Identifying toxicity effects of chemicals is a necessary step in many processes including drug design. To identify associations between chemicals and toxicity effects, we applied multi-label classification (MLC) methods. These methods have not undergone comprehensive benchmarking in the domain of predictive toxicology that could help in identifying guidelines for overcoming the existing deficiencies of these methods. Therefore, we performed extensive benchmarking and analysis of ~19,000 MLC models. We demonstrated variability in the performance of these models under several conditions and determined the best performing model that achieves accuracy of 91% on an independent testing set.
Building 9 - Lecture hall 2
13:15 - 13:30