Structure-based quantitative structure-activity relationship modeling of estrogen receptor β-ligands

Background: A variety of chemotypes have been studied as estrogen receptor (ER) beta-selective ligands for potential drugs against various indications, including neurodegenerative diseases. Their structure-activity relationship data and the x-ray structures of the ER beta ligand-binding domain bound with different ligands have become available. Thus, it is vitally important for future development of ER beta-selective ligands that robust quantitative structure-activity relationship (QSAR) models be built. Methods/results: We employed a newly developed structure-based QSAR method (structure-based pharmacophore keys QSAR) that utilizes both the structure-activity relationship data and the 3D structural information of ER beta, as well as a robust QSAR workflow to analyze 37 ligands. Four sets of QSAR models were obtained, among which approximately 30 models afforded high (> 0.60) training-r(2) and test set-R-2 statistics. Conclusion: We have obtained an ensemble of predictive models of ER beta ligands that will be useful in the future discovery of novel ER beta-selective molecules.