Multi-reference poly-conformational computational methods for de-novo design, optimization, and repositioning of pharmaceutical compounds

Abstract

The COVID-19 epidemic, SARS-CoV-2, that began in December of 2019 has drastically altered the aspects of daily life across the global society. Time-effective treatment of those infected has since become a major goal with multiple treatment strategies having been designed to prevent the progression of the disease into severe pneumonia. To date, no drug has been found to be 100% effective against SARS-COV-2, possibly because each candidate drug was targeting only one particular mechanism of action (MoA). Neither proposed up-to-date anti-SARS-COV-2 vaccine are 100% effective. To contribute to the process of finding a more robust small-molecule solution, utilizing several anti-SARS-COV-2 MoAs, a novel framework is presented; where the in silico generated set of virtual library compounds is compared to six known reference drugs: Chloroquine, Favipiravir, Remdesivir, JQ1, Apicidine, and Haloperidol which have been already used for SARS-CoV-2 treatment. The aims were: a) to present a universal search framework for potential candidate compounds based on the comparison of multiple similarities between compounds’ conformers and b) to identify candidate compounds that are simultaneously “close” to each of the six known reference compounds that counteract SARS-CoV-2 via different mechanisms of action.