Docking and other computing tools in drug design against SARS-CoV-2.

Area of Science:

• Computational chemistry and pharmacology
• Drug discovery and development
• Virology and infectious diseases

Background:

• Computer simulations are essential for identifying drugs against SARS-CoV-2.
• Extensive literature exists on molecular modeling for predicting inhibitors against SARS-CoV-2 target proteins.
• This review focuses on studies using computational methods to find inhibitors and experimentally test them.

Purpose of the Study:

• To review computational methods for identifying SARS-CoV-2 inhibitors.
• To highlight studies combining computational predictions with experimental validation.
• To provide an overview of simulation techniques and relevant compound databases.

Main Methods:

• Literature review of computational drug discovery studies for SARS-CoV-2.
• Analysis of various molecular modeling techniques: docking, molecular dynamics, quantum mechanics, machine learning.
• Inclusion of studies with experimental validation (protein assays, cell culture) and notable computational works.

Main Results:

• Docking is the most prevalent computational method, often followed by other simulations for refinement.
• A variety of molecular modeling approaches are employed to predict potential antiviral compounds.
• Databases for virtual screening of organic compounds are also discussed.

Conclusions:

• Computational methods, particularly docking, are vital in the search for SARS-CoV-2 inhibitors.
• Integrating computational predictions with experimental validation accelerates drug discovery.
• The review provides a structured overview of current computational strategies and resources for antiviral drug development.