Lather, VineyFernandes, Miguel X.2023-02-082023-02-082009Lather, V., & Fernandes, M. X. (2009). QSAR models for prediction of PPARδ agonistic activity of indanylacetic acid derivatives. QSAR & Combinatorial Science, 28(4), 447-457.http://hdl.handle.net/10400.13/5012Peroxisome Proliferator Activated Receptor b/d (PPAR b/d), one of three PPAR isoforms is a member of nuclear receptor superfamily and ubiquitously expressed in several metabolically active tissues such as liver, muscle, and fat. Tissue specific expression and knock-out studies suggest a role of PPARd in obesity and metabolic syndrome. Specific and selective PPARd ligands may play an important role in the treatment of metabolic disorders. Indanylacetic acid derivatives reported as potent and specific ligands against PPARd have been studied for the Quantitative Structure – Activity Relationships (QSAR). Molecules were represented by chemical descriptors that encode constitutional, topological, geometrical, and electronic structure features. Four different approaches, i.e., random selection, hierarchical clustering, k-means clustering, and sphere exclusion method were used to classify the dataset into training and test subsets. Forward stepwise Multiple Linear Regression (MLR) approach was used to linearly select the subset of descriptors and establish the linear relationship with PPARd agonistic activity of the molecules. The models were validated internally by Leave One Out (LOO) and externally for the prediction of test sets. The best subset of descriptors was then fed to the Artificial Neural Networks (ANN) to develop non-linear models. Statistically significant MLR models; with R2 varying from 0.80 to 0.87 were generated based on the different training and test set selection methods. Training of ANNs with different architectures for the different training and test selection methods resulted in models with R2 values varying from 0.83 to 0.94, which indicates the high predictive ability of the models.engArtificial neural networkIndanylacetic acidsMultiple linear regression (MLR)Peroxisome proliferator activated receptor (PPAR)QSAR.Faculdade de Ciências Exatas e da Engenhariajournal article10.1002/qsar.200810092