Because of the large number of endocrine-disrupting chemicals (EDCs) in the environment, there is a great need for an effective tool of rapidly assessing ED activity in the toxicology assessment process and in the context of the new European REACH policy. Classification and regression QSAR models were developed to predict the estrogen receptor binding affinity based on a large data set of heterogeneous chemicals and theoretical molecular descriptors from DRAGON. The built OLS regression model, was validated comprehensively (internal and external validation, Y-randomization test) and all the validations indicate that the proposed QSAR model is robust and satisfactory. For the classification models, three nonlinear classification methodologies: Least Square Support Vector Machine (LS-SVM), Counter Propagation Artificial Neural Network (CP-ANN), and k-nearest Neighbor (kNN) were applied, by using four molecular structural descriptors as inputs. The models were also applied to about 58 000 discrete organic chemicals; about 77% were predicted not to bind to an estrogen receptor, but 8% should be prioritized for testing. QSAR Studies on Selective Ligands for the Thyroid Hormone Receptor beta have been also performed.
H.Liu et al. Chem. Res. Toxicol., 2006, 19, 1540; J.Mol. Graph. Model. 2007, 26, 135; Bioorg. Med. Chem., 2007, 15, 5251; Chemosphere, 2008, 70, 1889; Comb. Chem. & H. T S. (special issue on “Machine learning for virtual screening”) 2009, 12, 490.
In the EU-FP7 Project CADASTER the endocrine disruptor activity of flame retardants and PFCs were also studied.
E.Papa et al. Chem Res. Toxicol, 2010, 23, 946. S. Kovarich et al. J.Haz. Mat., 2011,190, 106 and SAR QSAR Environ Res., 2012,23, 207..
Androgen receptor (AR) binders and pleiotropic endocrine disruptors (with double activity on AR and ER) have been also studied and modeled by regression and classification QSAR models for screening purposes.
J. Li and P. Gramatica, Molecular Diversity, 2010, 14, 687; J. Chem. Inf. Mod, 2010, 50, 861; SAR &QSAR in Environ Res, 2010, 21, 657.
The group is also involved in CoMPARA international Project for modeling Androgen Receptor Binders as Agonist and Antagonist.