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The present study aimed to assess the repurposing potential of existing antiviral drug candidates (FDA approved and investigational) against SARS-CoV-2 target proteins that facilitates viral entry and replication into the host body. To evaluate molecular affinities between antiviral drug candidates and SARS-CoV-2 associated target proteins such as spike protein (S) and main protease (Mpro ), a molecular interaction simulation was performed using MD software and subsequently the applicability score was calculated by machine learning algorithms. Furthermore, the STITCH algorithm was used to predict the pharmacology network involving multiple pathways of active drug candidate(s). Pharmacophores feature of active drug(s) molecules was also determined to predict structure activity relationship. The molecular interaction analysis showed that cordycepin has strong binding affinities with S protein (-180) and Mpro proteins (-205) which were relatively highest among other drug candidates used. Interestingly, compounds with low IC50 showed high binding energy. Furthermore, machine learning algorithm also revealed high applicability scores (0.42-0.47) of cordycepin. It is worth mentioning that the pharmacology network depicted the involvement of cordycepin in different pathways associated with bacterial and viral diseases including tuberculosis, hepatitis B, influenza A, viral myocarditis and herpes simplex infection. The embedded pharmacophore features with cordycepin also suggested strong structure-activity relationship (SAR). Cordycepin\'s anti-SARS-CoV-2 activity indicated 65% (E-gene) and 42% (N-gene) viral replication inhibition after 48h of treatment. Since cordycepin has both pre-clinical and clinical evidence on antiviral activity, in addition the present findings further validate and suggest repurposing potential of cordycepin against COVID-19.
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Chemical_biology_&_drug_design
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Repurposing potential of FDA approved and investigational drugs for COVID-19 targeting SARS-CoV-2 spike and main protease and validation by machine learning algorithm.
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