  Everyday we are exposed to various chemicals via food additives, cleaning and
cosmetic products and medicines -- and some of them might be toxic. However
testing the toxicity of all existing compounds by biological experiments is
neither financially nor logistically feasible. Therefore the government
agencies NIH, EPA and FDA launched the Tox21 Data Challenge within the
"Toxicology in the 21st Century" (Tox21) initiative. The goal of this challenge
was to assess the performance of computational methods in predicting the
toxicity of chemical compounds. State of the art toxicity prediction methods
build upon specifically-designed chemical descriptors developed over decades.
Though Deep Learning is new to the field and was never applied to toxicity
prediction before, it clearly outperformed all other participating methods. In
this application paper we show that deep nets automatically learn features
resembling well-established toxicophores. In total, our Deep Learning approach
won both of the panel-challenges (nuclear receptors and stress response) as
well as the overall Grand Challenge, and thereby sets a new standard in tox
prediction.
