ArticleNature communications2016
Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization.
Article in Nature communications, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 108 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
108 citing papers in PubMed.
- Expanded Tox21 Biological Assay Panel for the Prediction of Drug-Induced Liver Injury and Cardiotoxicity.Environmental health perspectives · 2026Article
- Review
- Integrating chemical structure and high-throughput transcriptomics for mechanistically interpretable Tox21 bioactivity prediction.Journal of cheminformatics · 2026Article
- Bioactivity Classification of 2,649 Per- and Polyfluoroalkyl Substances (PFASs) via Quantitative Structure-Activity Relationships and Molecular Docking to Health-Relevant Proteins.Environmental science & technology letters · 2026Article
- Machine learning-driven discovery of therapeutic nucleoside hydrogels for periodontitis.International journal of oral science · 2026Article
- Impact of Chemical Quality on High-Throughput in Vitro Assays: A Tox21 Study.Chemical research in toxicology · 2026Article
- Development of machine learning-based multi-task quantitative structure-activity relationship models for predicting toxicities in six human organ systems.Computational toxicology (Amsterdam, Netherlands) · 2026Article
- Multi-task deep learning models for mechanism-based prediction of developmental and reproductive toxicity (DART) using ToxCast bioassays.Frontiers in toxicology · 2026Article
- Review
- ToxACoL: an endpoint-aware and task-focused compound representation learning paradigm for acute toxicity assessment.Nature communications · 2025Article
- Artificial intelligence approaches for anti-addiction drug discovery.Digital discovery · 2025Review
- Application of cytochrome P450 enzyme assays to predict p53 inducers and AChE inhibitors that require metabolic activation.Toxicology and applied pharmacology · 2025Article
- Bayesian Optimization over Multiple Experimental Fidelities Accelerates Automated Discovery of Drug Molecules.ACS central science · 2025Article
- Analytical Quality Evaluation of the Tox21 Compound Library.Chemical research in toxicology · 2025Review
- Prediction of chemical-induced acute toxicity using in vitro assay data and chemical structure.Toxicology and applied pharmacology · 2024Article
- Impacts on Atlantic Killifish from Neurotoxicants: Genes, Behavior, and Population-Relevant Outcomes.Environmental science & technology · 2024Article
- Per-and polyfluoroalkyl substances and disrupted sleep: mediating roles of proteins.Environmental advances · 2024Article
- Exploring the anticancer mechanism of cardiac glycosides using proteome integral solubility alteration approach.Cancer medicine · 2024Article
- Emerging trends and future challenges of advanced 2D nanomaterials for combating bacterial resistance.Bioactive materials · 2024Review
- AI-driven Discovery of Morphomolecular Signatures in Toxicology.bioRxiv : the preprint server for biology · 2024Article
48 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Target-specific, mechanism-oriented in vitro assays post a promising alternative to traditional animal toxicology studies. Here we report the first comprehensive analysis of the Tox21 effort, a large-scale in vitro toxicity screening of chemicals. We test ∼ 10,000 chemicals in triplicates at 15 concentrations against a panel of nuclear receptor and stress response pathway assays, producing more than 50 million data points. Compound clustering by structure similarity and activity profile similarity across the assays reveals structure-activity relationships that are useful for the generation of mechanistic hypotheses. We apply structural information and activity data to build predictive models for 72 in vivo toxicity end points using a cluster-based approach. Models based on in vitro assay data perform better in predicting human toxicity end points than animal toxicity, while a combination of structural and activity data results in better models than using structure or activity data alone. Our results suggest that in vitro activity profiles can be applied as signatures of compound mechanism of toxicity and used in prioritization for more in-depth toxicological testing.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.