Identification of SARS-CoV-2 main protease inhibitors from FDA-approved drugs by artificial intelligence-supported activity prediction system

Although a certain level of efficacy and safety of several vaccine products against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) have been established, unmet medical needs for orally active small molecule therapeutic drugs are still very high. As a key drug target molecule, SARS-CoV-2 main protease (M^pro) is focused and large number of <i>in-silico</i> screenings, a part of which were supported by artificial intelligence (AI), have been conducted to identify M^pro inhibitors both through drug repurposing and drug discovery approaches. In the many drug-repurposing studies, docking simulation-based technologies have been mainly employed and contributed to the identification of several M^pro binders. On the other hand, because AI-guided INTerprotein’s Engine for New Drug Design (AI-guided INTENDD), an AI-supported activity prediction system for small molecules, enables to propose the potential binders by proprietary AI scores but not docking scores, it was expected to identify novel potential M^pro binders from FDA-approved drugs. As a result, we selected 20 potential M^pro binders using AI-guided INTENDD, of which 13 drugs showed M^pro-binding signal by surface plasmon resonance (SPR) method. Six (6) compounds among the 13 positive drugs were identified for the first time by the present study. Furthermore, it was verified that vorapaxar bound to M^pro with a K_d value of 27 µM by SPR method and inhibited virus replication in SARS-CoV-2 infected cells with an EC₅₀ value of 11 µM. Communicated by Ramaswamy H. Sarma