Significance 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. 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 unprecedentedly high hit rate, leading to successful identification of 15 potent inhibitors of SARS-CoV-2 main protease (M pro ) from 25 computationally selected drugs under a threshold of K i = 4 μM. 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. The 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 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 (M pro ). The accurate FEP-ABFE predictions were based on the use of a restraint energy distribution (RED) function, making the practical FEP-ABFE−based virtual screening of the existing drug library possible. As a result, out of 25 drugs predicted, 15 were confirmed as potent inhibitors of SARS-CoV-2 M pro . The most potent one is dipyridamole (inhibitory constant K i = 0.04 µM) which has shown promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (K i = 0.36 µM) and chloroquine (K i = 0.56 µM) were also found to potently inhibit SARS-CoV-2 M pro . We anticipate that the FEP-ABFE prediction-based virtual screening approach will be useful in many other drug repurposing or discovery efforts.
【저자키워드】 Drug repurposing, SARS-CoV-2, main protease, Virtual screening, free energy perturbation, 【초록키워드】 COVID-19, Treatment, coronavirus, Chloroquine, Hydroxychloroquine, COVID-19 pandemic, drugs, drug, protease, outcome, binding free energy, SARS-CoV-2 main protease, Computational methods, antiviral agent, threshold, distribution, disease, clinical study, acute respiratory syndrome, inhibitors of SARS-CoV-2, experimental validation, M pro, effort, therapeutic effect, treat, drug library, inhibitory, Therapeutic treatment, approach, shown, predicted, identify, caused, conducted, demonstrated, accelerated, drugs targeting, inhibit SARS-CoV-2, other disease, patients with COVID-19, selected drug, Significance, 【제목키워드】 drug, SARS-CoV-2 main protease, inhibitor, accelerated,