Using the Pharmacophoric features of Azithromycin to design potential SARS-CoV-2 inhibitor


  •   O. V. Ikpeazu

  •   F. J. Amaku

  •   I. E. Otuokere

  •   K. K. Igwe


The outbreak of novel coronavirus (SARS-CoV-2) found in Wuhan China is rapidly spreading to all nations of the world.  Currently, there are no approved drugs for the treatment of the novel coronaviral disease.  Meanwhile, repositioning of some antibiotics, antiviral and antimalaria drugs have been employed.  In this study, we used azithromycin as a model drug to virtual screen the ZINC database and the molecules obtained were docked against SARS-CoV-2 protein with PDB code: 5r7y.  The best five ligands with high affinity for the target protein was compared with the reference molecule (Azithromycin).  The docking score for the predicted ligands with high affinity for the target protein include ZINC10635972 (-6.3 kcal/mol), ZINC02651653 (-6.2 kcal/mol), ZINC09728215 (-6.2 kcal/mol), ZINC15003138 (-6.1 kcal/mol), ZINC89836288 (-6.1 kcal/mol) and azithromycin (+28.2 kcal/mol).  The lead molecule (ZINC10635972) was observed to interacted with LUE 141, ASN 142, SER 144, SER 46, GLY 189, GLU 166, MET 165, HIS163, MET 49, HIS 164, PHE 140, GLY 143,THR 25, CYS 145, HIS 41, CYS 44 and THR 45.  Meanwhile, hydrogen bond was predominant in the ZINC10635972-5r7y interaction.  The lead molecule demonstrated good pharmacokinetics properties, drug-like characteristic and moderate chemical reactivity index.  Besides, ZINC10635972 was noticed to fit the class 5 toxicity index.  Hence, ZINC10635972 is a promising compound that should be further examined as drug candidates before clinical evaluation.

Keywords: COVID-19, Virtual Screening, Azithromycin, Molecular Docking, Pharmacokinetics


Oke, J. and C. Heneghan, Global Covid-19 Case Fatality Rates. CEBM. URL https://www. cebm. net/covid-19/global-covid-19-case-fatality-rates/[accessed 29 March 2020], 2020.

Babalola Ph D, M.O., The Strengths, Weaknesses, Opportunities and Threats (SWOT) Analysis of the Severe Acute Respiratory Syndrome Coronavirus 2 of COVID-19. The University of Louisville Journal of Respiratory Infections, 2020. 4(1): p. 45.

Islam, R., et al., A molecular modeling approach to identify effective antiviral phytochemicals against the main protease of SARS-CoV-2. Journal of Biomolecular Structure and Dynamics, 2020: p. 1-12.

Prajapat, M., et al., Drug targets for corona virus: A systematic review. Indian journal of pharmacology, 2020. 52(1): p. 56.

Yuki, K., M. Fujiogi, and S. Koutsogiannaki, COVID-19 pathophysiology: A review. Clinical immunology, 2020: p. 108427.

Rabby, M.I.I., Current drugs with potential for treatment of COVID-19: a literature review. Journal of Pharmacy & Pharmaceutical Sciences, 2020. 23(1): p. 58-64.

Haider, Z., et al., In Silico discovery of novel inhibitors against main protease (Mpro) of SARS-CoV-2 using pharmacophore and molecular docking based virtual screening from ZINC database. 2020, Preprints.

Morris, G.M., et al., Automated docking using a Lamarckian genetic algorithm and an empirical binding free energy function. Journal of computational chemistry, 1998. 19(14): p. 1639-1662.

Pettersen, E.F., et al., UCSF Chimera—a visualization system for exploratory research and analysis. Journal of computational chemistry, 2004. 25(13): p. 1605-1612.

Koes, D.R. and C.J. Camacho, ZINCPharmer: pharmacophore search of the ZINC database. Nucleic acids research, 2012. 40(W1): p. W409-W414.

Hanwell, M.D., et al., Avogadro: an advanced semantic chemical editor, visualization, and analysis platform. Journal of cheminformatics, 2012. 4(1): p. 17.

Monteiro, A., M. Scotti, and L. Scotti. Molecular docking of fructose-derived nucleoside analogs against reverse transcriptase of HIV-1. in Proceedings of MOL2NET 2019, International Conference on Multidisciplinary Sciences, 5th edition. 2019. MDPI.

Jayaram, B., et al. Sanjeevini: a freely accessible web-server for target directed lead molecule discovery. in BMC bioinformatics. 2012. Springer.

Lipinski, C.A., Lead-and drug-like compounds: the rule-of-five revolution. Drug Discovery Today: Technologies, 2004. 1(4): p. 337-341.

Koopmans, T., Über die Zuordnung von Wellenfunktionen und Eigenwerten zu den einzelnen Elektronen eines Atoms. Physica, 1934. 1(1-6): p. 104-113.

Pandey, M., S. Muthu, and N.N. Gowda, Quantum mechanical and spectroscopic (FT-IR, FT-Raman, 1H, 13C NMR, UV-Vis) studies, NBO, NLO, HOMO, LUMO and Fukui function analysis of 5-Methoxy-1H-benzo [d] imidazole-2 (3H)-thione by DFT studies. Journal of Molecular Structure, 2017. 1130: p. 511-521.

Yang, W. and R.G. Parr, Hardness, softness, and the fukui function in the electronic theory of metals and catalysis. Proceedings of the National Academy of Sciences, 1985. 82(20): p. 6723-6726.

Gaussian09, R.A., M.J. Frisch, G.W. Trucks, H.B. Schlegel, G.E. Scuseria, M.A. Robb, J.R. Cheeseman, J.A. Gonzalez, J.A. Pople, Gaussian 09, Revision E.01, Gaussian, Inc, Wallingford, CT, 2004. Inc., Wallingford CT, 2009. 121: p. 150-166.

Becke, A.D., Real-space post-Hartree–Fock correlation models. The Journal of chemical physics, 2005. 122(6): p. 064101.

Ayers, P.W. and M. Levy, Perspective on “Density functional approach to the frontier-electron theory of chemical reactivity”. Theoretical Chemistry Accounts, 2000. 103(3-4): p. 353-360.

Chermette, H., Chemical reactivity indexes in density functional theory. Journal of Computational Chemistry, 1999. 20(1): p. 129-154.

Geerlings, P., F. De Proft, and W. Langenaeker, Conceptual density functional theory. Chemical reviews, 2003. 103(5): p. 1793-1874.

Gazquez, J.L., A. Cedillo, and A. Vela, Electrodonating and electroaccepting powers. The Journal of Physical Chemistry A, 2007. 111(10): p. 1966-1970.

Chattaraj, P.K., A. Chakraborty, and S. Giri, Net electrophilicity. The Journal of Physical Chemistry A, 2009. 113(37): p. 10068-10074.


Download data is not yet available.


How to Cite
Ikpeazu, O., Amaku, F., Otuokere, I. and Igwe, K. 2020. Using the Pharmacophoric features of Azithromycin to design potential SARS-CoV-2 inhibitor. European Journal of Engineering and Technology Research. 5, 9 (Sep. 2020), 1037-1042. DOI: