Modern computational chemistry and drug discovery: structure generating programs

Area of Science:

• Medicinal Chemistry
• Computational Drug Design
• Structural Biology

Background:

• Enzyme inhibitors are crucial for therapeutic interventions.
• Traditional drug design often relies on modifying existing compounds.
• The need for novel chemical structures with high binding affinity is persistent.

Purpose of the Study:

• To highlight the emergence and application of de novo design methods in creating novel enzyme inhibitors.
• To demonstrate the utility of de novo approaches in enhancing the binding affinity of existing drugs, such as HIV protease inhibitors.
• To outline ongoing advancements in de novo design, focusing on generating synthetically feasible and complementary molecular structures for specific binding sites.

Main Methods:

• De novo computational methods for designing novel molecular structures.
• Structure-based drug design principles to complement binding sites.
• Development of algorithms for generating synthetically feasible molecules.
• Creation of virtual molecular libraries for drug candidate screening.

Main Results:

• The first reports of active enzyme inhibitors designed entirely by de novo methods emerged in 1996-1997.
• De novo design successfully improved the binding affinity of an HIV protease inhibitor.
• Algorithms capable of generating synthetically feasible structures have been developed.
• Methods for automated generation of virtual libraries for pharmacophore matching are under development.

Conclusions:

• De novo design represents a powerful strategy for discovering novel enzyme inhibitors.
• Computational approaches are significantly advancing the optimization of drug candidates.
• The development of de novo algorithms enhances the efficiency and feasibility of drug discovery pipelines.