Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro via physics- and knowledge-based approaches

Area of Science:

• Computational chemistry
• Drug discovery
• Virology

Background:

• SARS-CoV-2 main protease (Mpro) is a key target for antiviral drug development.
• Identifying potent Mpro inhibitors is crucial for blocking viral replication.
• Computational approaches accelerate the search for effective drug candidates.

Purpose of the Study:

• To review recent computational methods for identifying SARS-CoV-2 Mpro inhibitors.
• To highlight the strengths and limitations of various computational techniques.
• To emphasize the role of integrated computational strategies in drug discovery.

Main Methods:

• Physics- and knowledge-based computational approaches.
• Molecular docking and molecular dynamics (MD) simulations for binding affinity prediction.
• Quantitative structure-activity relationship (QSAR) for large-scale screening.
• Quantum mechanics/molecular mechanics (QM/MM) for covalent inhibitors.
• Machine learning (ML) models for enhanced prediction accuracy.

Main Results:

• Various computational methods effectively predict ligand-binding affinity to SARS-CoV-2 Mpro.
• Molecular docking and MD simulations are commonly combined for high-throughput screening.
• QSAR offers a computationally inexpensive method for large ligand sets.
• QM/MM is suitable for analyzing covalent interactions.
• ML models significantly improve the accuracy of binding affinity predictions.

Conclusions:

• Computational approaches, particularly ML integration, are vital for discovering SARS-CoV-2 Mpro inhibitors.
• Combining different computational methods provides robust insights into ligand-protein interactions.
• Accelerated identification of potential inhibitors can expedite the development of new antiviral therapies.