Computational approaches for designing viral protease inhibitors

Area of Science:

• Biochemistry
• Computational Biology
• Drug Discovery

Background:

• Viral proteases are essential enzymes for the replication of numerous viruses, including HIV, HCV, and SARS-CoV-2.
• Developing inhibitors against these viral proteases is a key therapeutic strategy for treating viral infections.
• Resistance to antiviral drugs necessitates continuous research into novel inhibitor designs.

Purpose of the Study:

• To provide a comprehensive review of computational methodologies used in the development of viral protease inhibitors.
• To highlight case studies for major viral targets and discuss strategies for overcoming drug resistance.
• To explore future directions in computational antiviral drug discovery.

Main Methods:

• Structure-based drug design (SBDD)
• Ligand-based drug design (LBDD)
• Machine learning (ML) and artificial intelligence (AI) techniques

Main Results:

• Computational approaches significantly accelerate the discovery and optimization of viral protease inhibitors.
• These methods enable the design of potent inhibitors against a wide range of viral targets.
• Case studies demonstrate the successful application of computational strategies for specific viruses.

Conclusions:

• Computational methodologies are indispensable tools in modern antiviral drug discovery.
• Integrating SBDD, LBDD, ML, and AI offers powerful strategies to combat viral infections.
• Future research should focus on novel computational approaches to address drug resistance and discover next-generation antiviral therapies.