The worldwide burden of coronavirus disease 2019 (COVID-19) is still unremittingly prevailing, with more than 440 million infections and over 6 million deaths documented so far since the onset of the SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) pandemic. The non-availability of treatment further aggravates the scenario, thereby demanding the exploration of pre-existing FDA-approved drugs for their effectiveness against COVID-19. <i>In silico</i>-based non-testing approaches are promising tools in determining anti SARS-CoV-2 structural features and prediction of antiviral activity of existing and new drug candidates. The current research aims at identifying potential anti SARS-CoV-2 drugs using computational approach. In the present study, we have collected a set of 44 FDA approved drugs of different classes from a previously published literature by Jeon et al. (2020) [1] with their potential antiviral activity against COVID-19 disease. The work proceeds with initial development of multiple consensus QSAR models for identification of important chemical features essential for antiviral activity. This proceeds with similarity based Chemical Read-Across prediction using the Read-Across-v3.1 tool available from https://sites.google.com/jadavpuruniversity.in/dtc-lab-software/home. The structural features established from priorly developed QSAR models were used in the read-across prediction. The different similarity-based estimation techniques involving Euclidean distance, Gaussian kernel function, and Laplacian kernel function were used in read-across algorithm which helped in providing improved predictions of the query compounds. The models developed were scrutinized using various stringent validation criteria. Further, we have predicted a true external set of pharmaceuticals using the developed models for their probable anti-COVID activity. These compounds were further checked for their prediction reliability using the “Prediction Reliability Indicator” tool available fromhttps://dtclab.webs.com/software-tools.

Computational repurposing FDA approved drugs as antiviral drugs candidates against SARS-CoV-2: a similarity-based Quantitative Read Across (QRA) approach

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ACS Fall 2022

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