Identification of Endocrine Disrupting Chemicals by in Silico Methods

Identification of endocrine disrupting chemicals (EDCs) is one of the important goals of environmental chemical hazard screening. It is exhaustive and time consuming to test in vitro all chemicals – potential EDCs – used in industry, agriculture or as food preservatives against their effects on the endocrine system. Computational methods, such as virtual screening, quantitative structure activity relationships and docking, are already well recognized and used in many strategic programs for identification of EDCs. The aim of this study was to evaluate the predictive performances of model for identification of EDCs integrated in non-commercial software tool. The resulting statistics indicated that the binding affinities of the majority of chemicals included in the external validation dataset could be correctly predicted. However, a set of further improvements has been suggested in order to increase the predictive ability of the current profiling scheme for EDCs.