, toxicity, efficacy, limited bioavailability, and pharmacokinetics are further repercussions of drug development issues. Investigations show that a high BBB crossover is possible when synthesized derivative inclining occurs inside the yellow loop. Compounds in the white "egg white" area are predicted to have high GI absorption, while those in the yellow "yolk" area are likely to cross the BBB.

Table 2: Lipinski factors for drug similarity qualities of coumarin derivatives, absorption distribution, and metabolism elimination characteristics.

Table 3: Pharmacokinetic Prediction of coumarin derivatives using SWISS ADME.

Figure 1: Boiled Egg Plot of most effective Virtual Screened .

Figure 2: Selegiline bioavailability radar is displayed for the following compounds: LIPO (lipophilicity as XLOGP3), SIZE (size as molecular weight), POLAR (polarity as TPSA), INSOLU (insolubility in water by log S scale), INSATU (in saturation as per fraction of carbons in the sp3 hybridization), and FLEX (flexibility as per rotatable bond).

Figure 3: An example of the Swiss Target Prediction Report of Compound Selegiline.

Figure 4: Ramachandran Plot of Protein Molecule (4BM9)

Figure 5: PROCHECK statistics of Protein Molecule (4BM9)

Figure 5: 3D Structure of 4BM9

Figure 6: 3D interaction of protein (4BM9) with ligand

Figure 7: 3D visualization of standard Selegiline by using Discovery studio.

Figure 12: This image shows protein–ligand interaction visualization after molecular docking (amino-acid residues labeled around a bound ligand).

RESULT

Molecular Docking study was performed on Twenty five  derivatives of Coumarin