Discovery of Highly Potent Noncovalent Inhibitors of SARS-CoV-2 Main Protease through Computer-Aided Drug Design
The COVID-19 pandemic has highlighted a clear need to ensure rapid and equitable global access to health interventions in preparation for future coronavirus-driven pandemics. Here, we report the discovery of highly potent noncovalent inhibitors of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro) with pan-coronavirus (pan-CoV) Mpro inhibition through computer-aided drug design. Virtual screening led to the identification of a noncovalent hit compound with a piperazine core. Structure-guided scaffold morphing provided a novel trisubstituted piperidine core. Free energy perturbation (FEP)-guided designs, with induced-fit of Met49/Met165 and Gln189, resulted in the identification of highly potent compound <b>30</b>, which exhibits pan-CoV Mpro inhibition and cellular antiviral efficacy against the SARS-CoV-2 omicron variant. The optimized lead compound <b>30</b> was characterized by in vitro ADME/Tox assays and in vivo mouse pharmacokinetics. These findings suggest that compound <b>30</b> could be an addition to the repertoire of tools used to support future pandemic preparedness.
Authors
- Leah L. Frye
- Jumpei Aida (ORCID: https://orcid.org/0000-0002-7546-1915)
- Jangir Selimkhanov
- Abba E. Leffler (ORCID: https://orcid.org/0009-0004-5444-013X)
- Edcon Chang
- Atsuko Ochida
- Terufumi Takagi
- Mallareddy Komandla
- Atsutoshi Okabe
- Kazuko Yonemori
- Douglas R. Dougan
- Daniel W. Carney (ORCID: https://orcid.org/0000-0003-2741-8899)
- Thamina Akther (ORCID: https://orcid.org/0000-0003-2166-1900)
- Jeffrey A. Bell (ORCID: https://orcid.org/0000-0002-8310-0183)
- Michiko Tawada (ORCID: https://orcid.org/0000-0002-1349-0059)
- Kumar Singh Saikatendu (ORCID: https://orcid.org/0000-0001-6734-237X)
- Eileen Hickey (ORCID: https://orcid.org/0000-0002-4868-630X)
- Will Tao
Institutions
- Schrodinger (United States) (US)
- Takeda (United States) (US)
- Takeda (Japan) (JP)
Publication Details
- Journal
- Journal of Medicinal Chemistry
- Published
- 2025-10-12
- DOI
- https://doi.org/10.1021/acs.jmedchem.5c01199
- Citations
- 1
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 1.50