Activity landscape modeling of PPAR ligands with dual-activity difference maps

Area of Science:

• Medicinal Chemistry
• Chemoinformatics
• Pharmacology

Background:

• Peroxisome proliferator-activated receptor (PPAR) agonists are crucial for treating diabetes and metabolic disorders.
• Understanding structure-activity relationships (SAR) is vital for developing selective and dual PPAR agonists.

Purpose of the Study:

• To systematically characterize the SAR of 168 compounds against PPAR subtypes.
• To apply activity landscape modeling and chemoinformatic tools for drug design insights.

Main Methods:

• Screening of 168 compounds against three PPAR subtypes.
• Utilizing consensus dual-activity difference maps based on potency and structure similarity.
• Employing four different 2D and 3D structure representations.

Main Results:

• Identified regions with similar and inverse SAR across PPAR subtypes.
• Discovered single-, dual-, and pan-receptor activity cliffs and scaffold hops.
• Highlighted specific structural features beneficial for designing novel PPAR agonists.

Conclusions:

• Activity landscape modeling provides a powerful approach to navigate complex SAR data.
• The findings offer valuable guidance for the rational design of new PPAR-targeting therapeutics.
• The methodology is generalizable to larger datasets for broader drug discovery applications.