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Discovery of Potent Covid-19 Main Protease Inhibitors Using Machine Learning Based Virtual Screening Strategy

preprint
submitted on 07.04.2020 and posted on 10.04.2020 by Muthu Kumar T, Rohini K, Nivya James, Shanthi V, Ramanathan Karuppasamy

The emergence and rapid spreading of novel SARS-CoV-2 across the globe represent an imminent threat to public health. Novel antiviral therapies are urgently needed to overcome this pandemic. Given the great role of main protease of Covid-19 for virus replication, we performed drug repurposing study using recently deposited main protease structure, 6LU7. For instance, pharmacophore- and e-pharmacophore-based hypotheses such as AARRH and AARR respectively were developed using available small molecule inhibitors and utilized in the screening of DrugBank repository. Further, hierarchical docking protocol was implemented with the support of Glide algorithm. The resultant compounds were then examined for its binding free energy against main protease of Covid-19 by means of Prime-MM/GBSA algorithm. Most importantly, the resultant compounds antiviral activities were further predicted by machine learning-based model generated by AutoQSAR algorithm. Finally, the hit molecules were examined for its drug likeness and its toxicity parameters through QikProp algorithm. Overall, the present analysis yielded four potential inhibitors (DB07800, DB08573, DB03744 and DB02986) that are predicted to bind with main protease of Covid-19 better than currently used drug molecules such as N3 (co-crystallized native ligand), Lopinavir and Ritonavir.

History

Email Address of Submitting Author

kramanathan@vit.ac.in

Institution

Vellore Institute of Technology

Country

India

ORCID For Submitting Author

0000-0002-1613-0729

Declaration of Conflict of Interest

No conflict of interest

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