Identify potent SARS-CoV-2 main protease inhibitors via accelerated free energy perturbation-based virtual screening of existing drugs

Abstract

Coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global crisis. There is no therapeutic treatment specific for COVID-19. It is highly desirable to identify potential antiviral agents against SARS-CoV-2 from existing drugs available for other diseases and, thus, repurpose them for treatment of COVID-19. In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a virtual screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. Here we report a new virtual screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions and its use in identifying drugs targeting SARS-CoV-2 main protease (Mpro). The accurate FEP-ABFE predictions were based on the use of a new restraint energy distribution (RED) function designed to accelerate the FEP-ABFE calculations and make the practical FEP-ABFE-based virtual screening of the existing drug library possible for the first time. As a result, out of twenty-five drugs predicted, fifteen were confirmed as potent inhibitors of SARS-CoV-2 Mpro. The most potent one is dipyridamole (Ki=0.04 M) which has showed promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki=0.36 M) and chloroquine (Ki=0.56 M) were also found to potently inhibit SARS-CoV-2 Mpro for the first time. We anticipate that the FEP-ABFE prediction-based virtual screening approach will be useful in many other drug repurposing or discovery efforts. Significance StatementDrug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a virtual screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. It has been demonstrated that a new virtual screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions can reach an unprecedently high hit rate, leading to successful identification of 16 potent inhibitors of SARS-CoV-2 main protease (Mpro) from computationally selected 25 drugs under a threshold of Ki = 4 M. The outcomes of this study are valuable for not only drug repurposing to treat COVID-19, but also demonstrating the promising potential of the FEP-ABFE prediction-based virtual screening approach.