Computational prediction of toxicity

Abstract

As the number of new chemicals developed and being used keep adding every year, having the toxic profiles of each chemical becomes a daunting challenge. To meet this information gap, EPA suggested that certain in vitro assays and computational methods, which predict toxicity related information in much lesser time and cost than traditional in vivo methods, may be used. In this paper, we use computational techniques to use results from certain in vitro assays applied on 309 chemicals (whose toxicity profile is readily available) along with the molecular descriptors and other computed physical-chemical properties of the chemicals to predict the toxicity caused by chemical at a particular endpoint. The dataset is available from EPA TOXCAST group online. We show that Random Forest and Naïve Bayes have a good performance on this dataset. We also show that using small and related trees in random forest help to further improve the performance.