The DOI matches its Crossref record; 26 of the 27 references it cites were found in a registry or an index.
The Tox21 robotic platform for the assessment of environmental chemicals – from vision to reality
Open accessAbstract
Since its establishment in 2008, the US Tox21 inter-agency collaboration has made great progress in developing and evaluating cellular models for the evaluation of environmental chemicals as a proof of principle. Currently, the program has entered its production phase (Tox21 Phase II) focusing initially on the areas of modulation of nuclear receptors and stress response pathways. During Tox21 Phase II, the set of chemicals to be tested has been expanded to nearly 10,000 (10K) compounds and a fully automated screening platform has been implemented. The Tox21 robotic system combined with informatics efforts is capable of screening and profiling the collection of 10K environmental chemicals in triplicate in a week. In this article, we describe the Tox21 screening process, compound library preparation, data processing, and robotic system validation.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“While OGB-MolPCBA primarily focuses on predicting biophysical activity (e.g., protein binding), other datasets in MoleculeNet (Wu et al., 2018) include prediction of quantum mechanical properties (e.g., HOMO/LUMO), physical chemistry properties (e.g., water solubility), and physiological properties (e.g., toxicity prediction (Attene-Ramos et al., 2013)).”
From WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020) · cites this paper as Attene-Ramos et al., 2013
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Zhang, J.-H., Chung, T. D. Y., & Oldenburg, K. R. (1999) · SLAS Discovery · doi:10.1177/108705719900400206 · Cited by 6,847
The possible effects of the aggregation of the molecules of haemoglobin on its dissociation curves
Hill, A. V., Hill, A. L., & Hill, A. V. (1910) · The Journal of Physiology · Cited by 2,181
Toxicity Testing in the 21st Century: A Vision and a Strategy
Krewski, D. R., Acosta, D., Andersen, M. E., Anderson, H. A., Bailar, J. C., Boekelheide, K., Brent, R., Charnley, G., Cheung, V. G., Green, S., et al. (2010) · Journal of Toxicology and Environmental Health Part B · doi:10.1080/10937404.2010.483176 · Cited by 1,696
PubChem: Integrated Platform of Small Molecules and Biological Activities
Bolton, E., Wang, Y., Thiessen, P. A., & Bryant, S. H. (2008) · Annual reports in computational chemistry · doi:10.1016/s1574-1400(08)00012-1 · Cited by 1,267
Toxicity testing in the 21st century: A vision and a strategy
Gibb, S. (2007) · Reproductive Toxicology · doi:10.1016/j.reprotox.2007.10.013 · Cited by 1,256
(2014) · Choice Reviews Online · doi:10.5860/choice.52-0597 · Cited by 889
Inglese, J., Auld, D. S., Jadhav, A., Johnson, R. L., Simeonov, A. M., Yasgar, A., Zheng, W., & Austin, C. P. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0604348103 · Cited by 794
Self-citationTransforming Environmental Health Protection
Collins, F. S., Gray, G. M., & Bucher, J. R. (2008) · Science · doi:10.1126/science.1154619 · Cited by 630
Improving the Human Hazard Characterization of Chemicals: A Tox21 Update
Tice, R. R., Austin, C. P., Kavlock, R. J., & Bucher, J. R. (2013) · Environmental Health Perspectives · doi:10.1289/ehp.1205784 · Cited by 619
Self-citationJudson, R., Houck, K. A., Kavlock, R. J., Knudsen, T. B., Martin, M. T., Mortensen, H. M., Reif, D. M., Rotroff, D. M., Shah, I., Richard, A. M., et al. (2009) · Environmental Health Perspectives · doi:10.1289/ehp.0901392 · Cited by 610
Self-citation
Flagged first, then by citations
1 of the 27 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 201–255 of 255
Kwon, H. J., Lee, H., Lee, S., Ko, W., Yun, S. J., Uesawa, Y., & Jung, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-33419-3 · Cited by 4
Liang, W., Feng, X., Su, W., Zhong, L., Li, P., Wang, H., Li, T., Ruan, T., & Jiang, G. (2023) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2023.168266 · Cited by 3
Jamal, Q. M. S., Ansari, M. A., Alharbi, A. H., Alomary, M. N., Jamous, Y. F., Dutta, T., Maity, A., & Ahmad, V. (2025) · Journal of Molecular Graphics and Modelling · doi:10.1016/j.jmgm.2025.108976 · Cited by 3
Adam, M., Fleischer, H., & Thurow, K. (2017) · IEEE International Instrumentation and Measurement Technology Conference · doi:10.1109/i2mtc.2017.7969875 · Cited by 3
Screening Method for the Identification of Compounds That Activate Pregnane X Receptor
Lynch, C., Sakamuru, S., & Xia, M. (2022) · Current Protocols · doi:10.1002/cpz1.615 · Cited by 3
Self-citationSolayman, A., Halwani, M., Zaid, I. M., Ambade, R. B., Chang, R., Ayyad, A., Almaskari, F., Zweiri, Y., & Samad, Y. A. (2024) · Advanced Engineering Materials · doi:10.1002/adem.202400630 · Cited by 3
Toxicodynamics: How Chemicals Harm Cells
Burcham, P. C. (2013) · Springer eBooks · doi:10.1007/978-1-4471-5553-9_4 · Cited by 3
Hashimoto, K., Takeda, S., Swenberg, J. A., & Nakamura, J. (2015) · Mutagenesis · doi:10.1093/mutage/gev042 · Cited by 3
Melnikov, F., Hsieh, J., Sipes, N. S., & Anastas, P. T. (2018) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.7b03394 · Cited by 3
Cell-Free Assays in Environmental Toxicology
Arini, A., Mittal, K., & Basu, N. (2018) · Springer eBooks · doi:10.1007/978-3-319-66084-4_3 · Cited by 3
da Silva, M. V. F., Silva, J., da Rocha, M. N., de Morais, S. M., & Marinho, E. S. (2026) · In Silico Pharmacology · doi:10.1007/s40203-026-00590-y · Cited by 2
Thangapandian, S., Idakwo, G., Luttrell, J., Hong, H., Zhang, C., & Gong, P. (2023) · Computational methods in engineering & the sciences · doi:10.1007/978-3-031-20730-3_11 · Cited by 2
Quantitative Bioactivity Signatures of Dietary Supplements and Natural Products
Yasgar, A., Bougie, D., Eastman, R. T., Huang, R., Itkin, M., Kouznetsova, J., Lynch, C., McKnight, C. G., Miller, M., Ngan, D. K., et al. (2023) · ACS Pharmacology & Translational Science · doi:10.1021/acsptsci.2c00194 · Cited by 2
Self-citationIn vitro to in vivo extrapolation methods in chemical hazard identification and risk assessment
Zhang, Y., Zhu, M., Liu, W., Fu, Z., Li, X., & Chen, J. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15339-6.00034-5 · Cited by 2
Perfilova, V. N. (2023) · Safety and Risk of Pharmacotherapy · doi:10.30895/2312-7821-2023-379 · Cited by 2
Advanced High Throughput Screening Platforms in Drug Discovery
Joshi, C., & Makwana, D. (2024) · Advances in medical technologies and clinical practice book series · doi:10.4018/979-8-3693-6597-7.ch002 · Cited by 2
Linking Environmental Exposure to Toxicity
Oki, N. O., Leonard, J. A., Nelms, M., Bell, S. M., Tan, Y., Burgoon, L. D., & Edwards, S. W. (2017) · Royal Society of Chemistry eBooks · doi:10.1039/9781782623731-00060 · Cited by 2
Introducing Membrane Transport Energy into the Design of Sustainable Chemicals against Cytotoxicity
Shen, L. Q., Ji, N., & Lu, B. (2017) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.7b03493 · Cited by 2
Adaptive graph convolutional neural network and its biomedical applications
Huang, J., & Li, R. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-819872-8.00012-4 · Cited by 2
Jaime, M., Karott, S., Salem, G. H., Krynitsky, J., Garmendia‐Cedillos, M., Anderson, S., Harbison, S. T., Pohida, T. J., & Oliver, B. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/428037 · Cited by 2
Gong, Y., Yang, D., Liu, J., Barrett, H., Sun, J., & Peng, H. (2022) · ChemRxiv · doi:10.26434/chemrxiv-2022-zjkjw · Cited by 2
Predictive Tox-21 Methods for Assessing Emerging Pollutants in the Marine Environment
Kiani, Y. S. (2025) · Springer eBooks · doi:10.1007/978-3-031-75713-6_16 · Cited by 1
Novel target identification towards drug repurposing based on biological activity profiles
Xue, B., Xu, Y., Huang, R., & Zhu, Q. (2025) · PLoS ONE · doi:10.1371/journal.pone.0319865 · Cited by 1
Self-citationBreaking Lock-ins to Enable a Green Pharmacy
Shalin, A., Diamond, M. L., & Wang, Z. (2026) · Environmental Science & Technology · doi:10.1021/acs.est.5c12437 · Cited by 3
DYNAMEX: A Time-Resolved Model for Predicting Distribution of Chemicals in In Vitro Cell Assays
Fischer, F. C., Geyer, L., Rodriguez, M., Henneberger, L., & Escher, B. I. (2026) · Environmental Science & Technology · doi:10.1021/acs.est.6c06087 · Cited by 1
Designing a Data Analysis Subsystem for Predicting the Properties of Antifungal Antibiotics
Musayev, E. E., Chistyakova, T. B., Kolodyaznaya, V. A., & Белахов, В. В. (2022) · Studies in systems, decision and control · doi:10.1007/978-3-030-95112-2_5 · Cited by 1
Kagaya, M., & Uesawa, Y. (2024) · Pharmaceuticals · doi:10.3390/ph17081086 · Cited by 1
Characterizing sources of variability in zebrafish embryo screening protocols_suppl
Hamm, J. T. (2018) · ALTEX · doi:10.14573/altex.1804162s · Cited by 1
Machine Learning (ML) and Toxicity Studies
Ghosh, S., & Dasgupta, R. (2022) · Springer eBooks · doi:10.1007/978-981-16-8881-2_21 · Cited by 1
Polypharmacology in Predicting Drug Toxicity: Drug Promiscuity
Wang, Z., & Yang, B. (2022) · Springer eBooks · doi:10.1007/978-3-031-04998-9_14 · Cited by 1
Automation Concepts for Life Sciences
Fleischer, H., & Thurow, K. (2017) · doi:10.1002/9783527805297.ch3 · Cited by 1
Thurow, K., & Junginger, S. (2022) · Wiley eBooks · doi:10.1002/9783527829446.ch1 · Cited by 1
Toxicokinetics and Toxicity Testing in Drug Development1
Yanni, S. B. (2015) · doi:10.1002/9781118838440.ch7
Vrana, J. A. (2015) · The Research Repository @ WVU (West Virginia University) · doi:10.33915/etd.6880
Mitochondrial Function to Identify Compounds that Acutely Decrease Mitochondrial Membrane Potential
Attene‐Ramos, M. S., Huang, R., Michael, S., Witt, K. L., Richard, A. M., Tice, R. R., Simeonov, A. M., Austin, C. P., & Xia, M. (2014)
Self-citationComputational Toxicology and Risk Assessment
Fowler, B. A. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.64225-3
A Toxicological Application of Signal Transduction
Miller, J. V., Prince, N., Mouch, J. A., & Boyd, J. W. (2019) · doi:10.1002/9781119060208.ch8
Automatisierung in den Lebenswissenschaften – eine kritische Betrachtung
Fleischer, H., & Thurow, K. (2019) · doi:10.1002/9783527822843.ch2
Automation in Life Sciences – A Critical Review
Fleischer, H., & Thurow, K. (2017) · doi:10.1002/9783527805297.ch2
Automatisierte Datenauswertung in den Lifesciences
Fleischer, H., & Thurow, K. (2019) · Wiley eBooks · doi:10.1002/9783527822843.ch6
Automated Data Evaluation in Life Sciences
Fleischer, H., & Thurow, K. (2017) · Wiley eBooks · doi:10.1002/9783527805297.ch6
Automationskonzepte für die Lebenswissenschaften
Fleischer, H., & Thurow, K. (2019) · doi:10.1002/9783527822843.ch3
Zhang, Q., Taniguchi, S., So, K., Tsuda, M., Higuchi, Y., Hashida, M., & Yamashita, F. (2022) · The Journal of Toxicological Sciences · doi:10.2131/jts.47.337
Musayev, E. E., Chistyakova, T. B., Kolodyaznaya, V. A., & Белахов, В. В. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-20875-1_24
A hierarchical constrained density regression model for predicting cluster‐level dose‐response
Pennell, M. L., Wheeler, M. W., & Auerbach, S. S. (2024) · Environmetrics · doi:10.1002/env.2880
Vandenberg, L. N., & Blumberg, B. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95488-4.00140-6
Uterine Tumors and the Environment
Yang, S., Parra-Forero, L. Y., Andrus, A. R., Sexton, C. N., Kuang, D., & Nowak, R. A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-323-95488-4.00256-4
Karaduman, G., & Çeli̇k, F. K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-36474-7.00006-5
Sun, W., Li, M., Wang, Z., Cai, H., Zhao, Z., Huang, X., Xu, F. Y., Zeng, X., Feng, H., & Pan, Y. (2026) · Environmental Science & Technology · doi:10.1021/acs.est.6c01236
Leveraging Machine Learning for Network Pharmacology
Singh, S. D., & Nagamani, S. (2026) · Royal Society of Chemistry eBooks · doi:10.1039/9781837070206-00721
Xiao, M., Xiao, Y., Wu, Y., You, Y., Liu, X., Thanh, H. V., & Zhang, L. (2026) · Briefings in Bioinformatics · doi:10.1093/bib/bbag491
Fragment-Based Design of Pesticide-Like Molecules: From Method Development to Agrochemical Discovery
Shi, X.-X., Wang, Z.-Z., Yang, R., Wang, F., & Yang, G. (2026) · Accounts of Chemical Research · doi:10.1021/acs.accounts.6c00580
Metadata
- Authors
- Attene-Ramos, M. S., Miller, N., Huang, R., Michael, S., Itkin, M., Kavlock, R. J., Austin, C. P., Shinn, P., Simeonov, A., Tice, R. R., & Xia, M.
- Affiliations
- National Institutes of Health (US)
- Environmental Protection Agency (US)
- Corresponding author
- Xia, Menghang
- ORCID
- Venue
- Drug Discovery Today
- Publisher
- Elsevier BV
- Published
- 2013-08
- Volume
- 18
- Issue
- 15-16
- Pages
- 716-723
- ISSN
- 1359-6446 (ISSN-L)
- PMC
- PMC3771082
- OpenAlex
- W1991146785
- Cited by
- 255
- References
- 27
- Citations by year
- 2026: 9 · 2025: 12 · 2024: 19 · 2023: 26 · 2022: 25 · 2021: 11 · 2020: 18 · 2019: 23 · 2018: 33 · 2017: 24 · 2016: 23 · 2015: 20 · 2014: 10 · 2013: 2
- FWCI
- 13.48
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Drug Discovery Today (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (Z99 TR999999)
- National Human Genome Research Institute (ZIBHG200319)
- National Institute of Environmental Health Sciences (Y02 ES007020)
- Topics
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Effects and risks of endocrine disrupting chemicals
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Environmental Toxicology and Ecotoxicology
- Keywords
- Tox21, environmental chemicals, high-throughput screening, stress response pathways, automated data processing, chemical profiling
- MeSH terms
- Animals, Environmental Monitoring, Environmental Pollutants, Humans, Robotics, United States, United States Environmental Protection Agency
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
