Consensus modeling for HTS assays using in silico descriptors calculates the best balanced accuracy in Tox21 challenge

Abstract

The need for filling information gaps while reducing toxicity testing in animals is becoming more predominant in risk assessment. Recent legislations are accepting in silico approaches for predicting toxicological outcomes. This article describes the results of Quantitative Structure Activity Relationship (QSAR) modeling efforts within Tox21 Data Challenge 2014 1 , which calculated the best balanced accuracy across all molecular pathway endpoints as well as the highest scores for ATAD5 and mitochondrial membrane potential disruption. Automated QSPR workflow systems, OCHEM (http://ochem.eu), the analytics platform, KNIME and the statistics software, CRAN R, were used to conduct the analysis and develop consensus models using 10 different descriptor sets. A detailed analysis of QSAR models for all 12 molecular pathways and the effect of underlying models' accuracy on the quality of the consensus model are provided. The resulting consensus models yielded a balanced accuracy as high as 88.1% ± 0.6 for mitochondrial membrane disruptors. Such high balanced accuracy and use of the applicability domain show a promising potential for in silico modeling to complement design HTS screening experiments. The comprehensive statistics of all models are publicly available online at https://github.com/amaziz/Tox21-Challenge-Publication while the developed consensus models can be accessed at http://ochem.eu/article/98009.