QSAR Models Using a Large Diverse Set of Estrogens

Endocrine disruptors (EDs) have a variety of adverse effects in humans and animals. About 58 000 chemicals,most having little safety data, must be tested in a group of tiered assays. As assays will take years, it isimportant to develop rapid methods to help in priority setting. For application to large data sets, we havedeveloped an integrated system that contains sequential four phases to predict the ability of chemicals tobind to the estrogen receptor (ER), a prevalent mechanism for estrogenic EDs. Here we report the resultsof evaluating two types of QSAR models for inclusion in phase III to quantitatively predict chemical bindingto the ER. Our data set for the relative binding affinities (RBAs) to the ER consists of 130 chemicalscovering a wide range of structural diversity and a 6 orders of magnitude spread of RBAs. CoMFA andHQSAR models were constructed and compared for performance. The CoMFA model had a r