Abstract
Papain-like protease (PL(pro)) is critical to COVID-19 infection. Therefore, it is a significant target protein for drug development. We virtually screened a 26,193 compound library against the PL(pro) of SARS-CoV-2 and identified several drug candidates with convincing binding affinities. The three best compounds all had better estimated binding energy than those of the drug candidates proposed in previous studies. By analyzing the docking results for the drug candidates identified in this and previous studies, we demonstrate that the critical interactions between the compounds and PL(pro) proposed by the computational approaches are consistent with those proposed by the biological experiments. In addition, the predicted binding energies of the compounds in the dataset showed a similar trend as their IC(50) values. The predicted ADME and drug-likeness properties also suggested that these identified compounds can be used for COVID-19 treatment.