Pharmacophore modeling and parallel screening for PPAR ligands

Area of Science:

• Computational chemistry
• Pharmacology
• Drug discovery

Background:

• Peroxisome proliferator-activated receptors (PPARs) are crucial drug targets.
• Developing accurate pharmacophore models and screening approaches is vital for identifying PPAR ligands.

Purpose of the Study:

• To generate and validate pharmacophore models for PPARs.
• To assess the efficacy of a parallel screening approach in predicting the correct pharmacological target for compounds.
• To determine the best models for PPAR-alpha, PPAR-delta, and PPAR-gamma agonists.

Main Methods:

• Generation and validation of 48 PPAR pharmacophore models.
• Screening of 357 known PPAR ligands against these models.
• Large-scale parallel screening against a database of 1537 structure-based models.
• Categorization of models into 181 protein targets and development of a target ranking score.

Main Results:

• The parallel screening approach successfully enriched PPAR ligands within PPAR hypotheses.
• PPAR targets were ranked first more frequently than other targets for the tested ligands.
• The study identified optimal models for PPAR-alpha, PPAR-delta, and PPAR-gamma agonists.

Conclusions:

• Parallel screening is an effective strategy for predicting the correct pharmacological target of compounds.
• Validated pharmacophore models and the parallel screening approach can accelerate drug discovery for PPARs.