Docking-based classification models for exploratory toxicology studies on high-quality estrogenic experimental data

Background: The ethical and practical limitation of animal testing has recently promoted computational methods for the fast screening of huge collections of chemicals. Results: The authors derived 24 reliable docking-based classification models able to predict the estrogenic potential of a large collection of chemicals provided by the US Environmental Protection Agency. Model performances were challenged by considering AUC, EF1% (EFmax = 7.1), -LR (at sensitivity = 0.75); +LR (at sensitivity = 0.25) and 37 reference compounds comprised within the training set. Moreover, external predictions were made successfully on ten representative known estrogenic chemicals and on a set consisting of >32,000 chemicals. Conclusion: The authors demonstrate that structure-based methods, widely applied to drug discovery programs, can be fairly adapted to exploratory toxicology studies.