Analysis of neighborhood behavior in lead optimization and array design.

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Neighborhood behavior quantifies the impact of small structural changes on molecular properties.
• Accurate neighborhood behavior assessment is crucial for lead optimization in drug design.
• Existing methods require evaluation for their effectiveness in describing these property changes.

Purpose of the Study:

• To evaluate and compare two distinct methods for quantifying neighborhood behavior: the optimal diagonal method and the optimality criterion method.
• To assess the performance of these methods using diverse molecular fingerprints and real-world screening data.
• To determine which method provides a more accurate description of neighborhood behavior for chemical array design during lead optimization.

Main Methods:

• Evaluation of the optimal diagonal method and the optimality criterion method.
• Utilized twelve different 2D and 3D molecular fingerprints.
• Employed screening data from GlaxoSmithKline lead optimization projects, considering biological activity, metabolic stability, permeability, and lipophilicity.

Main Results:

• Both methods were assessed using various fingerprints and drug-like properties.
• The optimality criterion method demonstrated a potentially superior quantitative description of neighborhood behavior.
• Findings are relevant for designing effective chemical arrays in drug discovery.

Conclusions:

• The optimality criterion method may offer a more accurate representation of neighborhood behavior compared to the optimal diagonal method.
• This research aids in selecting appropriate methods for quantifying molecular property changes during lead optimization.
• Improved quantification of neighborhood behavior can enhance the efficiency of chemical array design and drug development.