Computational Strategies for Discovering Inhibitors Targeting Therapeutic Enzymes

Area of Science:

• Computational chemistry
• Drug discovery
• Medicinal chemistry

Background:

• Computational strategies significantly accelerate drug discovery.
• Algorithms exist for target identification through to hit molecule discovery.
• These methods reduce cost and time while improving prediction accuracy.

Purpose of the Study:

• To provide a comprehensive procedure for discovering drug inhibitors.
• To combine structure-based and ligand-based computational approaches.
• To guide the use of computational platforms for successful drug discovery.

Main Methods:

• Target identification via comparative genomics and network-based approaches.
• AI-based molecular modeling and ligand-based pharmacophore modeling.
• Structure-based molecular docking, virtual screening, molecular dynamics, and free energy calculations.

Main Results:

• The discussed methods enable efficient identification of potential drug targets.
• Virtual screening of large compound libraries can be performed effectively.
• Accurate prediction of drug-target interactions and inhibitor efficacy is enhanced.

Conclusions:

• This work serves as a guide for employing computational platforms in drug discovery.
• Combining diverse computational methods optimizes the inhibitor discovery process.
• Successful drug discovery relies on the effective utilization of advanced computational tools.