A Novel Covalent Inhibitor Fragment for the SARS-CoV-2 Main Protease Identified by Target-Specific Deep Learning

The SARS-CoV-2 main protease (M^pro, also known as 3CL^pro) is an attractive antiviral drug target due to its essential role in viral replication and absence of human homologues. Development of new coronavirus-specific M^pro inhibitors will be important as SARS-CoV-2 continues to evolve. Leveraging the rapidly expanding pool of diverse, experimental M^pro-inhibitor data, we developed a target-specific deep learning workflow to accelerate the discovery of new M^pro inhibitor compounds and fragment-like starting points. This workflow combined a fine-tuned inhibitor prediction model with solubility (logS) and lipophilicity (logP) models, molecular similarity analysis, and literature mining to prioritize novel, drug-like candidates. Applied to a purchasable library of over 500,000 compounds, the approach rapidly identified 24 candidates for experimental testing. Biochemical assays revealed a novel, small covalent inhibitor fragment (A02) with an apparent IC₅₀ of 1.5 μM, prior to any synthetic optimization or derivatization. A 1.76 Å crystal structure of M^pro bound to A02 confirmed covalent modification of the catalytic M^pro cysteine (C145), unique engagement of the underutilized M^pro S3' pocket, and the potential for derivatives of this scaffold to interact with additional M^pro pockets in future optimization efforts. Together, these results demonstrate the potential for target-specific deep learning approaches to guide the rapid screening and discovery of new inhibitor leads or drug scaffolds.