Weijun Zhou, Angel D′Oliviera, Xuhang Dai, Jeffrey S. Mugridge, and Yingkai Zhang
ACS Chemical Biology 2026
DOI: 10.1021/acschembio.6c00120
The SARS-CoV-2 main protease (Mpro, also known as 3CLpro) is an attractive antiviral drug target due to its essential role in viral replication and absence of human homologues. Development of new coronavirus-specific Mpro inhibitors will be important as SARS-CoV-2 continues to evolve. Leveraging the rapidly expanding pool of diverse, experimental Mpro-inhibitor data, we developed a target-specific deep learning workflow to accelerate the discovery of new Mpro 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 Mpro bound to A02 confirmed covalent modification of the catalytic Mpro cysteine (C145), unique engagement of the underutilized Mpro S3′ pocket, and the potential for derivatives of this scaffold to interact with additional Mpro 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.
