AI Article Synopsis
- The chapter discusses methods in predictive toxicology and computer-aided drug design, focusing on adverse outcome pathways, pharmacophore modeling, docking, and 3D QSAR analysis.
- These methods are applied to explore interactions between PPARγ (a receptor involved in metabolism) and its ligands, specifically for predicting hepatotoxicity of full agonists.
- Additionally, the integrated approach aids in identifying natural antidiabetic compounds that may work as partial agonists through PPARγ.
Article Abstract
The chapter is focused on methods relevant for predictive toxicology and computer-aided drug design (adverse outcome pathway development, pharmacophore modeling, docking, and 3D QSAR analysis) and applied to study interactions between peroxisome proliferator-activated receptor γ (PPARγ) and its ligands. The methods have been combined to develop an integrated in silico approach allowing both to predict potential PPARγ-mediated hepatotoxicity of receptor's full agonists, thus supporting hazard characterization, and to identify naturally derived antidiabetic triterpenoids potentially acting through PPARγ partial agonism.
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| http://dx.doi.org/10.1007/978-1-4939-9195-2_22 | DOI Listing |
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