Integrating Physics-Based Simulations with Data-Driven Deep Learning Represents a Robust Strategy for Developing Inhibitors Targeting the Main Protease.

Journal: Journal of chemical information and modeling
Published Date:

Abstract

The coronavirus main protease, essential for viral replication, is a well-validated antiviral target. Here, we present Deep-CovBoost, a computational pipeline integrating deep learning with free energy perturbation (FEP) simulations to guide the structure-based optimization of inhibitors targeting the coronavirus main protease. Starting from a reported noncovalent inhibitor, the pipeline generated and prioritized analogs using predictive modeling, followed by rigorous validation through FEP and molecular dynamics simulations. This approach led to the identification of optimized compounds (e.g., I3C-1, I3C-2, I3C-35) that enhance binding affinity by engaging the underexploited S4 and S5 subpockets. These results highlight the potential of combining physics-based and AI-driven approaches to accelerate lead optimization and antiviral design.

Authors

  • Yanqing Yang
    Department of Anesthesiology, Taizhou Hospital, Linhai, China.
  • Yangwei Jiang
    Institute of Quantitative Biology, College of Life Sciences, Zhejiang University, Hangzhou, Zhejiang 310058, China.
  • Dong Zhang
    Institute of Acoustics, Nanjing University, Nanjing 210093, China.
  • Leili Zhang
    IBM Thomas J. Watson Research Center, Yorktown Heights, NY 10598USA.
  • Ruhong Zhou
    ZheJiang University, 688 Yuhangtang Road, Hangzhou, 310027, China.

Keywords

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