Structure-activity relationship study of a diverse set of estrogen receptor ligands (I) using MultiCASE expert system

The MultiCASE expert system was used to construct a quantitative structure-activity relationship model to screen chemicals with estrogen receptor (ER) binding potential. Structures and ER binding data of 313 chemicals were used as inputs to train the expert system. The training data set covers inactive, weak as well as very powerful ER binders and represents a variety of chemical compounds. Substructural features associated with ER binding activity (biophores) and features that prevent receptor binding (biophobes) were identified. Although a single phenolic hydroxyl group was found to be the most important biophore responsible for the estrogenic activity of most of the chemicals, MultiCASE also identified other biophores and structural features that modulate the activity of the chemicals. Furthermore, the findings supported our previous hypothesis that a 6 A distant descriptor may describe a ligand-binding site on an ER. Quantitative structure-activity relationship models for the chemicals associated with each biophore were constructed as part of the expert system and can be used to predict the activity of new chemicals. The model was cross validated via 10 x 10%-off tests, giving an average concordance of 84.04%.