In-Silico Evaluation of Flavone Derivatives for Cardioprotective Effects: A Comparative Molecular Docking Approach

Sneha Nandeshwar , Sapan Shah, Rida Saiyad, Nikita Gaikwad , Pooja Wankhade,

doi:10.5281/zenodo.15087478

Research Paper | Open Access
Volume 03 | Issue 03 | Article Id IJPS/250303183

In-Silico Evaluation of Flavone Derivatives for Cardioprotective Effects: A Comparative Molecular Docking Approach
Sneha Nandeshwar * Sapan Shah Rida Saiyad Nikita Gaikwad Pooja Wankhade
Department of Pharmaceutical Chemistry, Priyadarshini JL College of Pharmacy, Nagpur, 440016.

Abstract

Background: In this study, we used molecular docking to explore the binding affinity, ADME, and toxicity of flavone derivatives on several receptors associated with cardioprotective action. The binding affinity of several flavone derivatives to various receptors involved in cardioprotective action was determined. Auto Dock Vina, PyMol, Discovery Studio, AutoDock Tools, ChemSketch, Swiss ADME, and PROTOX 3.0. Methods: Molecular docking. Results: The binding results of the selected plant compounds and target proteins, namely 1o86, 7Q29, 5JMY, 4DLI, 2YCW, and 1CX2, showed that the good binding affinity and good receptor binding mode selected target. However, among all protein ? 1 adrenergic receptor (ID:- 2YCW) showed lowest binding affinity with compound D10 (2-(4-tert-butylphenyl)-4H-1-benzopyran-4-one) (binding energy – 11.0 Kcal/mol), D44 (binding energy – 10.6 Kcal/mol). D39 (binding energy – 10.4 Kcal/mol), D32, D35 & D42 (binding energy – 10.2 Kcal/mol). Conclusions: The current study attempted to computationally find chemicals that can bind to the numerous targets of cardiovascular disease. The docking scores and interaction analysis indicate that most drugs have the ability to bind to many targets involved in cardiovascular illness. However, the ? 1 adrenergic receptor has a high binding affinity. Absorption, distribution, metabolism, excretion, and toxicity, as well as toxicity prediction, revealed several chemicals that could be employed as possible candidates against cardiovascular disease.

Keywords

ADME, Binding energy, Cardioprotective agents, Docking, Toxicity, Flavones.

Introduction

Cardiovascular diseases (CVDs) are the leading cause of death globally.¹ Approximately 10 million fatalities occur annually, and the figure will climb to 23.6 million by 2030.² The leading cause of these deaths is ischemic heart disease (IHD), which claimed over 9 million lives in 2016. IHD remains the leading cause of death in countries of all income levels. Rates vary per country and are declining in the majority of them, indicating a lot of potential for growth. Future progress may be hampered by rising hypertension in some developing countries, as well as global obesity rates.³ IHD has a greater death rate than other CVDs, such as stroke, hypertension, and other cardiovascular illnesses. People who follow a high saturated fatty acid, high dietary energy density, and high sodium diet are substantially more likely to develop coronary heart disease than those who do not. Cardiovascular disorders (CVDs), which include coronary heart disease (CHD), cerebrovascular disease or accident (CVA), peripheral arterial disease, and rheumatic disease, are recognized as the leading cause of death worldwide. Rising trends in CVD prevalence and deaths, particularly CHD, have focused global attention on disease prevention and control.⁴ Low-middle-income countries, such as Pakistan, India, Bangladesh, Nepal, and Sri Lanka, have a higher risk of coronary heart disease (CHD) than other countries.⁵ Obesity, physical inactivity, smoking, and hereditary predisposition all contribute to the development of cardiovascular problems.⁶ Flavone derivatives (2-phenylchromone) are naturally occurring heterocyclic compounds belonging to the flavonoid category⁷. They are abundant in many naturally occurring products and represent a significant group of oxygen heterocycles, which are widely distributed in the plant kingdom as secondary metabolites⁸. Flavonoids are a broad category of around 4000 polyphenolic chemicals found in plant-derived foods. The fundamental basis for systematic classification of these compounds is the saturation level and aperture of the major pyran ring, which results in the synthesis of flavones, flavanols, flavonols, isoflavones, and flavanonols⁹. Molecular docking is an important approach for understanding flavone derivatives' cardioprotective effect since it offers insight on their binding interactions with specific biological targets10. This computational method enables researchers to predict the affinity and orientation of flavonoids when they interact with proteins crucial in cardiovascular health, allowing the identification of novel therapeutic agents. This knowledge contributes in the development of treatment methods for cardiovascular disorders by improving flavone derivatives' cardioprotective properties.11 These interactions frequently entail the creation of hydrogen bonds and hydrophobic contacts, which are critical for the stability of the enzyme-ligand complex and improve the cardioprotective properties of these molecules¹². We used molecular docking methods to investigate the interaction between flavones and cardiovascular targets. As a result, we conducted an in silico, ADME, toxicity analysis docking investigation of cardiovascular disease targets using flavones derivatives.

2. MATERIALS AND METHODS

2.1 Platform for molecular docking

The docking study of all the flavone derivatives selected as ligand and with target proteins was perform using AutoDock Vina software¹³.

2.2 Ligand preparation

UCSF chimera software retrieves the 3D structure of all compounds from the PubChem database¹⁰(https://pubchem.ncbi.nlm.nih.gov/). The Gasteiger charges and rotatable bonds were then allocated to the PDB ligands by Auto Dock Tool 1.5.6¹¹. The compound structures were evaluated for docking studies after minimizing their energy. (Table 1).

2.3 Protein preparation

Based on a review of the literature, a total of six proteins linked to various cardiovascular conditions were chosen (Table 2). The RCSB protein data bank (http://www.pdb.org) provided the 3D structures of a few chosen target proteins. Co-crystallized ligands (X-ray ligands) were present in the binding site of every protein. Each protein structure's ligands were extracted from the binding site and stored in a different file. Using the docking program AutoDock Vina, automated molecular docking was carried out to determine molecular interaction and optimum geometry¹⁴.

2.4 ADMET and toxicity prediction

The absorption, distribution, metabolism, excretion, and toxicity (ADMET) screening of ligands contributes in determining their absorption properties, toxicity, and drug-like nature. Ligand compounds were saved in smiles format and specific drugs were submitted to SWISSADME. SWISSADME is a web-based tool for predicting ADME and pharmacokinetic features of molecules. The anticipated outcome includes lipophilicity, water solubility, physicochemical characteristics, pharmacokinetics, drug-likeness, medicinal chemistry, and brain or intestinal estimated¹⁵. Toxicity classes include Category I (compounds with LD50 values ≤50 mg/kg), Category II (compounds with LD50 values >50 mg/kg), and Category III (somewhat toxic) (compounds with LD50 values 500-5000 mg/kg), which are included in Category IV¹⁶^,¹⁷. PROTOX is a rodent oral toxicity server that predicts the LD50 value and toxicity class of the query substance¹⁸.

2.5 Drug likeness calculations

Drug scans were conducted to establish whether the compounds fitted the drug-likeness criteria. Lipinski's filters were used with Molinspiration (http://www.molinspiration.com) to examine drug likeness attributes such as the number of hydrogen acceptors (no more than 10), the number of hydrogen donors (no more than 5), the molecular weight (more than 500 daltons), and the partition coefficient log P (no less than 5). The smiles format for each compounds was uploaded for examination.

Table 1: Major flavone derivatives for docking studies.

Sr no.	IUPAC NAME	Molecular Formula	PubChem ID
D1	2-(3-nitrophenyl)-4H-1-benzopyran-4-one	C₁₅H₉NO₄	252248
D2	2-(3-hydroxyphenyl)-4H-1-benzopyran-4-one	C₁₅H₁₀O₃	229015
D3	2-(4-methylphenyl)-4H-1-benzopyran-4-one	C₁₆H₁₂O₂	688829
D4	2-(4-methoxyphenyl)-4H-1-benzopyran-4-one	C₁₆H₁₂O₃	77793
D5	2-(4-bromophenyl)-4H-1-benzopyran-4-one	C₁₅H₉BrO₂	1686
D6	2-(4-aminophenyl)-4H-1-benzopyran-4-one	C₁₅H₁₁NO₂	177048
D7	2-(4-hydroxyphenyl)-4H-1-benzopyran-4-one	C₁₅H₁₀O₃	229016
D8	2-(2-hydroxyphenyl)-4H-1-benzopyran-4-one	C₁₅H₁₀O₃	161860
D9	2-(3-aminophenyl)-4H-1-benzopyran-4-one	C₁₅H₁₁NO₂	69569538
D10	2-(4-tert-butylphenyl)-4H-1-benzopyran-4-one	C₁₉H₁₈O₂	776409
D11	2-(3-bromophenyl)-4H-1-benzopyran-4-one	C₁₅H₉BrO₂	493373
D12	4-(4-oxo-4H-1-benzopyran-2-yl) benzoic acid	C₁₆H₁₀O₄	5272796
D13	2-[3,6-(di-propan-2-yl)4-hydroxy-phenyl]-4H-1-benzopyran-4-one	C₂₃H₂₆O_s
D14	2-(3,4,5-trimethoxyphenyl)-4H-1-benzopyran-4-one	C₁₈H₁₆O₅	332208
D15	6-tert-butyl-2-phenyl-4H-1-benzopyran-4-one	C₁₉H₁₈O₂	91872361
D16	2-(4-chlorophenyl)-4H-1-benzopyran-4-one	C₁₅H₉ClO₂	151515
D17	2-(3,4-dimethoxyphenyl)-4H-1-benzopyran-4-one	C₁₇H₁₄O₄	688674
D18	2-(4-fluorophenyl)-4H-1-benzopyran-4-one	C₁₅H₉FO₂	261391
D19	6-fluoro-2-phenyl-4H-1-benzopyran-4-one	C₁₅H₉FO₂	261395
D20	6-chloro-2-phenyl-4H-1-benzopyran-4-one	C₁₅H₉ClO₂	248021
D21	6-methyl-2-phenyl-4H-1-benzopyran-4-one	C₁₆H₁₂O₂	689013
D22	5-hydroxy-2-(3,4,5-trimethoxyphenyl)-4H-1-benzopyran-4-one	C₁₈H₁₆O₆	13302209
D23	2-(3,4-dimethoxyphenyl)-5-hydroxy-4H-1-benzopyran-4-one	C₁₇H₁₄O₅	21721891
D24	2-(4-chlorophenyl)-5-hydroxy-4H-1-benzopyran-4-one	C₁₅H₉ClO₃	15308132
D25	2-(4-iodophenyl)-4H-1-benzopyran-4-one	C₁₅H₉IO₂	466287
D26	2-(2-fluorophenyl)-4H-1-benzopyran-4-one	C₁₅H₉FO₂	261400
D27	2-(4-nitrophenyl)-4H-1-benzopyran-4-one	C₁₅H₉NO₄	622510
D28	2-(2-hydroxyphenyl)-4H-1-benzopyran-4-one	C₁₅H₁₀O₃	161860
D29	2-(2-methylphenyl)-4H-1-benzopyran-4-one	C₁₆H₁₂O₂	776385
D30	2-(2-nitrophenyl)-4H-1-benzopyran-4-one	C₁₅H₉NO₄	775978
D31	2-(2-ethylphenyl)-4H-1-benzopyran-4-one	C₁₇H₁₄O₂	66553950
D32	2-[3-(propan-2-yl) phenyl]-4H-1-benzopyran-4-one	C₁₈H₁₆O₂
D33	2-(2-aminophenyl)-4H-1-benzopyran-4-one	C₁₅H₁₁NO₂	12481696
D34	5-hydroxy-2-(4-hydroxyphenyl)-4H-1-benzopyran-4-one	C₁₅H₁₀O₄	165521
D35	5-hydroxy-2-(2-methylphenyl)-4H-1-benzopyran-4-one	C₁₆H₁₂O₃	16060407
D36	5-hydroxy-2-(4-methoxyphenyl)-4H-1-benzopyran-4-one	C₁₆H₁₂O₄	276147
D37	5-hydroxy-2-(4-nitrophenyl)-4H-1-benzopyran-4-one	C₁₅H₉NO₅	11098040
D38	5-hydroxy-2-(3,4,5-trimethoxyphenyl)-4H-1-benzopyran-4-one	C₁₈H₁₆O₆	13302209
D39	2-(2-ethylphenyl)-5-hydroxy-4H-1-benzopyran-4-one	C₁₇H₁₄O₃
D40	6-chloro-2-(4-methoxyphenyl)-4H-1-benzopyran-4-one	C₁₆H₁₁ClO₃	678256
D41	6-chloro-2-(4-nitrophenyl)-4H-1-benzopyran-4-one	C₁₅H₈ClNO₄	5011227
D42	5-hydroxy-2-(4-methylphenyl)-4H-1-benzopyran-4-one	C₁₆H₁₂O₃	11779973
D43	5-hydroxy-2-(2-hydroxyphenyl)-4H-1-benzopyran-4-one	C₁₅H₁₀O₄	688660
D44	5-hydroxy-2-[3-(propan-2-yl) phenyl]-4H-1-benzopyran-4-one	C₁₈H₁₆O₃
D45	2-(4-aminophenyl)-5-hydroxy-4H-1-benzopyran-4-one	C₁₅H₁₁NO₃	46873260
D46	2-(4-aminophenyl)-6-chloro-4H-1-benzopyran-4-one	C₁₅H₁₀ClNO₂	82046480
D47	6-chloro-2-[3-(propan-2-yl) phenyl]-4H-1-benzopyran-4-one	C₁₈H₁₅ClO₂
D48	2-(2-bromophenyl)-6-chloro-4H-1-benzopyran-4-one	C₁₅H₈BrClO₂	688748
D49	6-chloro-2-(2-methoxyphenyl)-4H-1-benzopyran-4-one	C₁₆H₁₁ClO₃	930715
D50	6-chloro-2-(2-chlorophenyl)-4H-1-benzopyran-4-one	C₁₅H₈Cl₂O₂	688872

Table 2: Targeted receptor proteins associated with cardiovascular disease Results

Sr no.	Target Proteins	Disease	PDB ID
1	Angiotensin-converting enzyme	Atherosclerosis/coronary artery diseases	1o86
2	ACE/NEP inhibitor	Atherosclerosis/coronary artery diseases	7Q29
3	MAPK	Myocardial infarction	4DLI
4	Neprilysin	Coronary artery disease	5JMY
5	β?1?adrenergic receptor	Chronic heart failure	2YCW
6	Cox-2	Myocardial infarction, pain	1CX2

Docking results

The binding results of the selected compounds and target proteins, namely 1o86, 7Q29, 5JMY, 4DLI, 2YCW, and 1CX2, revealed that the good binding affinity and receptor binding mode selected target. However, among all derivatives, D10 (2-(4-tert-butylphenyl)-4H-1-benzopyran-4-one) has the lowest binding energy with β?1?adrenergic receptor (binding energy – 11.0 Kcal/mol), D47 with ACE and neutral endopeptidase inhibitors (binding energy – 9.4 Kcal/mol), D47 with ACE has lowest binding energy (binding energy – 9.2 Kcal/mol), for cox-2 the lowest binding energy with D47 derivative (binding energy – 10.5 Kcal/mol), D32 has lowest binding energy with receptor MAPK (binding energy – 10.4 Kcal/mol), D11 has lowest binding energy with protein neprilysin (binding energy – 9.4 Kcal/mol). All binding affinities of flavone derivatives are shown in Table 3.

Table 3: Binding energy of flavone derivatives against targeted protein receptors

Derivative code	Cox-2 (1CX2)	ACE/NEP, ID – 7Q29	ACE (1o86)	β?1?adrenergic receptor ID:- 2YCW	MAPK ID:- 4DLI	Neprilysin ID:- 5JMY
D1	-9.4	-9.1	-8.4	-9.8	-8.5	-8.3
D2	-8.9	-8.8	-8.3	-9.6	-9.9	-9.0
D3	-10	-9.0	-8.3	-10.0	-8.6	-7.9
D4	-8.9	-8.5	-8.2	-9.4	-9.2	-8.0
D5	-9.1	-8.9	-8.1	-9.7	-8.7	-7.8
D6	-9.3	-8.5	-8.1	-9.6	-9.1	-7.7
D7	-9.6	-8.5	-8.0	-9.7	-9.2	-8.1
D8	-8.9	-8.5	-8.2	-9.3	-9.7	-8.9
D9	-9.0	-8.8	-8.3	-9.6	-9.6	-9.0
D10	-9.8	-8.9	-8.5	-11.0	-8.7	-8.3
D11	-9.4	-8.7	-8.1	-9.6	-9.6	-9.4
D12	-9.2	-8.5	-8.8	-9.9	-9.1	-8.5
D13	-9.3	-9.0	-8.5	-8.8	-9.2	-8.2
D14	-8.8	-8.5	-7.9	-9.0	-8.1	-7.5
D15	-9.4	-9.0	-8.2	-10	-9.1	-8.1
D16	9.0	-8.8	-8.1	-9.7	-9.1	-7.8
D17	-8.9	-8.6	-8.0	-9.3	-9.0	-8.3
D18	-9.0	-8.7	-8.0	-9.6	-9.6	-8.8
D19	-8.2	-8.4	-8.0	-9.3	-9.3	-8.0
D20	-8.7	-8.3	-7.8	-9.4	-8.2	-8.0
D21	-10.3	-8.4	-8.0	-9.7	-8.2	-8.8
D22	-9.2	-8.3	-8.1	-8.8	-7.6	-8.6
D23	-9.1	-8.2	-8.4	-9.7	-7.7	-7.8
D24	-8.9	-8.5	-8.2	-10.0	-9.6	-7.7
D25	-8.9	-8.9	-7.8	-9.7	-8.5	-7.8
D26	-9.1	-8.8	-7.7	-9.6	-9.6	-8.3
D27	-9.1	-8.9	-8.7	-9.8	-8.2	-8.3
D28	-8.9	-8.5	-8.2	-9.3	-9.7	-8.9
D29	-8.5	-8.9	-8.4	-9.8	-9.8	-7.5
D30	-8.7	-7.9	-8.1	-9.8	-9.6	-7.5
D31	-8.3	-8.9	-7.7	-10.1	-10.0	-7.5
D32	-9.0	-9.3	-9.0	-10.3	-10.4	-8.1
D33	-9.4	-8.6	-8.0	-9.3	-9.2	-8.6
D34	-8.7	-8.2	-8.1	-9.7	-9.6	-8.3
D35	-9.2	-8.6	-8.1	-10.3	-10.1	-7.4
D36	-9.0	-8.1	-8.3	-9.8	-9.7	-7.9
D37	-9.4	-8.8	-8.6	-10.2	-9.3	-8.0
D38	-9.1	-8.3	-8.1	-8.8	-9.3	-8.6
D39	-9.1	-8.6	-8.3	-10.4	-8.0	-9.1
D40	-8.6	-8.2	-7.9	-9.0	-8.4	-7.2
D41	-9.2	-9.0	-8.5	-8.6	-7.6	-7.9
D42	-9.0	-8.6	-8.3	-10.3	-9.6	-8.1
D43	-8.9	-8.2	-8.1	-9.6	-10.1	-8.8
D44	-9.8	-9.2	-8.7	-10.6	-10.3	-8.4
D45	-8.8	-8.2	-8.0	-9.7	-9.5	-7.9
D46	-9.0	-8.2	-8.0	-8.6	-8.3	-7.4
D47	-10.5	-9.4	-9.2	-10.0	-8.5	-8.5
D48	-8.0	-8.4	-7.7	-9.0	-7.9	-8.0
D49	-9.3	-8.3	-7.9	-8.8	-7.3	-7.7
D50	-8.8	-8.2	-8.2	-8.8	-8.9	-8.0
Rofecoxib	-8.1
Omapatrilat		-9.6
Lisinopril			-7.6
Carazolol				-9.6
losmapimod					-8.7

Figure1: COX?2 receptor docked with D47

Figure 3: ACE receptor docked with D47

Figure 4: β?1?adrenergic receptor docked with D10

Figure 5: MAPK receptor docked with D37

Figure 6: Neprilysin receptor docked with D11

Toxicity and ADMET prediction

The toxicity of compounds was evaluated using the Protox 3.0 software. The server admet generates pharmacokinetic characteristics of substances using various criteria: Absorption, distribution, metabolism, and excretion¹⁹. The results of admet analysis and toxicity prediction have been shown in table 4. Except for D1 (600 mg/kg), all examined compounds had greater LD50 values, indicating that they are non-toxic. Except for D6, D9, D27, D30, D33, D45, and D46, all of the chemicals chosen have no hepatotoxic properties. Except for D2, D3, D7, D21, D24, D32, D42, and D44, all of the derivatives have no cardiotoxic activity. As a result, based on ADMET and toxicity analysis, some derivatives meet all of the mentioned requirements, and we may indicate that they are prospective candidates for the development of a better cardiovascular disease treatment.

Drug-likeness prediction

The Drug-likeness filters help in the early preclinical development by avoiding costly late step preclinical and clinical failure. The drug-likeness properties of molecules were analyzed based on Lipinski rule of 5(Table 5). All the selected compounds satisfied Lipinski’s rule of five.

Table 4: ADMET prediction of selected derivatives used through swiss ADME and Protox-3.0 software

Compounds	LD50, (mg/kg)	Hepatotoxicity	Cardiotoxicity	Cytotoxicity	Carcinogens
D1	600 (class 4)	inactive	inactive	inactive	inactive
D2	4000 (class 5)	inactive	active	active	active
D3	2500 (class 5)	inactive	active	active	active
D4	4000 (class 5)	inactive	inactive	active	active
D5	2500 (class 5)	inactive	inactive	active	active
D6	2500 (class 5)	active	inactive	inactive	active
D7	2500 (class 5)	inactive	active	active	active
D8	4000 (class 5)	inactive	inactive	inactive	active
D9	2500 (class 5)	active	inactive	inactive	active
D10	2500 (class 5)	inactive	inactive	inactive	active
D11	2500 (class 5)	inactive	inactive	inactive	active
D12	2500 (class 5)	inactive	inactive	inactive	active
D13	2500 (class 5)	inactive	inactive	inactive	inactive
D14	4000 (class 5)	inactive	inactive	inactive	inactive
D15	2500 (class 5)	inactive	inactive	inactive	active
D16	2500 (class 5)	inactive	inactive	active	active
D17	4000 (class 5)	inactive	inactive	inactive	inactive
D18	2500 (class 5)	inactive	inactive	active	inactive
D19	2500 (class 5)	inactive	inactive	active	active
D20	2500 (class 5)	inactive	inactive	active	active
D21	2500 (class 5)	inactive	active	active	active
D22	4000 (class 5)	inactive	inactive	inactive	inactive
D23	4000 (class 5)	inactive	inactive	inactive	inactive
D24	4000 (class 5)	inactive	active	active	active
D25	2500 (class 5)	inactive	inactive	active	active
D26	2500 (class 5)	inactive	inactive	inactive	active
D27	2500 (class 5)	active	inactive	inactive	active
D28	4000 (class 5)	inactive	inactive	inactive	active
D29	2500 (class 5)	inactive	inactive	active	active
D30	2500 (class 5)	active	inactive	active	active
D31	2500 (class 5)	inactive	inactive	inactive	active
D32	2500 (class 5)	inactive	inactive	inactive	active
D33	2500 (class 5)	active	inactive	inactive	active
D34	2500 (class 5)	inactive	inactive	inactive	inactive
D35	4000 (class 5)	inactive	inactive	inactive	inactive
D36	4000 (class 5)	inactive	inactive	inactive	active
D37	4000 (class 5)	active	inactive	inactive	active
D38	4000 (class 5)	inactive	inactive	inactive	inactive
D39	4000 (class 5)	inactive	inactive	inactive	inactive
D40	4000 (class 5)	inactive	inactive	active	active
D41	2500 (class 5)	inactive	inactive	inactive	active
D42	4000 (class 5)	inactive	active	inactive	active
D43	2500 (class 5)	inactive	inactive	inactive	inactive
D44	4000 (class 5)	inactive	active	active	inactive
D45	4000 (class 5)	active	inactive	inactive	active
D46	2500 (class 5)	active	inactive	inactive	active
D47	2500 (class 5)	inactive	inactive	inactive	active
D48	2500 (class 5)	inactive	inactive	inactive	active
D49	4000 (class 5)	inactive	inactive	inactive	active
D50	2500 (class 5)	inactive	inactive	inactive	active

Table 5: Drug-likeness prediction of selected compounds.

	Molecular Formula	Molecular weight	Log p	TPSA	H-bond donar	H-bond accepter	Lipinski rule
D1	C₁₅H₉NO₄	267.24 g/mol	2.56	76.03 Å²	0	4	Yes
D2	C₁₅H₁₀O₃	238.24 g/mol	2.75	50.44 Å²	1	3	Yes
D3	C₁₆H₁₂O₂	236.27 g/mol	3.50	30.21 Å²	0	2	Yes
D4	C₁₆H₁₂O₃	252.26 g/mo	3.15	39.44 Å²	0	3	Yes
D5	C₁₅H₉BrO₂	301.13 g/mol	3.80	30.21 Å²	0	1	Yes
D6	C₁₅H₁₁NO₂	237.25 g/mol	2.61	56.23 Å²	1	2	Yes
D7	C₁₅H₁₀O₃	238.24 g/mol	2.75	50.44 Å²	1	3	Yes
D8	C₁₅H₁₀O₃	238.24 g/mol	2.78	50.44 Å²	1	3	Yes
D9	C₁₅H₁₁NO₂	237.25 g/mol	2.61	56.23 Å²	1	2	Yes
D10	C₁₉H₁₈O₂	278.35 g/mol	4.38	30.21 Å²	0	2	Yes
D11	C₁₅H₉BrO₂	301.13 g/mol	3.80	30.21 Å²	0	2	Yes
D12	C₁₆H₁₀O₄	266.25 g/mol	2.64	67.51 Å²	1	4	Yes
D13	C₂₃H₂₆O_s	350.45 g/mol	4.19	50.44 Å²	1	3	Yes
D14	C₁₈H₁₆O₅	312.32 g/mol	3.12	57.90 Å²	0	5	Yes
D15	C₁₉H₁₈O₂	278.35 g/mol	4.38	30.21 Å²	0	2	Yes
D16	C₁₅H₉ClO₂	256.68 g/mol	3.71	30.21 Å²	0	2	Yes
D17	C₁₇H₁₄O₄	282.29 g/mol	3.13	48.67 Å²	0	4	Yes
D18	C₁₅H₉FO₂	240.23 g/mol	3.48	30.21 Å²	0	3	Yes
D19	C₁₅H₉FO₂	240.23 g/mol	3.56	30.21 Å²	0	3	Yes
D20	C₁₅H₉ClO₂	256.68 g/mol	3.78	30.21 Å²	0	2	Yes
D21	C₁₆H₁₂O₂	236.27 g/mol	3.51	30.21 Å²	0	2	Yes
D22	C₁₈H₁₆O₆	328.32 g/mol	3.00	78.13 Å²	1	6	Yes
D23	C₁₇H₁₄O₅	298.29 g/mol	2.95	68.90 Å²	1	5	Yes
D24	C₁₅H₉ClO₃	272.68 g/mol	3.59	50.44 Å²	1	3	Yes
D25	C₁₅H₉IO₂	348.14 g/mol	3.84	30.21 Å²	0	2	Yes
D26	C₁₅H₉FO₂	240.23 g/mol	3.49	30.21 Å²	0	3	Yes
D27	C₁₅H₉NO₄	267.24 g/mol	2.55	76.03 Å²	0	4	Yes
D28	C₁₅H₁₀O₃	238.24 g/mol	2.78	50.44 Å²	1	3	Yes
D29	C₁₆H₁₂O₂	236.27 g/mol	3.51	30.21 Å²	0	2	Yes
D30	C₁₅H₉NO₄	267.24 g/mol	2.54	76.03 Å²	0	4	Yes
D31	C₁₇H₁₄O₂	250.29 g/mol	3.79	30.21 Å²	0	2	Yes
D32	C₁₈H₁₆O₂	264.32 g/mol	4.11	30.21 Å²	0	2	Yes
D33	C₁₅H₁₁NO₂	237.25 g/mol	2.63	56.23 Å²	1	2	Yes
D34	C₁₅H₁₀O₄	254.24 g/mol	2.64	70.67 Å²	2	4	Yes
D35	C₁₆H₁₂O₃	252.26 g/mol	3.36	50.44 Å²	1	3	Yes
D36	C₁₆H₁₂O₄	268.26 g/mol	3.04	59.67 Å²	1	4	Yes
D37	C₁₅H₉NO₅	283.24 g/mol	2.44	96.26 Å²	1	5	Yes
D38	C₁₈H₁₆O₆	328.32 g/mol	3.00	78.13 Å²	1	6	Yes
D39	C₁₇H₁₄O₃	266.29 g/mol	3.66	50.44 Å²	1	3	Yes
D40	C₁₆H₁₁ClO₃	286.71 g/mol	3.68	39.44 Å²	0	3	Yes
D41	C₁₅H₈ClNO₄	301.68 g/mol	3.07	76.03 Å²	0	4	Yes
D42	C₁₆H₁₂O₃	252.26 g/mol	3.38	50.44 Å²	1	3	Yes
D43	C₁₅H₁₀O₄	254.24 g/mol	2.67	70.67 Å²	2	4	Yes
D44	C₁₈H₁₆O₃	280.32 g/mol	3.99	50.44 Å²	1	3	Yes
D45	C₁₅H₁₁NO₃	253.25 g/mol	2.50	76.46 Å²	2	3	Yes
D46	C₁₅H₁₀ClNO₂	271.70 g/mol	3.14	56.23 Å²	1	2	Yes
D47	C₁₈H₁₅ClO₂	298.76 g/mol	4.72	30.21 Å²	0	2	Yes
D48	C₁₅H₈BrClO₂	335.58 g/mol	4.39	30.21 Å²	0	2	Yes
D49	C₁₆H₁₁ClO₃	286.71 g/mol	3.76	39.44 Å²	0	3	Yes
D50	C₁₅H₈Cl₂O₂	291.13 g/mol	4.31	30.21 Å²	0	2	Yes

DISCUSSION

In pharmaceutical research, computational strategies are of tremendous significance since they help in the identification and development of novel promising molecules, notably using molecular docking approaches^{20, 21}The angiotensin-converting enzyme (ACE) plays an important role in blood pressure regulation, and inhibiting ACE using inhibitory peptides is thought to be a main target for hypertension prevention. Several research employed the docking technique to block the expression of the ACE protein with drugs²². Among all derivatives, D10 (2-(4-tert-butylphenyl)-4H-1-benzopyran-4-one) has the lowest binding energy with β 1 adrenergic receptor (binding energy – 11.0 Kcal/mol), D47 with ACE and neutral endopeptidase inhibitors (binding energy – 9.4 Kcal/mol), D47 with ACE has lowest binding energy (binding energy – 9.2 Kcal/mol), for cox-2 the lowest binding energy with D47 derivative (binding energy – 10.5 Kcal/mol), D32 has lowest binding energy with receptor MAPK (binding energy – 10.4 Kcal/mol), D11 has lowest binding energy with protein neprilysin (binding energy – 9.4 Kcal/mol). In the present study, we have selected 50 flavone derivatives which are synthesize from benzaldehyde and acetophenone derivatives against protein targets of various cardiovascular disease. It was found that among all selected above derivatives satisfy all parameters of ADMET and toxicity, also showed good affinity with selected protein targets, therefore, they could be used as potential broad -spectrum candidate for treatment of different heart problems.

CONCLUSIONS

The current study attempted to computationally find chemicals that can bind to the numerous targets of cardiovascular disease. The docking scores and interactions of the compounds indicate that the majority of the compounds can bind to several targets involved in cardiovascular disease. ADMET and toxicity prediction revealed that derivatives D10, D11, D32, and D47 could be employed as possible cardiovascular disease treatments.

ACKNOWLEDGEMENT

Authors would like to acknowledge Priyadarshini JL College of Pharmacy, Nagpur, for providing research facilities

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