Conformal Prediction Classification of a Large Data Set of Environmental Chemicals from ToxCast and Tox21 Estrogen Receptor Assays

Quantitative structure–activity relationships (QSAR) are critical to exploitation of the chemical information in toxicology databases. Exploitation can be extraction of chemical knowledge from the data but also making predictions of new chemicals based on quantitative analysis of past findings. In this study, we analyzed the ToxCast and Tox21 estrogen receptor data sets using Conformal Prediction to enhance the full exploitation of the information in these data sets. We applied aggregated conformal prediction (ACP) to the ToxCast and Tox21 estrogen receptor data sets using support vector machine classifiers to compare overall performance of the models but, more importantly, to explore the performance of ACP on data sets that are significantly enriched in one class without employing sampling strategies of the training set. ACP was also used to investigate the problem of applicability domain using both data sets. Comparison of ACP to previous results obtained on the same data sets using traditional QSAR app...