IIARD International Institute of Academic Research and Development

INTERNATIONAL JOURNAL OF HEALTH AND PHARMACEUTICAL RESEARCH (IJHPR )

E-ISSN 2545-5737
P-ISSN 2695-2165
VOL. 10 NO. 1 2025
DOI: 10.56201/ijhpr.vol.10.no1.2025.pg43.63

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Swiss Similarity Studies of 5,7-dihydroxy-2-(4-hydroxyphenyl) chromen-4-one: A Promising Role against Sickle cell disease

Yemisi Elizabeth ASIBOR, Nathaniel Oladoye OLATUNJI, Dayo Felix LATONA, Abel Kolawole OYEBAMIJI, and Banjo SEMIRE

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Abstract

Swiss Similarity Studies were carried out on 5,7-dihydroxy-2-(4-hydroxyphenyl)chromen-4- one (A28) to estimate the anti-sickle properties of the compound. Seven compounds similar to 5,7-dihydroxy-2-(4-hydroxyphenyl)chromen-4-one were obtained, A28-1, A28-2, A28-3, A28-4, A28-5, A28-6 and A28-7. The Swiss ADMET and Pro-tox II tools were utilized for ADMET profiling of these compounds. Six compounds with good ADMET properties and conventional drug (Rivaroxaban) were optimized at DFT, docked with (PBD: 5E6E, 6DI4, and 6WBU). Molecular docking analysis showed that A28-2 and A28-4 had -9.1 kcal/mol and -9.2 kcal/mol binding affinities with 5E6E, A28-3 -8.7 kcal/mol had binding affinity with 6D14, A28-2 and A28-4 had binding affinities -9.5 kcal/mol and -9.9 kcal/mol binding affinities with 6BWU. Rivaroxaban had -8.5 kcal/mol, -8.0 kcal/mol, and -8.1 kcal/mol binding affinities with 5E6E, 6D14, and 6BWU respectively. From the docking results, compounds A28-2, A28-3, and A28-4 show a stronger therapeutic potential than rivaroxaban.

keywords:

ADMET, DFT, Molecular docking, Sickle Cell Diseases, Swiss Similarity

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References:

Adegbola, P. I., Semire, B., Fadahunsi, O. S. & Adegoke, A. E. Molecular docking and
ADMET studies of Allium cepa, Azadirachta indica and Xylopia aethiopica
isolates as potential anti-viral drugs for Covid-19. Virus Disease h t t p s : / / d o
i . o r g / 1 0 . 1 0 0 7 / s 1 3 3 3 7 - 0 2 1 - 0 0 6 8 2 - 7 (2021).
Adepoju, A.J., Latona, D.F., Olafare, O.G., Oyebamiji, A.K., Abdul-Hammed, M., &
Semire, B. Molecular docking and pharmacokinetics studies of Curcuma longa
(Curcumin) potency against Ebola virus. Ovidius Uni. Ann. Chem. 33(1), 23 -
35, 2022
Asibor, Y. E., Oyebamiji, A. K., Latona, D. F., & Semire, B. (2024).
Computational screening of phytochemicals presents in some Nigerian medicinal
plants against sickle cell disease. Scientific Reports, 14(1),
26368.https://dio.org/10.1038/s41598-024-75078-w.
Banerjee P, Eskert A, O, Schrey AK Preissner R. ProTox -II: a webserver for the
prediction of toxicity of chemicals Nucleic Acids Res. 2018 Jul, 2;46(WI);
W257-W263.
Doi:
10.1093/nar/gky318.
PMID:
29718510
PMCID:
PMC6031011.
Becke, A. D. Density-functional thermochemistry. III. The role of exact exchange. J.
Chem. Phys. 98, 5648–6565. h t t p s : / / d o i . o r g / 1 0 . 1 0 6 3 / 1 . 4 6 4 9 1
3 (1993).
Bragina, M.E.; Daina, A.; Perez, M.A.S.; Michielin, O.; Zoete, V. The SwissSimilarity
2021 Web Tool: Novel Chemical Libraries and Additional Methods for an
Enhanced Ligand-Based Virtual Screening Experience. Int. J. Mol. Sci. 2022, 23,
https://doi.org/10.3390/ ijms23020811.
Daina A, Michielin O, Zoete V. SwissADME: (2017) a free web tool to evaluate
pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small
molecules. Scientific Reports.Mar;7:42717. doi: 10.1038/srep42717.
Jacquemin, D., Perpete, E.A., Ciofini, I., Adamo, C. (2008). Accurate simulation of
optical properties in dyes, Accounts of chemical Research ;42:326 –
3344.https://doi.org/10.1021/ar800163d
Laskowski, R. A., & Thornton, J. M. (2022). PDBsum extras: SARS?Cov?2 and
AlphaFold models. Protein science, 31(1), 283-289.
Lee, C., Yang, W., Parr, R.G (1988). Development of the Colle-Salvetti correlation-
energy formula into a functional of the electron density. Phys. Rev. B 37: 785-



789.https://doi.org/10.1103/PhysRevB.37.785.mainstream. Clin Transl Med.
2022;12(4):e766.
doi:10.1002/ctm2.766.
Molecules.
2017;22(2).
doi:10.3390/molecules220202.
Lipinski,C.A., Lombardo, F., Dominy, B.W., Feeney P.J., (1997) mainstream. Clin
Transl Med. 2022;12(4):e766. doi:10.1002/ctm2.766.
Lipinski, C. A. Lead-and drug-like compounds: The rule-of-five revolution. Drug
Discov. Today: Technol. 1(4), 337–343. h t t p s : / / d o i . o r g / 1 0 . 5 7 7 2 / 5
2 6 4 2 (2004).
Middleton, E., Kandaswami, C., & Theoharides, T. C. (2000). The effects of plant
flavonoids on mammalian cells: Implications for inflammation, heart disease,
and cancer. Pharmacological Reviews, 52(4), 673-751.
Nur, E., Biemond, B. J., Otten, H. E., et al. (2011). Oxidative stress in sickle cell
disease; pathophysiology and potential implications for disease management.
American Journal of Hematology, 86(6), 484-489.
Owonikoko, A.D., Sodamade A., Olatunde, A.M., Odoje, O.F., & Semire, B. In silico
Identification of bioactive compounds from Chasmanthera dependens and
Carissa edulis as potential inhibitors of Carbonic anhydrases (CAs) receptors
using a target-based drug design approach. Int. J. Novel Res. Dev. 9(7), b416-
b441
Piel, F. B., Steinberg, M. H., & Rees, D. C. (2017). Sickle cell disease. New England
Journal of Medicine, 376(16), 1561-1573.
Pérez, L.R.M., Lázaro, J.M., Puentes, N.C., Amador, A. ., Marrugo-Padilla, A. (2024)
Identification of proinflammatory pathways and promising bioactive
polyphenols for the treatment of sickle cell anemia by in silico study and network
pharmacology. Inform Med Unlocked. 49, 101534
Rees, D. C., Williams, T. N., & Gladwin, M. T. (2010). Sickle-cell disease. The Lancet,
376(9757), 2018-2031.
Serjeant, G. R. (2010). The natural history of sickle cell disease. Cold Spring Harbor
Perspectives in Medicine, 3(10), a011783.
Shravani S. Pawar, Sachin H. Rohane. Review on Discovery Studio: An important Tool
for Molecular Docking. Asian J. Research Chem. 2021; 14(1):86-88. doi:
10.5958/0974-4150.2021.00014.6
Available
on:
https://www.ajrconline.org/AbstractView.aspx?PID=2021-14-1-14



Tian, Chang Chen, Xue Lei, Jieling Zhao, Jie Liang, CASTp 3.0: computed atlas of
surface topography of proteins, Nucleic Acids Research, Volume 46, Issue W1,
2 July 2018, Pages W363–W367, https://doi.org/10.1093/nar/gky473.
Trott, O. & Olson, A. J. (2010). AutoDock Vina: Improving the speed and accuracy
of docking with a new scoring function, efficient optimization, and
multithreading.
J.
Comput.
Chem.
31,
455–461.
https://doi.org/10.1002/jcc.21334.
Veszelka, S. et al. Comparison of a rat primary cell-based blood-brain barrier model
with epithelial and brain endothelial cell lines: Gene expression and drug
transport.
Front.
Mol.
Neurosci.
11,
https://doi.org/10.3389/fnmol.2018.00166 (2018).
Ware, R. E., de Montalembert, M., Tshilolo, L., & Abboud, M. R. (2017). Sickle cell
disease. The Lancet, 390(10091), 311-323.
Yadav, S., Aslam, M., Prajapat, A. et al. (2024) Investigate the binding of pesticides with
the TLR4 receptor protein found in mammals and zebrafish using molecular
docking
and
molecular
dynamics
simulations.
Sci
Rep 14,
https://doi.org/10.1038/s41598-024-75527-6
Zoete, V., Daina, A., Bovigny, C., & Michielin, O. (2016). SwissSimilarity: A web tool
for low to ultra-high throughput ligand-based virtual screening. Journal of
Chemical Information and Modeling, 56(8), 1399-1404.

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