Structure-based pharmacophore modeling 2. Developing a novel framework for structure-based pharmacophore model...

Area of Science:

• Computational chemistry
• Drug discovery
• Structural biology

Background:

• Pharmacophore models are crucial for identifying drug candidates targeting G protein-coupled receptors (GPCRs).
• Many GPCRs lack known ligands or structures, hindering traditional pharmacophore model generation.
• Existing methods struggle with generating accurate models for these challenging targets.

Purpose of the Study:

• To develop a robust structure-based pharmacophore modeling approach for GPCRs.
• To address the challenge of selecting effective pharmacophore models.
• To improve ligand identification for GPCR drug development.

Main Methods:

• Utilized Multiple Copy Simultaneous Search (MCSS) to place fragments and generate structure-based pharmacophore models.
• Applied a cluster-then-predict machine learning workflow for pharmacophore model selection.
• Generated models using both experimentally determined and homology-modeled GPCR structures.

Main Results:

• Generated pharmacophore models for 13 class A GPCRs, achieving high enrichment factors in ligand database searches.
• The machine learning classifier demonstrated strong performance in selecting high-enrichment pharmacophore models.
• Achieved positive predictive values of 0.88 and 0.76 for models from experimental and modeled structures, respectively.

Conclusions:

• The proposed structure-based pharmacophore modeling approach effectively generates high-quality models for GPCRs.
• The machine learning-based selection workflow significantly improves the identification of relevant pharmacophore models.
• This method offers a valuable tool for GPCR-focused drug discovery, even with limited structural data.