Discovering highly selective and diverse PPAR-delta agonists by ligand based machine learning and structural modeling

Sci Rep. 2019 Jan 31;9(1):1106. doi: 10.1038/s41598-019-38508-8.


PPAR-δ agonists are known to enhance fatty acid metabolism, preserving glucose and physical endurance and are suggested as candidates for treating metabolic diseases. None have reached the clinic yet. Our Machine Learning algorithm called "Iterative Stochastic Elimination" (ISE) was applied to construct a ligand-based multi-filter ranking model to distinguish between confirmed PPAR-δ agonists and random molecules. Virtual screening of 1.56 million molecules by this model picked ~2500 top ranking molecules. Subsequent docking to PPAR-δ structures was mainly evaluated by geometric analysis of the docking poses rather than by energy criteria, leading to a set of 306 molecules that were sent for testing in vitro. Out of those, 13 molecules were found as potential PPAR-δ agonist leads with EC50 between 4-19 nM and 14 others with EC50 below 10 µM. Most of the nanomolar agonists were found to be highly selective for PPAR-δ and are structurally different than agonists used for model building.

MeSH terms

  • Databases, Protein*
  • Drug Evaluation, Preclinical
  • Humans
  • Machine Learning*
  • Metabolic Diseases / drug therapy
  • Metabolic Diseases / metabolism
  • Molecular Docking Simulation*
  • PPAR delta / agonists*
  • PPAR delta / chemistry*
  • PPAR delta / metabolism


  • PPAR delta