Towards the discovery of molecules with anti-COVID-19 activity: Relationships between screening and docking results
Abstract
The study presents the results of a combined approach to the theoretical description of potential antiviral activity against COVID-19. We found that pharmacophore screening based on limited experimental data on "protein-ligand" binding complexes might have low predictive ability. Therefore, in this study, we build a model based on the statistical description of QSAR for data obtained from docking which serves as a basis for adequate prediction of ligand activity. We use the logistic regression to construct the predictive model for the main protease Mpro inhibitors.
Downloads
References
https://www.worldometers.info/coronavirus (Last updated: April 13, 2024, 01:00 GMT)
Jin Z et al. Structure of Mpro from SARS-CoV-2 and discovery of its inhibitors. Nature. 2020, 582(7811), 289–293. https://doi.org/10.1038/s41586-020-2223-y
Zhao Y et al. Crystal structure of SARS-CoV-2 main protease in complex with protease inhibitor PF-07321332. Protein Cell, 2022, 13(9), 689–693. https://doi.org/10.1007/s13238-021-00883-2
Yevsieieva, L. V.; Lohachova, K. O.; Kyrychenko, A. V.; Kovalenko, S. M.; Ivanov, V. V.; Kalugin O.N. Main and papain-like proteases as prospective targets for pharmacological treatment of coronavirus SARS-CoV-2. RSC Advances. 2023, 13, 35500-35524. https://doi.org/10.1039/D3RA06479D
Citarella, A.; Dimasi, A.; Moi, D.; Passarella, D.; Scala, A.; Piperno, A.; Micale, N. Recent Advances in SARS-CoV-2 Main Protease Inhibitors: From Nirmatrelvir to Future Perspectives. Biomolecules 2023, 13, 1339. https://doi.org/10.3390/biom13091339
Huang, C.; Shuai, H.; Qiao, J. et al. A new generation Mpro inhibitor with potent activity against SARS-CoV-2 Omicron variants. Sig Transduct Target Ther 2023, 8, 128. https://doi.org/10.1038/s41392-023-01392-w
Lohachova, K. O.; Sviatenko, A. S; Kyrychenko, A; Ivanov, V. V.; Langer, T.; Kovalenko, S. M.; Kalugin, O. N. Computer-aided drug design of novel nirmatrelvir analogs inhibiting main protease of Coronavirus SARS-CoV-2. Journal of Applied Pharmaceutical Science, 2024, 14(05), 232-239. http://doi.org/10.7324/JAPS.2024.158114
Pojtanadithee, P; Isswanich K; Buaban, K; Chamni, S, et al. A combination of structure-based virtual screening and experimental strategies to identify the potency of caffeic acid ester derivatives as SARS-CoV-2 3CLpro inhibitor from an in-house database. Biophysical Chemistry, 2024, 304, 107125. https://doi.org/10.1016/j.bpc.2023.107125
BIOVIA, Dassault Systemes, BIOVIA Draw 2018, San Diego: Dassault Systemes, 2018.
Berman, H.M.; Westbrook, J; Feng, Z; Gilliland, G.; Bhat, T.N.; Weissig H., Shindyalov, I.N.; Bourne, P.E. The Protein Data Bank. Nucleic Acids Research. 2000, 28, 235–242. doi: 10.1093/nar/28.1.235. URL: http://www.rcsb.org/
Yap, C.W. PaDEL-descriptor: an open source software to calculate molecular descriptors and fingerprints. J Comput Chem. 2011, 32(7), 1466–1474. http://doi.org/10.1002/jcc.21707
Hastie, T.; Tibshirani, R.; Friedman, J. The Elements of Statistical Learning. Springer Series in Statistics. 2009, 119–122. http://doi.org/10.1007/978-0-387-84858-7
Hosmer Jr, D. W.; Lemeshow, S.; Sturdivant, R. X. Applied Logistic Regression; John Wiley & Sons, 2013; Vol. 398.
Bilski, J.; Smol, J.; Kowalczyk, B.; Grzanek, K.; Izonin, I. Fast Computational Approach to the Levenberg-Marquardt Algorithm for Training Feedforward Neural Networks. Journal of Artificial Intelligence and Soft Computing Research. 2023, 12(2), 45-61. https://doi.org/10.2478/jaiscr-2023-0006
Moreau, G; Broto, P. Autocorrelation of a topological structure: A new molecular descriptor. Nouv. J. Chim. 1980, 4, 359–360.
Kier, L. B.; Hall, L. H. Molecular connectivity in chemistry and drug research, New York: Academic Press. 1976. https://doi.org/10.1016/b978-0-12-406560-4.x5001-6
Todeschini, R.; Consonni, V. Molecular descriptors for chemoinformatics, Weinheim: Wiley VCH. 2009, 27–37. https://doi.org/10.1007/b94608
James, G.; Witten, D.; Hastie, T.; Tibshiranu, R.; Taylor, J. Resampling Methods. in An Introduction to Statistical Learning, Springer Texts in Statistics, 2023, 201–226. https://doi.org/10.1007/978-3-031-38747-0_5
Fawcett, T. An Introduction to ROC Analysis. Pattern Recognition Letters. 2006, 27(8), 861-874. https://doi.org/10.1016/j.patrec.2005.10.010
Citations
From ML300 to Novel Non-covalent Inhibitors of SARS‑CoV‑2 Main Protease via Evolutionary De Novo Design, Virtual Screening, Molecular Dynamics, and Retrosynthetic Strategies
Geleverya Anna, Kyrychenko Alexander, Ivanov Volodymyr V., Yevsieieva Larysa V., Fetyukhin Volodymyr, Kovalenko Sergiy M. & Kalugin Oleg N. (2026) Polycyclic Aromatic Compounds
Crossref
Data-driven discovery of functional materials: LARS–LASSO logistic regression for QSAR/QSPR design of compounds with anti-COVID-19 and other activities
Berdnyk M. I., Anokhin D. O., Khristenko I. V., Ivanov V. V., Kovalenko S. M. & Kalugin O. N. (2025) Functional Materials
Crossref
