Quantitative structure-activity relationship modelling of influenza М2 ion channels inhibitors Scientific paper

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Ivanka G. Stankova
Radoslav L. Chayrov
Michaela Schmidtke
Dancho L. Danalev
Liudmila N. Ognichenko
Anatoly G Artemenko
Valery A. Shapkin
Victor E. Kuz'min


A series of adamantane derivatives (rimantadine and amantadine) incorporating amino-acid residues are investigated by simplex representation of molecular structure (SiRMS) approach in order to found correlation between chemical structures of investigated compounds and obtained data for antiviral activity and cytotoxicity. The obtained data from QSAR analysis show that adamantane derivatives containing amino acids with short aliphatic non-polar residues in the lateral chain will have good antiviral activity against the tested virus A/H3N2, strain Hong Kong/68 with low cytotoxicity. QSAR experiments and in vitro data also show good correlation and reveal that modified adaman­tine derivatives including guanidated in the lateral chain amino acid and β-amino acids as substituents show low to none activity.

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I. G. Stankova, “Quantitative structure-activity relationship modelling of influenza М2 ion channels inhibitors: Scientific paper”, J. Serb. Chem. Soc., vol. 86, no. 7-8, pp. 625-637, Aug. 2021.
Organic Chemistry


R. L. Tominack, F. G. Hayden, Infect. Dis. Clin. North. Am. 1 (1987) 459.

R. B. Belshe, M. H. Smith, C. B. Hall, R. Betts, A. J. Hay, J. Virol. 62 (1988) 1508 (https://jvi.asm.org/content/62/5/1508)

J. Wang, C. Ma, J. Wang, H. Jo, B. Canturk, G. Fiorin, W. F. DeGrado, J. Med. Chem. 56 (2013) 2804 (https://doi.org/10.1021/jm301538e)

R. M. Pielak, J. J. Chou, Biochem. Biophys. Res. Commun. 401 (2010) 58 (https://doi.org/10.1016/j.bbrc.2010.09.008)

J. Wang, J. X. Qiu, C. Soto, W. F. DeGrado, Curr. Opin. Struct. Biol. 21 (2011) 68 (https://doi.org/10.1016/j.sbi.2010.12.002)

A. V. Gaiday, I. A. Levandovskiy, K. G. Byler, T. E. Shubina, in Proceedings of International Conference on Computational Science, 2008, Berlin, Germany, pp. 360–368 (https://doi.org/10.1007/978-3-540-69387-1_40)

V. E. Kuz’min, A. G. Artemenko, E. N. Muratov, J. Computer-Aided Molec. Des. 22 (2008) 403 (https://doi.org/10.1007/s10822-008-9211-x)

V. E. Kuz’min, A. G. Artemenko, E. N. Muratov, P. G. Polischuk, L. N. Ognichenko, A. V. Liahovsky, A. I. Hromov, E. V. Varlamova, Recent Advances in QSAR Studies: Methods and Applications,Springer, Dordrecht, 2010, p.127 (ISBN 978-1-4020-9783-6)

S. Rännar, F. Lindgren, P. Geladi, S. Wold, J. Chemometrics 8 (1994) 111 (https://doi.org/10.1002/cem.1180080204)

L. Breiman, Machine Learning 45 (2001) 5 (https://doi.org/10.1023/A:1010933404324)

R. E. Carhart, D. H. Smith, R. Venkataraghavan, J. Chem. Inform. Comp. Sci. 25 (1985) 64 (https://doi.org/10.1021/ci00046a002)

K. Hasegawa, Y. Miyashita, K. Funatsu, J. Chem. Inform. Comp. Sci. 37 (1997) 306 (https://doi.org/10.1021/ci960047x)

V. E. Kuz’min, A. G. Artemenko, P. G. Polischuk, E. N. Muratov, A. I. Hromov, A. V. Liahovskiy, S. A. Andronati, S. Y. Makan, J. Mol. Model 11 (2005) 457 (http://doi.org/10.1007/s00894-005-0237-x)

OECD (2014) Guidance Document on the Validation of (Quantitative) Structure-Activity Relationship [(Q)SAR] Models, OECD Series on Testing and Assessment, OECD Publishing, Paris, 2014, p.154 (http://doi.org/10.1787/9789264085442-en)

M. Meloun, J. Militku, M. Hill, Analyst 127 (2002) 433 (http://doi.org/10.1039/B110779H)

P. G. Polischuk, E. N. Muratov, A. G. Artemenko, O. G. Kolumbin, N. N. Muratov, V. E. Kuz'min, J. Chem. Inf. Mod. 49 (2009) 2481 (http://doi.org/10.1021/ci900203n)

R. Chayrov, N. A. Parisis, M. V. Chatziathanasiadou, E. Vrontaki, K. Moschovou, G. Melagraki, H. Sbirkova-Dimitrova, B. Shivachev, M. Schmidtke, Y. Mitrev, M. Sticha, T. Mavromoustakos, A. G. Tzakos, I. Stankova, Molecules 25 (2020) 3989 (https://doi.org/10.3390/molecules25173989)

A. Chintakrindi, Ch. D'souza, M. Kanyalkar, Mini Rev. Med. Chem. 12 (2012) 1273 (https://doi.org/10.2174/138955712802761997).