Eltrombopag is a potential target for drug intervention in SARS-CoV-2 spike protein.

The COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), is a current global threat for which there is an urgent need to search for an effective therapy. The transmembrane spike (S) glycoprotein of SARS-CoV-2 directly binds to the host angiotensin-converting enzyme 2 (ACE2) and mediates viral entrance, which is therefore considered as a promising drug target. Considering that new drug development is a time-consuming process, drug repositioning may facilitate rapid drug discovery dealing with sudden infectious diseases. Here, we compared the differences between the virtual structural proteins of SARS-CoV-2 and SARS-CoV, and selected a pocket mainly localizing in the fusion cores of S2 domain for drug screening. A virtual drug design algorithm screened the Food and Drug Administration-approved drug library of 1234 compounds, and 13 top scored compounds were obtained through manual screening. Through in vitro molecular interaction experiments, eltrombopag was further verified to possess a high binding affinity to S protein plus human ACE2 and could potentially affect the stability of the ACE2-S protein complex. Hence, it is worth further exploring eltrombopag as a potential drug for the treatment of SARS-CoV-2 infection.

Molecular Docking Study on Several Benzoic Acid Derivatives against SARS-CoV-2.

Several derivatives of benzoic acid and semisynthetic alkyl gallates were investigated by an in silico approach to evaluate their potential antiviral activity against SARS-CoV-2 main protease. Molecular docking studies were used to predict their binding affinity and interactions with amino acids residues from the active binding site of SARS-CoV-2 main protease, compared to boceprevir. Deep structural insights and quantum chemical reactivity analysis according to Koopmans' theorem, as a result of density functional theory (DFT) computations, are reported. Additionally, drug-likeness assessment in terms of Lipinski's and Weber's rules for pharmaceutical candidates, is provided. The outcomes of docking and key molecular descriptors and properties were forward analyzed by the statistical approach of principal component analysis (PCA) to identify the degree of their correlation. The obtained results suggest two promising candidates for future drug development to fight against the coronavirus infection.