Identifying Activity Cliff Generators of PPAR Ligands Using SAS Maps

Area of Science:

• Medicinal Chemistry
• Computational Chemistry
• Drug Discovery

Background:

• Structure-activity relationships (SAR) are crucial for identifying drug candidates and optimizing leads.
• Activity cliffs, pairs of similar molecules with large potency differences, offer valuable SAR insights.
• Systematic identification of molecular structures prone to forming activity cliffs is needed.

Purpose of the Study:

• Introduce the concept of an "activity cliff generator"—a molecular structure likely to form activity cliffs.
• Identify and analyze activity cliff generators in a dataset of peroxisome-proliferator-activated receptor (PPAR) modulators.
• Investigate the structural basis of activity cliffs in PPAR subtypes.

Main Methods:

• Utilized Structure-Activity Similarity maps to analyze a dataset of 168 compounds against three PPAR subtypes (PPARα, PPARδ, PPARγ).
• Defined and identified single-target and dual-target activity cliff generators.
• Employed molecular docking calculations to explore ligand-enzyme interactions and identify potential "hot spots" responsible for activity cliffs.

Main Results:

• Identified specific molecular structures as activity cliff generators within the PPAR dataset.
• Discovered single-target and dual-target activity cliff generators for PPARα and PPARδ.
• Docking studies suggested a protein "hot spot" implicated in the formation of activity cliffs and its role in ligand binding.

Conclusions:

• Activity cliff generators are valuable tools for understanding SAR and guiding drug design.
• The identified cliff generators and potential hot spots provide insights into PPAR subtype selectivity and modulation.
• This approach facilitates the systematic analysis of activity cliffs, enhancing hit identification and lead optimization strategies.