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

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.