Electrostatic Complementarity in Structure-Based Drug Design.

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Electrostatic complementarity is crucial for enhancing protein-ligand binding affinity in structure-based drug discovery.
• Optimizing these interactions can lead to substantial improvements in drug efficacy and selectivity.

Purpose of the Study:

• To review examples of optimizing electrostatic complementarity for improved drug properties.
• To highlight the correlation between electrostatic potential (ESP) surfaces and drug potency.
• To discuss the potential of deep neural networks in making ESP calculations more accessible.

Main Methods:

• Review of case studies demonstrating optimization of protein-ligand and intramolecular electrostatic interactions.
• Retrospective analysis of factor Xa inhibitors correlating potency with calculated ESP surfaces.
• Discussion of recent advancements in graph-convolutional deep neural networks for ESP surface generation.

Main Results:

• Optimization of electrostatic complementarity yielded up to 250-fold improvements in target affinity.
• Significant improvements were observed in physicochemical properties, in vitro properties, and off-target selectivity.
• An 8000-fold range in potency for factor Xa inhibitors was correlated with ESP surfaces.

Conclusions:

• Optimizing electrostatic complementarity is a validated strategy for improving drug discovery outcomes.
• ESP surface analysis is a valuable tool for drug design, with AI enhancing its accessibility.
• Further application of these methods promises to accelerate the development of more effective therapeutics.