DeepTox: Toxicity Prediction using Deep Learning

Knowledge Source Identification
Knowledge source name DeepTox: Toxicity Prediction using Deep Learning
Owner/Developer Frontiers Media S.A.
Country Switzerland
Languages English
Description The Tox21 Data Challenge has been the largest effort of the scientific community to compare computational methods for toxicity prediction. This challenge comprised 12,000 environmental chemicals and drugs which were measured for 12 different toxic effects by specifically designed assays. We participated in this challenge to assess the performance of Deep Learning in computational toxicity prediction. Deep Learning has already revolutionized image processing, speech recognition, and language understanding but has not yet been applied to computational toxicity. Deep Learning is founded on novel algorithms and architectures for artificial neural networks together with the recent availability of very fast computers and massive datasets. It discovers multiple levels of distributed representations of the input, with higher levels representing more abstract concepts. We hypothesized that the construction of a hierarchy of chemical features gives Deep Learning the edge over other toxicity prediction methods. Furthermore, Deep Learning naturally enables multi-task learning, that is, learning of all toxic effects in one neural network and thereby learning of highly informative chemical features.
Knowledge Source Category
Category Publication
Sub categories Review / Research article
Knowledge Dissemination and Sharing
Dissemination channel Website
Targeted audience (specified/objective analysis) Scientists, Regulators, Industry, Researchers, Educators, Students
Users access Open access
Knowledge Characterization
3Rs relevance Replacement, Reduction
Purpose Toxicological and safety evaluation, Documentation and information
Technology/Tools Non-testing methods (in silico)