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BACKGROUND The vast geographical expansion of novel coronavirus and an increasing number of COVID-19 affected cases has overwhelmed health and public health services. AI and ML algorithms have extended its major role in tracking the disease patterns, and in identifying possible treatment of disease. OBJECTIVE To identify potential COVID-19 protease inhibitor through shape-based Machine Learning assisted by Molecular docking and Molecular Dynamics simulation. METHODS 31 repurposed compounds have been selected targeting coronavirus protease (6LU7) and a machine learning approach was employed to generate shape-based molecules starting from 3D shape to pharmacophoric features of its seed compound. Ligand-Receptor docking was performed with Optimized Potential for Liquid Simulations (OPLS3) algorithms to identify high-affinity compounds from the list of selected candidates for 6LU7. This compound was subjected to Molecular Dynamic Simulations followed by ADMET studies and other analysis. RESULTS Shape-based Machine learning reported Remdesivir, Valrubicin, Aprepitant, and Fulvestrant and a novel therapeutic compound as the best therapeutic agents with the highest affinity for its target protein. Among the best shape-based compounds, the novel theoretical compound was not indexed in any chemical databases (PubChem, Zinc, or ChEMBL). Hence, the novel compound was named \'nCorvEMBS\'. Further, toxicity analysis showed nCorv-EMBS to be efficacious that can be qualified as a 6LU7 inhibitor in COVID-19. CONCLUSION An effective ACE-II, GAK, AAK1, and protease 3C blockers that can serve a novel therapeutic approach to block the binding and attachment of COVID-19 protein (PDB ID: 6LU7) to the host cell and thus inhibit the infection at AT2 Lung cells. The novel theoretical compound nCorv-EMBS herein proposed stands as a promising inhibitor that can be extended for entering phases of clinical trials for COVID-19 treatment.
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