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3-D QSARS FOR RANKING AND PRIORITIZATION OF LARGE CHEMICAL DATASETS: AN EDC CASE STUDY
Citation:
Schmieder, P. K., S P. Bradbury, AND O. G. Mekenyan. 3-D QSARS FOR RANKING AND PRIORITIZATION OF LARGE CHEMICAL DATASETS: AN EDC CASE STUDY. Presented at 10th International Workshop on Quantitative Structure-Activity Relationships (QSARs) in Environmental Sciences, Ottawa, Ontario, Canada, May 25-29, 2002.
Description:
The COmmon REactivity Pattern (COREPA) approach is a three-dimensional structure activity (3-D QSAR) technique that permits identification and quantification of specific global and local steroelectronic characteristics associated with a chemical's biological activity. It goes beyond conventional 3-D QSAR approaches by incorporating dynamic chemical conformational flexibility in ligand-receptor interactions. The approach provides flexibility in screening chemical data sets in that it helps establish criteria for identifying false "positives" and "negatives". The current study evaluates the capacity of expert systems to identify the potential for chemicals to act as estrogen receptor (ER) ligands. A COREPA model is developed using a training set of > 200 rat ER relative binding affinity. (RBA) values for steroidal and non-streoidal chemicals, and is the basis of an expert system enabling categorization of thousands of chemicals into order of magnitude RBA ranges from < 0.001 (activity cut-off) to > 100%, relative to 17B-estradiol. The influences of training set structural diversity on model performance and expert system categorization is described by comparison of three models and associated expert systems, each derived from training sets with varying structural diversity. Potential application of the model for hazard evaluation of large chemical data sets is presented. This abstract does not necessarily reflect EPA policy.