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Identifying Activity Cliff Generators of PPAR Ligands Using SAS Maps
Author(s) -
MéndezLucio Oscar,
PérezVillanueva Jaime,
Castillo Rafael,
MedinaFranco José L.
Publication year - 2012
Publication title -
molecular informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.481
H-Index - 68
eISSN - 1868-1751
pISSN - 1868-1743
DOI - 10.1002/minf.201200078
Subject(s) - cliff , computational biology , virtual screening , cheminformatics , docking (animal) , computer science , peroxisome proliferator activated receptor , chemistry , drug discovery , data mining , combinatorial chemistry , biological system , receptor , biology , biochemistry , computational chemistry , medicine , paleontology , nursing
Structure‐activity relationships (SAR) of compound databases play a key role in hit identification and lead optimization. In particular, activity cliffs, defined as a pair of structurally similar molecules that present large changes in potency, provide valuable SAR information. Herein, we introduce the concept of activity cliff generator , defined as a molecular structure that has a high probability to form activity cliffs with molecules tested in the same biological assay. To illustrate this concept, we discuss a case study where Structure‐Activity Similarity maps were used to systematically identify and analyze activity cliff generators present in a dataset of 168 compounds tested against three peroxisome‐proliferator‐activated receptor (PPAR) subtypes. Single‐target and dual‐target activity cliff generators for PPARα and δ were identified. In addition, docking calculations of compounds that were classified as cliff generators helped to suggest a hot spot in the target protein responsible of activity cliffs and to analyze its implication in ligand‐enzyme interaction.