The spike glycoprotein (S) of the SARS-CoV-2 virus surface plays a key role in receptor binding and virus entry. The S protein uses the angiotensin converting enzyme (ACE2) for entry into the host cell and binding to ACE2 occurs at the receptor binding domain (RBD) of the S protein. Therefore, the protein-protein interactions (PPIs) between the SARS-CoV-2 RBD and human ACE2, could be attractive therapeutic targets for drug discovery approaches designed to inhibit the entry of SARS-CoV-2 into the host cells. Herein, with the support of machine learning approaches, we report structure-based virtual screening as an effective strategy to discover PPIs inhibitors from ZINC database. The proposed computational protocol led to the identification of a promising scaffold which was selected for subsequent binding mode analysis and that can represent a useful starting point for the development of new treatments of the SARS-CoV-2 infection.
Keywords: COVID-19; PPI focused library; QSAR; Virtual screening; docking.
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