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In-silico ADMET predicated Pharmacoinformatics of Quercetin-3-Galactoside, polyphenolic compound from Azadirachta indica, a sacred tree from Hill Temple in Alagarkovil Reserve Forest, Eastern Ghats, INDIA

M. Sabitha1, K. Krishnaveni2, M. Murugan1, A N Basha1, Gilse A Pallan1, C. Kandeepan1, S. Ramya3, R. Jayakumararaj3*

PG & Research Department of Zoology, Arulmigu Palaniandavar College of Arts & Culture, Palani – 624601, TamilNadu, India

Department of Zoology, GTN College, Dindigul, TamilNadu, India

Department of Botany, Government Arts College, Melur – 625106, Madurai, TamilNadu, India 

Article Info:


Article History:

Received 11 August 2021      

Reviewed 20 September 2021

Accepted 28 September 2021  

Published 15 October 2021  


Cite this article as: 

Sabitha M, Krishnaveni K, Murugan M, Basha AN, Pallan GA, Kandeepan C, Ramya S, Jayakumararaj R, In-silico ADMET predicated Pharmacoinformatics of Quercetin-3-Galactoside, polyphenolic compound from Azadirachta indica, a sacred tree from Hill Temple in Alagarkovil Reserve Forest, Eastern Ghats, INDIA, Journal of Drug Delivery and Therapeutics. 2021; 11(5-S):77-84

DOI: http://dx.doi.org/10.22270/jddt.v11i5-S.5026        


*Address for Correspondence:  

R. Jayakumararaj, PG & Research Department of Zoology, Arulmigu Palaniandavar College of Arts & Culture, Palani – 624601, TamilNadu, India



Quercetin (3,3′,4′,5,7-Pentahydroxyflavone) is the one among the bioactive secondary metabolite (BASM) in neem seed of Azadirachta indica A. Juss. Quercetin (Que) and its derivatives hold promising pharmacological effects. Antidiabetic, anti-inflammatory, antioxidant, antimicrobial, anti-Alzheimer’s, antiarthritic, cardiovascular, and wound-healing effects of Que have been extensively investigated, recently lot of work has been carried out on its anticancer activity against different cancer cell lines. Recently, in silico/ in vitro studies have demonstrated that Que interferes with different stages of coronavirus entry and replication cycle (PLpro, 3CLpro, and NTPase/helicase). Due to its pleiotropic effects in human health and disease and lack of systemic toxicity, Que and its derivatives could be tested for their efficacy on human target system in future clinical trials. In the present study, an attempt has been made to evaluate the physicochemical, druggable properties of Que from A. indica to prospect its ADMET properties.

Keywords: NEEM; Azadirachta indicaQuercetin; Pharmacoinformatics; ADMET; Drug-Likeness; Toxicology




Azadirachta indica A. Juss commonly known as Neem belongs to the family Meliaceae1-3. Popular as natural store-house of phyto-drugs it has been exploited for its medicinal properties since the dawn of civilization4,5. Neem is a versatile plant across the country for its use in Indigenous/ Traditional Systems of Medicine. A. indica has its origin from India however, common in South East Asian (SEA) Region (Bangladesh, Srilanka, Bhutan, Myanmar, Pakistan, and Nepal)6. Recently, it has been disseminated world over, (tropical and sub-tropical regions)7

Neem, a perennial, medium-sized (10 - 15 m) fast-growing tree needs an optimum temperature of 40-50 °C, annual rainfall (400 – 800 mm/annum) and grows well in poor/ degraded/ mined soils. Being repository of bioactive secondary metabolites Neem tree remains as the ideal target for research. As most of the secondary metabolite are localised in leaves/ seeds, destruction of the plant is not warranted. Almost all of the bioactive secondary metabolites in Neem are therapeutic, eco-friendly and biodegradable in nature, therefore GRAS to man and environment8-11. The most active constituent is azadirachtin, besides others viz., nimbolinin, nimbin, nimbidin, nimbidol, sodium nimbinate, gedunin, salannin, and quercetin. Leaves contain BASM such as nimbin, nimbanene, 6-desacetylnimbinene, nimbandiol, nimbolide, ascorbic acid, n-hexacosanol, 7-desacetyl-7-benzoylazadiradione, 7-desacetyl-7-benzoylgedunin, 17-hydroxyazadiradione, and nimbiol12-14. Quercetin and β-sitosterol, polyphenolic flavonoids, obtained from fresh leaves have significant antibacterial and antifungal properties14

Since antiquity all parts of the plant, including root, stem, bark, leaves, fruits, and seeds are used to cure various ailments in humans and domestic animals therefore, Neem has been considered as multi-purposes village dispensary15-25. In fact, therapeutic applications attributed to Neem include abortive, analgesic, antibacterial, anticancer, antidiabetic, antifungal, anti-helminthic, anti-hyperglycemic, anti-inflammatory, antimalarial, antipyretic, antispasmodic, anti-spermatogenic, antiviral, diuretic, hyper-cholesteremic, immuno-modulatory, mouth-wash, contraception, dental plaque, head lice, heart disease, insect repellent, malaria, pesticide, psoriasis, skin diseases, wound healing, gastro-intestinal ailments, SARS-CoV-2 26-59

Que - aglycone is able to conjugate with glucose, xylose, or rutinose attaching to one of the Que’s hydroxyl groups with the consequent creation of various Que glycoside forms. Quercetin-3-O-glycoside is present as a pigment in flowers, vegetables, and fruits; as glycosides rather than as aglycones. Que exhibits higher bioavailability than other phytochemicals. Main sources of Que include grapes, berries, cherries, apples, citrus fruits, onions, buckwheat, kale, tomatoes, red wine, and black tea. However, the concentration of Que varies from one plant to another sometimes in different parts of the same plant even. Que, is powerful antioxidant than vitamins C and E. Que and its derivatives regulate cell cycle progression and cellular signal transduction pathway. Metabolic plasticity of Que is the key determinant in the plant adaptive reaction, Que aglycones are effective regulators of auxin transport, growth and development in plant systems. In animal system - anti-inflammatory and antioxidant effects of Que regulate oxidative, kinase, and cell cycle inhibitor activity, apoptosis-inducing effect holds anticancer potential. Furthermore, Que exerts a remarkable effect on cellular immunity and localised inflammations60. Recently, it has been reported that abundance of Que in different plant parts of Artemisia annua may be exploited for the treatment of coronavirus61. With this background information pharmacological characterization is expected to further validate Que as novel drug lead62-68.







Equisetopsida C. Agardh




Magnoliidae Novák Ex Takht.



Rosanae Takht.



Sapindales Juss. Ex Bercht.



Meliaceae Juss.



Azadirachta A. Juss.



Azadirachta indica A. Juss.

Common Name 



Vernacular Name


Vempamaram (Tamil)



Botanical Description:  Tree, up to 15 m tall. Branches glabrous; Leaves imparipinnate, pulvinus at the base; leaflets alternate to opposite, 2.5 - 7.0 cm long, 1.5 - 4.0 cm broad, ovate, subsessile, acuminate; Flowers white, sweet-scented; Sepals obovate, 1.5 mm long, puberulous, imbricate. Petals 6 mm long, obvoate to oblong, white, margin ciliate; Staminal tube 5 mm long, puberulous, 10-striate, 10-toothed; teeth 2-lobed; anthers oblong, basifixed; Ovary sub-globose; style linear 2.5 mm long; stigma trifid. Fruit: Drupe oblong, 1.3 - 2.0 cm long, greenish-yellow, Seed: 1-seeded. Plants were collected from the fields in the wild Alagar Hills, Eastern Ghats, INDIA as described previously33

GC-MS Analysis 

Neem Seed Oil Extracts of A. indica was obtained from the seed samples collected from the foothills of Alagar Hills, Alagarkovil Reserve Forest, Dindigul District, Tamil Nadu, India. Phyto-components were identified using GC–MS detection system as described previously, however with modification, whereby portion of the extract was analysed directly by headspace sampling. GC–MS analysis was accomplished using an Agilent 7890A GC system set up with 5975C VL MSD (Agilent Technologies, CA, USA). Capillary column used was DB-5MS (30×0.25 mm, film thickness of 0.25 μm; J&W Scientific, CA, USA). Temperature program was set as follows: initial temperature 50°C held for 1 min, 5°C per min to 100°C, 9°C per min to 200°C held for 7.89 min, and the total run time was 40 min. The flow rate of helium as a carrier gas was 0.811851 mL/ min. MS system was performed in electron ionization (EI) mode with Selected Ion Monitoring (SIM). The ion source temperature and quadruple temperature were set at 230°C and 150°C, respectively. Identification of phyto-components was performed by comparison of their retention times and mass with those of authentic standards spectra using computer searches in NIST 08.L and Wiley 7n.l libraries3,33

ADMET Prediction 

PubChem database was applied to get the smiles structures of the natural compounds, and was further used for the ADMET prediction. The qualitative assessment of pharmacokinetics viz; absorption, distribution, metabolism, excretion and toxicity (ADMET) profile of selected compounds were predicted computationally by using SwissADME and toxicity prediction using TOPKAT (Accelrys, Inc., USA). QikProp develops and employs QSAR/QSPR models using partial least squares, principal component analysis and multiple linear regression to predict physico-chemical significant descriptors68-70.





Chemical kingdom

Organic compounds

Super class

Phenylpropanoids and polyketides




Flavonoid glycosides

PubChem Identifier




Canonical SMILES 


InChI Key




Physicochemical Properties

The molecular weight of AZA was 720.72 (g/mol); the calculated LogP value was -0.20; LogD - 0.14; LogSw - -4.34. The total number of stereo-centers in the molecule was 16; the stereo-chemical complexity of the molecule was 0.457; the calculated Fsp3 value of AZA was 0.771; The overall calculated Topological polar surface area of AZA was 215.34(Å2). Likewise the calculated number of hydrogen bond donors in the molecule was 3; whereas the number of hydrogen bond acceptors was 16; the number of smallest set of smallest rings (SSSR) in the molecule analyzed was 2; the size of the biggest system ring in the molecule was 15; similarly, the total number of rotatable bonds in the molecule was 6; the number of rigid bonds was 38; the number of charged groups was 0; similarly the total charge of the compound was 0; the number of carbon atoms in the molecule was 35; whereas the number of heteroatoms in AZA was calculated as 16; the number of heavy atoms in the molecule was calculated as 51; the ratio between the number of non-carbon atoms and the number of carbon atoms in the compound was 0.46 (Fig. 1).

Druggability Properties

Lipinski's rule of 5 violations of the molecule was 2; Veber rule was Low for the molecule; similarly Egan rule for the molecule was also Low; the Oral PhysChem score (Traffic Lights) for the molecule was recorded as 5; GSK's 4/400 score for the molecule was Good; Pfizer's 3/75 score for the molecule was Good; Weighted quantitative estimate of drug-likeness (QEDw) score for the molecule was 0.164; Solubility Forecast Index was Good and the solubility score was 9441.49; 

ADMET Properties

Only when the ADME/Tox properties of a drug like compound are of high quality, and when the target has been validated, the compound could be developed into a pharma-drug. In silico drug-likeness evaluation of Azadirachtin for Human Intestinal Absorption (HIA+) value had a probability of 0.890; Blood Brain Barrier (BBB-) value for the molecule had a probability of 0.773; Caco-2 permeable (Caco2-) value for the molecule had a probability of 0.711 (Fig. 4); P-glycoprotein substrate (Substrate) value for the molecule had a probability of 0.835; P-glycoprotein inhibitor I (Inhibitor) value for the molecule had a probability of 0.672; P-glycoprotein inhibitor II (Non-inhibitor) value for the molecule had a probability of 0.534. CYP450 2C9 substrate (Non-substrate) value for the molecule had a probability of 0.857; CYP450 2D6 substrate (Non-substrate) - 0.872; CYP450 3A4 substrate (Substrate) - 0.714; CYP450 1A2 inhibitor (Non-inhibitor) - 0.887; CYP450 2C9 inhibitor (Non-inhibitor) - 0.845; CYP450 2D6 inhibitor (Non-inhibitor) - 0.944; CYP450 2C19 inhibitor (Non-inhibitor) - 0.833; CYP450 3A4 inhibitor (Non-inhibitor) - 0.770; CYP450 inhibitory promiscuity (Low CYP Inhibitory Promiscuity) - 0.886; Ames test (Non AMES toxic) - 0.756; Carcinogenicity (Non-carcinogens) - 0.946; Biodegradation (Not ready biodegradable) - 1.000; Rat acute toxicity (4.348 LD50, mol/kg) - PNA; hERG inhibition (predictor I) (Weak inhibitor) - 0.992; hERG inhibition (predictor II) (Non-inhibitor) - 0.569 respectively. Computational methods for analysing and estimating the toxicity of natural bioactive compounds are considered as useful tool for validation as it provides in-depth understanding of toxicogenomics. Therefore, determining the toxicity of BASM in-silico is warranted to identify their potential harmful effects on humans, animals, plants, besides the environment as in-vivo animal tests are constrained by time, ethical considerations, and financial burden. Data pertaining to the descriptors viz., Toxicity, Environmental toxicity, Tox21 pathway and Toxicophore Rules for Azadirachtin are summarized in Table 2. Furthermore, GPCR ligand, ion channel modulator, kinase inhibitor, nuclear receptor ligand, protease inhibitor, enzyme inhibitor score for AZA were calculated as -0.71; -1.51; -1.46; -0.67; -0.35 and -0.71 respectively (Fig. 3). Swiss Target Prediction towards Macrophage migration inhibitory factor, Heat shock protein (HSP 90-alpha), Kappa Opioid receptor, Mu opioid receptor, Delta opioid receptor, Thrombin, Squalene synthetase, Glycogen synthase kinase-3 beta, Glycogen synthase kinase-3 alpha, Protein kinase C alpha, Apoptosis regulator Bcl-X, HMG-CoA reductase, Zinc finger protein GLI1, Proto-oncogene c-JUN, Vanilloid receptor for the compound has been provided in Table 4. Chemical and biological investigations on Azadirachta indica bioactive compounds indicates that the compound is safe for use as a drug molecule3,72-74.


The present study is an example to insights into the broad scope of pharmacoinformatics of Quercetin-3-Galactoside, a polyphenolic compound from Azadirachta indica (Neem), with an emphasis on plant based natural product drug discovery. The study indicates that plant based natural products still possess an extraordinary challenge that has to be solved before taken for drug development. However, it is anticipated that as more quality data on natural product research, such as bio-activity, biomolecularinformatics, cheminformatics, toxicoinformatics integrated together with new IoT data mining, algorithms and machine learning techniques to accelerate natural product based drug discovery. Furthermore, online databases serve as attractive sources for identifying novel natural product scaffolds with promising drug-like properties in NPs which is expected to accelerate the pace of Drug Discovery. 


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Figure 1:  2D, 3D, Physicochemical and Boiled-Egg Prediction of Quercetin-3 Galactoside



Table 1 Physicochemical, Druggability, ADMET properties of Quercetin-3 Galactoside



Molecular weight

464.38 g/mol







Number of stereo enters


Stereo-chemical complexity




Topological polar surface area

210.51 Å2

Number of hydrogen bond donors


Number of hydrogen bond acceptors


Number of smallest set of smallest rings (SSSR)


Size of the biggest system ring


Number of rotatable bonds


Number of rigid bonds


Number of charged groups


Total charge of the compound


Number of carbon atoms


Number of heteroatoms


Number of heavy atoms


Ratio between the number of non-carbon atoms and the number of carbon atoms




Lipinski's rule of 5 violations


Veber rule


Egan rule


Oral PhysChem score (Traffic Lights)


GSK's 4/400 score


Pfizer's 3/75 score


Weighted quantitative estimate of drug-likeness (QEDw) score




Solubility Forecast Index

Good Solubility




Human Intestinal Absorption



Blood Brain Barrier



Caco-2 permeable



P-glycoprotein substrate



P-glycoprotein inhibitor I



P-glycoprotein inhibitor II



CYP450 2C9 substrate



CYP450 2D6 substrate



CYP450 3A4 substrate



CYP450 1A2 inhibitor



CYP450 2C9 inhibitor



CYP450 2D6 inhibitor



CYP450 2C19 inhibitor



CYP450 3A4 inhibitor



CYP450 inhibitory promiscuity

Low CYP Inhibitory Promiscuity


Ames test

AMES toxic






Not ready biodegradable


Rat acute toxicity

2.387 LD50, mol/kg

Not applicable

hERG inhibition (predictor I)

Weak inhibitor


hERG inhibition (predictor II)



Physicochemical properties were computed using FAF-Drugs4 (28961788) and RDKit open-source cheminformatics platform. The druggabiity scoring schemes were computed using FAF-Drugs4 (28961788) and FAF-QED (28961788) open-source cheminformatics platform. ADMET features were predicted using admetSAR (23092397) open-source tool.