Structure-based pharmacophore modeling 1. Automated random pharmacophore model generation.

Area of Science:

• Computational chemistry
• Drug discovery
• Structural biology

Background:

• Pharmacophores are crucial for virtual screening in drug discovery.
• G protein-coupled receptors (GPCRs) are important drug targets.
• Structure-based pharmacophore models are gaining prominence due to increased GPCR structure availability.

Purpose of the Study:

• To develop a method for generating structure-based pharmacophore models for GPCRs.
• To enable ligand discovery for GPCRs with few known ligands.

Main Methods:

• Generated pharmacophore models within the active sites of 8 class A GPCR crystal structures.
• Utilized automated annotation of functional group fragments.
• Screened 5000 pharmacophores against active and decoy compounds for 30 class A GPCRs.
• Assessed performance using enrichment factor and goodness-of-hit metrics.

Main Results:

• Pharmacophore models achieved the theoretical maximum enrichment factor in 8 out of 8 resolved structures.
• Models also showed high performance in 7 out of 8 homology models.
• The method demonstrated utility in virtual screening for GPCRs.

Conclusions:

• The developed method is effective for generating structure-based pharmacophore models.
• This approach can significantly aid virtual screening efforts in GPCR ligand discovery.
• The method shows promise for targets with limited known ligands.