Using a staged multi-objective optimization approach to find selective pharmacophore models.

Area of Science:

• Computational chemistry
• Medicinal chemistry
• Pharmacology

Background:

• Distinguishing between closely related G-protein coupled receptors (GPCRs) and their subtypes is a significant challenge in ligand-based drug design.
• Existing methods often struggle when ligands do not fully discriminate between receptor subtypes or when no prior specificity information is available.

Purpose of the Study:

• To introduce GALAHAD, a novel computational tool designed to improve the differentiation of related GPCRs and their subtypes.
• To demonstrate GALAHAD's utility in generating informative ligand alignments for drug design, even with limited or ambiguous data.

Main Methods:

• GALAHAD employs a multi-objective scoring system to create multiple ligand alignments.
• Alignments balance the minimization of internal strain with the maximization of pharmacophoric and steric (pharmacomorphic) concordance.
• The approach does not require pre-existing receptor subtype specificity information to bias the alignment.

Main Results:

• The tool generates diverse ligand overlays, each potentially corresponding to different receptor subtypes.
• Examination of these overlays allows for the association of specific ligand conformations with distinct receptor subtypes.
• The method successfully illustrated subtype differentiation using a set of dopaminergic agonists with varying D1 vs. D2 receptor selectivity.

Conclusions:

• GALAHAD offers a powerful approach for identifying discriminating models in GPCR drug design.
• The software facilitates subtype identification by analyzing ligand alignments that account for structural and pharmacophoric variations.
• This method enhances the ability to design ligands with improved selectivity for specific GPCR subtypes.