The QSAR-search of effective agents towards coronaviruses applying the Monte Carlo method

ABSTRACT

Perhaps there is some similarity between the coronavirus of 2017 and the COVID-19. Consequently, a predictive model for the antiviral activity for the Middle East respiratory syndrome coronavirus (MERS‐CoV, 2017) could be useful for designing the strategy and tactics in the struggle with coronaviruses in general and with COVID 19 in particular. Quantitative structure-activity relationships (QSARs) of inhibitory activity to MERS-CoV were developed. The index of ideality of correlation was applied to build up these models for the antiviral activity. The statistical quality of the best model is quite good (r² = 0.84). A mechanistic interpretation of these models based on the molecular features with strong positive (i.e. promoters for endpoint increase) and strong negative (i.e. promoters for endpoint decrease) influence on the inhibitory activity is suggested. A collection of possible biologically active compounds, constructed using data on the above molecular features which are statistically reliable promoters of increase or decrease of the activity, is presented.

KEYWORDS:

• Coronavirus
• antiviral activity
• QSAR
• Monte Carlo method
• bioinformatics
• index of ideality of correlation

Acknowledgements

The authors are grateful to the project LIFE-VERMEER (LIFE16 ENV/IT/000167) for the support.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2021.1952649.

Additional information

Funding

This work was supported by the LIFE-VERMEER [LIFE16 ENV/IT/000167].