Abstract
Over the years development of selective estrogen receptor (ER) ligands has been of great concern to researchers involved in the chemistry and pharmacology of anticancer drugs, resulting in numerous synthesized selective ER subtype inhibitors. In this work, a data set of 82 ER ligands with ERα and ERβ inhibitory activities was built, and quantitative structure-activity relationship (QSAR) methods based on the two linear (multiple linear regression, MLR, partial least squares regression, PLSR) and a nonlinear statistical method (Bayesian regularized neural network, BRNN) were applied to investigate the potential relationship of molecular structural features related to the activity and selectivity of these ligands. For ERα and ERβ, the performances of the MLR and PLSR models are superior to the BRNN model, giving more reasonable statistical properties (ERα: for MLR, Rtr2 = 0.72, Qte2 = 0.63; for PLSR, Rtr2 = 0.92, Qte2 = 0.84. ERβ: for MLR, Rtr2 = 0.75, Qte2 = 0.75; for PLSR, Rtr2 = 0.98, Qte2 = 0.80). The MLR method is also more powerful than other two methods for generating the subtype selectivity models, resulting in Rtr2 = 0.74 and Qte2 = 0.80. In addition, the molecular docking method was also used to explore the possible binding modes of the ligands and a relationship between the 3D-binding modes and the 2D-molecular structural features of ligands was further explored. The results show that the binding affinity strength for both ERα and ERβ is more correlated with the atom fragment type, polarity, electronegativites and hydrophobicity. The substitutent in position 8 of the naphthalene or the quinoline plane and the space orientation of these two planes contribute the most to the subtype selectivity on the basis of similar hydrogen bond interactions between binding ligands and both ER subtypes. The QSAR models built together with the docking procedure should be of great advantage for screening and designing ER ligands with improved affinity and subtype selectivity property.
Highlights
The estrogen receptor (ER), a member of the nuclear receptor superfamily of ligand-modulated transcriptional factors [1], is responsible for transcription of genes containing estrogen responsive elements or repression of some genes [2]
Self-organizing maps are a special kind of neural network that can be used for clustering, visualization and abstraction tasks
Numbers in blue rectangles are compounds further split for the validation of the Bayesian regularized neural networks (BRNNs) models
Summary
The estrogen receptor (ER), a member of the nuclear receptor superfamily of ligand-modulated transcriptional factors [1], is responsible for transcription of genes containing estrogen responsive elements or repression of some genes [2]. ER mediates the activity of estrogens in the regulation of a number of important physioligical processes, including the development and function of the female reproductive system and maintenance of bone mineral density and cardiovascular health; stimulation of other tissues can increase the risk of cancer within these tissues, particular in female breast and uterus [3]. ER has been a target for pharmaceutical agents for hormone replacement in menopausal women, uterine and breast cancers. ER was found in two isoform subtypes, i.e., ERα and ERβ. Studies have shown the two subtypes have different functions and distributions in certain tissues [4,5].
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