Influence of the structural diversity of data sets on the statistical quality of three-dimensional quantitative-activity relationship (3D-QSAR) models: Predicting the estrogenic activity of xenoestrogens

Federal legislation has resulted in the two-tiered in vitro and in vivo screening of some 80 000 structurally diverse chemicals for possible endocrine disrupting effects. To maximize efficiency and minimize expense, prioritization of these chemicals with respect to their estrogenic disrupting potential prior to this time-consuming and labor-intensive screening process is essential. Computer-based quantitative structure-activity relationship (QSAR) models, such as those obtained using comparative molecular field analysis (CoMFA), have been demonstrated as useful for risk assessment in this application. In general, however, CoMFA models to predict estrogenicity have been developed from data sets with limited structural diversity. In this study, we constructed CoMFA models based on biological data for a structurally diverse set of compounds spanning eight chemical families. We also compared two standard alignment schemes employed in CoMFA, namely, atom-fit and flexible field-fit, with respect to the predictive capabilities of their respective models for structurally diverse data sets. The present analysis indicates that flexible field-fit alignment fares better than atom-fit alignment as the structural diversity of the data set increases. Values of log(RP), where RP = relative potency, predicted by the final flexible field-fit CoMFA models are in good agreement with the corresponding experimental values. These models should be effective for predicting the endocrine disrupting potential of existing chemicals as well as prospective and newly prepared chemicals before they enter the environment.