Tox21Challenge to Build Predictive Models of Nuclear Receptor and Stress Response Pathways as Mediated by Exposure to Environmental Chemicals and Drugs

By Ruili Huang, Menghang Xia, Dac-Trung Nguyen, Tongan Zhao, Srilatha Sakamuru, Jinghua Zhao, Sampada Shahane, Anna Rossoshek, Anton Simeonov. 2016

222 citations recorded by the source corpus.

Tens of thousands of chemicals with poorly understood biological properties are released into the environment each day. High-throughput screening (HTS) is potentially a more efficient and cost-effective alternative to traditional toxicity tests. Using HTS, one can profile chemicals for potential adverse effects and prioritize a manageable number for more in-depth testing. Importantly, it can provide clues to mechanism of toxicity. The Tox21 program has generated >50 million quantitative high-throughput screening (qHTS) data points. A library of several thousands of compounds, including environmental chemicals and drugs, is screened against a panel of nuclear receptor and stress response pathway assays. The National Center for Advancing Translational Sciences (NCATS) has organized an International data challenge in order to “crowd-source” data and build predictive toxicity models. This Challenge asks a “crowd” of researchers to use these data to elucidate the extent to which the interference of biochemical and cellular pathways by compounds can be inferred from chemical structure data. The data generated against the Tox21 library served as the training set for this modeling Challenge. The competition attracted participants from 18 different countries to develop computational models aimed at better predicting chemical toxicity. The winning models from nearly 400 model submissions all achieved >80% accuracy. Several models exceeded 90% accuracy, which was measured by area under the receiver operating characteristic curve (AUC-ROC). Combining the winning models with the knowledge already gained from Tox21 screening data are expected to improve the community’s ability to prioritize novel chemicals with respect to potential human health concern.

Subfields: Biophysics, Computational Theory and Mathematics, Materials Chemistry

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Citations in the corpus

ProTox-II: a webserver for the prediction of toxicity of chemicals (2018, methodology): “The toxicology in the 21st Century (Tox21) (an initiative by the federal collaboration of National Institute of Environmental Health Sciences, the EPA, and the FDA), has greatly supported the development of computational methods for the assessment of toxicity of chemicals, fostering the vision of tr”
ToxicBlend: Virtual Screening of Toxic Compounds with Ensemble Predictors (2018, unknown): “As witnessed in the Tox21 Challenge, a spin-oﬀ of the ToxCast project organized in 2014 [6], techniques based around deep neural networks (DNNs), are able to provide state-of-the-art performance for virtual toxicity screening [7–12].”
All SMILES Variational Autoencoder (2019, methodology): “We also evaluate on the Tox21 dataset [22, 45] in the DeepChem package [69], comprising binarized binding afﬁnities of 7831 compounds against 12 proteins.”
Relational Pooling for Graph Representations (2019, methodology): “We chose three on which graph-based models achieved superior performance2: HIV, MUV (Rohrer & Baumann, 2009), and Tox21 (Mayr et al., 2016; Huang et al., 2016).”