Structure-based fragment hopping for lead optimization using predocked fragment database.

Area of Science:

• Computational chemistry and drug discovery.
• Structure-based drug design.
• Medicinal chemistry optimization.

Background:

• De novo drug design aims to create novel molecules with desired properties.
• Optimizing lead compounds is crucial for developing effective therapeutics.
• Fragment-based approaches offer a modular strategy for molecular design.

Purpose of the Study:

• To introduce and validate LeadOp, a structure-based de novo optimization process.
• To demonstrate the ability of LeadOp to improve ligand-receptor binding interactions.
• To systematically develop optimized analogs for drug candidates.

Main Methods:

• Decomposition of compounds into molecular fragments based on chemical or user-defined rules.
• Evaluation of fragments using a predocked fragment database and group efficiency.
• Iterative replacement of low-contribution fragments with high-affinity binders.
• Reassembly of optimized fragments to generate new ligand structures.

Main Results:

• The LeadOp methodology successfully optimized query molecules in tested biomolecular systems.
• Improved analogs were systematically developed for mutant B-Raf kinase and human 5-lipoxygenase.
• Fragment replacement strategy enhanced ligand binding to target proteins.

Conclusions:

• LeadOp provides an effective structure-based de novo approach for lead optimization.
• The method facilitates the systematic improvement of compound activity by targeted fragment modification.
• LeadOp demonstrates potential for accelerating drug discovery and development.