Autoencoder Predicting Estrogenic Chemical Substances (APECS): An improved approach for screening potentially estrogenic chemicals using in vitro assays and deep learning

Abstract In 2015 the US Environmental Protection Agency published a computational toxicology approach to screen chemicals for potential estrogenic activity. This complicated approach requires several steps, including concentration-response modeling (which includes fitting several different models and identifying the best model), application of a multi-factor mathematical model that attempts to model the concentration-response data, calculation of the area under the concentration-modeled response curve, and finally standardizing the area under the concentration-modeled response curve to that of 17-beta estradiol. Toxicologists will find it difficult to implement this approach on their own, creating a need for a more straightforward tool. Recently, it has been shown that deep learning approaches lead to less complicated approaches, that can run faster than more complicated approaches, while maintaining or improving overall algorithmic performance. In this paper we examine the Autoencoder Predicting Estrogenic Chemical Substances (APECS). APECS is two deep autoencoder models that achieve at least the same performance while being less complicated for an average toxicologist to use than the US EPA’s approach. Our deep autoencoders achieved accuracies of 91% vs 86% and 93% vs 93% on the in vivo and in vitro datasets used by the US EPA in validating their approach. Users can use our deep autoencoder models to make predictions of assay data by using our open source Java desktop applications. APECS has a simple push-button interface and was written in Java.