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The Tox21 robotic platform for the assessment of environmental chemicals – from vision to reality
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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.
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“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
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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
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“…of different toxicity prediction software and web-based tools such as Tox21 (https:// ntp. niehs. nih. gov/ whatw estudy/ tox21/ index. html) [327], SEA (http:// sea. bkslab. org/) [328], eToxPred (https:// www. bryli nski. org/ etoxp red-0) [329], and TargeTox (https:// github. com/…”
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Yang, H., Lou, C., Sun, L., Li, J., Cai, Y., Wang, Z., Li, W., Liu, G., & Tang, Y. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty707 · Cited by 1,480
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Erythropel, H. C., Zimmerman, J. B., de Winter, T. M., Petitjean, L., Melnikov, F., Lam, J. C., Lounsbury, A. W., Mellor, K. E., Janković, N. Z., Tu, Q., et al. (2018) · Green Chemistry · doi:10.1039/c8gc00482j · Cited by 848
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Escher, B. I., Stapleton, H. M., & Schymanski, E. (2020) · Science · doi:10.1126/science.aay6636 · Cited by 809
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Mansouri, K., Abdelaziz, A., Rybacka, A., Roncaglioni, A., Tropsha, A., Varnek, A., Zakharov, A., Worth, A., Richard, A. M., Grulke, C. M., et al. (2016) · Environmental Health Perspectives · doi:10.1289/ehp.1510267 · Cited by 375
“EPA’s HTS assays, online databases, and other data sets used by participants to train models: • HTS data from Tox21 project consisting of ~ 8,000 chemicals evaluated in four assays (Attene-Ramos et al. 2013; Collins et al. 2008; Huang et al. 2014; Shukla et al. 2010; Tice et al. 2013), extending beyond the 1,677 used…”
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Richard, A. M., Huang, R., Waidyanatha, S., Shinn, P., Collins, B. J., Thillainadarajah, I., Grulke, C. M., Williams, A. J., Lougee, R. R., Judson, R. S., et al. (2021) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.0c00264 · Cited by 323
Self-citationHuang, R., Grishagin, I. V., Wang, Y., Zhao, T., Greene, J., Obenauer, J. C., Ngan, D. K., Nguyễn, Ð.-T., Guha, R., Jadhav, A., et al. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.00445 · Cited by 294
Self-citationWILDS: A Benchmark of in-the-Wild Distribution Shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518
“…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)).”
Huang, R., Xia, M., Sakamuru, S., Zhao, J., Shahane, S. A., Attene‐Ramos, M. S., Zhao, T., Austin, C. P., & Simeonov, A. M. (2016) · Nature Communications · doi:10.1038/ncomms10425 · Cited by 259
Self-citationWetmore, B. A., Wambaugh, J. F., Allen, B., Ferguson, S., Sochaski, M. A., Setzer, R. W., Houck, K. A., Strope, C. L., Cantwell, K. E., Judson, R., et al. (2015) · Toxicological Sciences · doi:10.1093/toxsci/kfv171 · Cited by 242
Judson, R., Houck, K., Martin, M., Richard, A. M., Knudsen, T. B., Shah, I., Little, S., Wambaugh, J., Woodrow Setzer, R., Kothya, P., et al. (2016) · Toxicological Sciences · doi:10.1093/toxsci/kfw092 · Cited by 232
Self-citationCiallella, H. L., & Zhu, H. (2019) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.8b00393 · Cited by 227
Phenotypic screening of the ToxCast chemical library to classify toxic and therapeutic mechanisms
Kleinstreuer, N. C., Yang, J., Berg, E. L., Knudsen, T. B., Richard, A. M., Martin, M. T., Reif, D. M., Judson, R. S., Polokoff, M., Dix, D. J., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2914 · Cited by 211
Self-citationMachine learning for synergistic network pharmacology: a comprehensive overview
Noor, F., Asif, M., Ashfaq, U. A., Qasim, M., & Qamar, M. T. U. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad120 · Cited by 226
Adverse Outcome Pathways—Organizing Toxicological Information to Improve Decision Making
Edwards, S. W., Tan, Y., Villeneuve, D. L., Meek, M. E., & McQueen, C. A. (2015) · Journal of Pharmacology and Experimental Therapeutics · doi:10.1124/jpet.115.228239 · Cited by 191
Wu, Y., & Wang, G. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19082358 · Cited by 193
Advancing Computational Toxicology by Interpretable Machine Learning
Jia, X., Wang, T., & Zhu, H. (2023) · Environmental Science & Technology · doi:10.1021/acs.est.3c00653 · Cited by 199
Schmidt, B. Z., Lehmann, M., Gutbier, S., Nembo, E. N., Noël, S., Smirnova, L., Forsby, A., Hescheler, J., Avci, H. X., Härtung, T., et al. (2016) · Archives of Toxicology · doi:10.1007/s00204-016-1805-9 · Cited by 191
How Green is Your Plasticizer?
Jamarani, R., Erythropel, H. C., Nicell, J. A., Leask, R. L., & Marić, M. (2018) · Polymers · doi:10.3390/polym10080834 · Cited by 174
Attene‐Ramos, M. S., Huang, R., Michael, S., Witt, K. L., Richard, A., Tice, R. R., Simeonov, A. M., Austin, C. P., & Xia, M. (2014) · Environmental Health Perspectives · doi:10.1289/ehp.1408642 · Cited by 172
Self-citationHuang, R., Sakamuru, S., Martin, M. T., Reif, D. M., Judson, R., Houck, K. A., Casey, W., Hsieh, J., Shockley, K. R., Ceger, P. C., et al. (2014) · Scientific Reports · doi:10.1038/srep05664 · Cited by 169
Self-citationMav, D., Shah, R., Howard, B. E., Auerbach, S. S., Bushel, P. R., Collins, J. B., Gerhold, D. L., Judson, R., Karmaus, A. L., Maull, E. A., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0191105 · Cited by 159
Zhu, H., Zhang, J., Kim, M. T., Boison, A., Sedykh, A., & Moran, K. (2014) · Chemical Research in Toxicology · doi:10.1021/tx500145h · Cited by 154
Bal‐Price, A., Crofton, K. M., Leist, M., Allen, S. L., Arand, M., Buetler, T. M., Delrue, N., FitzGerald, R., Härtung, T., Heinonen, T., et al. (2015) · Archives of Toxicology · doi:10.1007/s00204-015-1464-2 · Cited by 157
Modeling Exposure in the Tox21 in Vitro Bioassays
Fischer, F. C., Henneberger, L., König, M., Bittermann, K., Linden, L., Goss, K., & Escher, B. I. (2017) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.7b00023 · Cited by 152
Escher, B. I., Henneberger, L., König, M., Schlichting, R., & Fischer, F. C. (2020) · Environmental Health Perspectives · doi:10.1289/ehp6664 · Cited by 123
Liu, Z., Huang, R., Roberts, R. A., & Tong, W. (2018) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2018.12.001 · Cited by 135
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Adler, M., Ramm, S., Hafner, M. A., Muhlich, J. L., Gottwald, E. M., Weber, E. J., Jaklic, A., Ajay, A. K., Svoboda, D. L., Auerbach, S. S., et al. (2015) · Journal of the American Society of Nephrology · doi:10.1681/asn.2015010060 · Cited by 128
Fritsche, E., Grandjean, P., Crofton, K. M., Aschner, M., Goldberg, A., Heinonen, T., Hessel, E. V. S., Hogberg, H. T., Bennekou, S. H., Lein, P. J., et al. (2018) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2018.02.004 · Cited by 124
Judson, R., Houck, K. A., Martin, M. T., Richard, A. M., Knudsen, T. B., Shah, I., Little, S. B., Wambaugh, J. F., Setzer, R. W., Kothiya, P., et al. (2016) · Toxicological Sciences · doi:10.1093/toxsci/kfw148 · Cited by 115
Self-citationBlackwell, B. R., Ankley, G. T., Corsi, S. R., DeCicco, L., Houck, K. A., Judson, R., Li, S., Martin, M. T., Murphy, E., Schroeder, A. L., et al. (2017) · Environmental Science & Technology · doi:10.1021/acs.est.7b01613 · Cited by 114
Zhang, J., Li, H., Zhang, Y., Huang, J., Ren, L., Zhang, C., Zou, Q., & Zhang, Y. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf533 · Cited by 134
Escher, B. I., Neale, P. A., & Leusch, F. D. L. (2015) · Water Research · doi:10.1016/j.watres.2015.05.049 · Cited by 120
Cote, I., Andersen, M. E., Ankley, G. T., Barone, S., Birnbaum, L. S., Boekelheide, K., Bois, F. Y., Burgoon, L. D., Chiu, W. A., Crawford‐Brown, D., et al. (2016) · Environmental Health Perspectives · doi:10.1289/ehp233 · Cited by 101
Self-citationModern methodologies and tools for human hazard assessment of chemicals
Authority, E. F. S. (2014) · EFSA Journal · doi:10.2903/j.efsa.2014.3638 · Cited by 109
Review of high-content screening applications in toxicology
Li, S., & Xia, M. (2019) · Archives of Toxicology · doi:10.1007/s00204-019-02593-5 · Cited by 99
Self-citationZhang, Q., Li, J., Middleton, A., Bhattacharya, S., & Conolly, R. B. (2018) · Frontiers in Public Health · doi:10.3389/fpubh.2018.00261 · Cited by 99
Nonanimal Models for Acute Toxicity Evaluations: Applying Data-Driven Profiling and Read-Across
Russo, D. P., Strickland, J. A., Karmaus, A. L., Wang, W., Shende, S. M., Härtung, T., Aleksunes, L. M., & Zhu, H. (2019) · Environmental Health Perspectives · doi:10.1289/ehp3614 · Cited by 87
Advancing toxicology research using in vivo high throughput toxicology with small fish models
Planchart, A. (2016) · ALTEX · doi:10.14573/altex.1601281 · Cited by 85
Idakwo, G., Thangapandian, S., Luttrell, J., Zhou, Z., Zhang, C., & Gong, P. (2021) · US Army Corps of Engineers: Engineer Research and Development Center (Knowledge Core) · doi:10.21079/11681/41302 · Cited by 82
A Quantitative High-Throughput Screening Data Analysis Pipeline for Activity Profiling
Huang, R. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-6346-1_12 · Cited by 81
Self-citationHamm, J. T., Sullivan, K. M., Clippinger, A. J., Strickland, J. A., Bell, S. M., Bhhatarai, B., Blaauboer, B. J., Casey, W., Dorman, D. C., Forsby, A., et al. (2017) · Toxicology in Vitro · doi:10.1016/j.tiv.2017.01.004 · Cited by 76
Self-citationChiu, W. A. (2017) · ALTEX · doi:10.14573/altex.1703231 · Cited by 76
Cai, R., Ren, A., Liu, N., Ding, C., Wang, L., Qian, X., Pedram, M., & Wang, Y. (2018) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3173162.3173212 · Cited by 76
Luechtefeld, T. (2016) · ALTEX · doi:10.14573/altex.1510052 · Cited by 74
Shah, I., Setzer, R. W., Jack, J., Houck, K. A., Judson, R., Knudsen, T. B., Liu, J., Martin, M. T., Reif, D. M., Richard, A. M., et al. (2015) · Environmental Health Perspectives · doi:10.1289/ehp.1409029 · Cited by 80
Self-citationMacedo, B., Ribeiro‐Vaz, I., & Taveira‐Gomes, T. (2024) · Scientific Reports · doi:10.1038/s41598-023-50834-6 · Cited by 79
CEBS: a comprehensive annotated database of toxicological data
Lea, I. A., Gong, H., Paleja, A., Rashid, A., & Fostel, J. M. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw1077 · Cited by 73
Lynch, C., Sakamuru, S., Ooka, M., Huang, R., Klumpp-Thomas, C. A., Shinn, P., Gerhold, D. L., Rossoshek, A., Michael, S., Casey, W., et al. (2023) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-112122-104310 · Cited by 79
Self-citationAn integrative data mining approach to identifying adverse outcome pathway signatures
Oki, N. O., & Edwards, S. W. (2016) · Toxicology · doi:10.1016/j.tox.2016.04.004 · Cited by 68
Kim, M. T., Huang, R., Sedykh, A., Wang, W., Xia, M., & Zhu, H. (2015) · Environmental Health Perspectives · doi:10.1289/ehp.1509763 · Cited by 68
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Cases, M., Briggs, K. A., Steger‐Hartmann, T., Pognan, F., Marc, P., Kleinöder, T., Schwab, C. H., Pastor, M., Wichard, J., & Sanz, F. (2014) · International Journal of Molecular Sciences · doi:10.3390/ijms151121136 · Cited by 65
Ciallella, H. L., Russo, D. P., Aleksunes, L. M., Grimm, F. A., & Zhu, H. (2021) · Environmental Science & Technology · doi:10.1021/acs.est.1c02656 · Cited by 63
Zhang, S., Yan, Z., Huang, Y., Liu, L., He, D., Wang, W., Fang, X., Zhang, X., Wang, F., Wu, H. R., et al. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac342 · Cited by 68
The NCATS Pharmaceutical Collection: a 10-year update
Huang, R., Zhu, H., Shinn, P., Ngan, D. K., Ye, L., Thakur, A., Grewal, G. S., Zhao, T., Southall, N. T., Hall, M. D., et al. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.09.019 · Cited by 64
Self-citationApplication of a Dual-Arm Robot in Complex Sample Preparation and Measurement Processes
Fleischer, H., Drews, R. R., Janson, J., Patlolla, B. R. C., Chu, X., Klös, M., & Thurow, K. (2016) · SLAS TECHNOLOGY · doi:10.1177/2211068216637352 · Cited by 63
Mitochondrial Membrane Potential Assay
Sakamuru, S., Zhao, J., Attene‐Ramos, M. S., & Xia, M. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-2213-1_2 · Cited by 61
Self-citationCiallella, H. L., Russo, D. P., Aleksunes, L. M., Grimm, F. A., & Zhu, H. (2020) · Laboratory Investigation · doi:10.1038/s41374-020-00477-2 · Cited by 62
Fay, K. A., Villeneuve, D. L., Swintek, J., Edwards, S. W., Nelms, M., Blackwell, B. R., & Ankley, G. T. (2018) · Toxicological Sciences · doi:10.1093/toxsci/kfy049 · Cited by 56
Idakwo, G., Thangapandian, S., Luttrell, J., Zhou, Z., Zhang, C., & Gong, P. (2019) · Frontiers in Physiology · doi:10.3389/fphys.2019.01044 · Cited by 55
Rethinking Environmental Protection: Meeting the Challenges of a Changing World
Burke, T. A., Cascio, W. E., Costa, D. L., Deener, K., Fontaine, T. D., Fulk, F., Jackson, L. E., Munns, W. R., Orme–Zavaleta, J., Slimak, M. W., et al. (2017) · Environmental Health Perspectives · doi:10.1289/ehp1465 · Cited by 57
Characterizing sources of variability in zebrafish embryo screening protocols
Hamm, J. T. (2018) · ALTEX · doi:10.14573/altex.1804162 · Cited by 55
Large-Scale Predictive Drug Safety: From Structural Alerts to Biological Mechanisms
García-Serna, R., Vidal, D., Remez, N., & Mestres, J. (2015) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.5b00260 · Cited by 55
Biomarkers of nanomaterials hazard from multi-layer data
Fortino, V., Kinaret, P., Fratello, M., Serra, A., Saarimäki, L. A., Gallud, A., Gupta, G. S., Vales, G., Correia, M., Rasool, O., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31609-5 · Cited by 51
Hu, Z., Brooks, S. A., Dormoy, V., Hsu, C.-W., Hsu, H.-Y., Lin, L., Massfelder, T., Rathmell, W. K., Xia, M., Al‐Mulla, F., et al. (2015) · Carcinogenesis · doi:10.1093/carcin/bgv036 · Cited by 51
Self-citationMerrick, B. A., Paules, R. S., & Tice, R. R. (2015) · International Journal of Biotechnology · doi:10.1504/ijbt.2015.074797 · Cited by 50
Self-citationWei, Z., Liu, X., Ooka, M., Zhang, L., Song, M. J., Huang, R., Kleinstreuer, N., Simeonov, A. M., Xia, M., & Ferrer, M. (2020) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2020.00109 · Cited by 50
Self-citationHsu, C.-W., Zhao, J., Huang, R., Hsieh, J., Hamm, J. T., Chang, X., Houck, K. A., & Xia, M. (2014) · Scientific Reports · doi:10.1038/srep06437 · Cited by 49
Self-citationMachine learning for small molecule drug discovery in academia and industry
Volkamer, A., Riniker, S., Nittinger, E., Lanini, J., Grisoni, F., Evertsson, E., Rodríguez-Pérez, R., & Schneider, N. (2023) · Artificial Intelligence in the Life Sciences · doi:10.1016/j.ailsci.2022.100056 · Cited by 48
Matsuzaka, Y., & Uesawa, Y. (2019) · Frontiers in Bioengineering and Biotechnology · doi:10.3389/fbioe.2019.00065 · Cited by 48
Structure-based QSAR models to predict repeat dose toxicity points of departure
Pradeep, P., Friedman, K. P., & Judson, R. (2020) · Computational Toxicology · doi:10.1016/j.comtox.2020.100139 · Cited by 50
Uncertainty quantification in ToxCast high throughput screening
Watt, E. D., & Judson, R. (2018) · PLoS ONE · doi:10.1371/journal.pone.0196963 · Cited by 46
Pradeep, P., Patlewicz, G. Y., Pearce, R., Wambaugh, J. F., Wetmore, B. A., & Judson, R. (2020) · Computational Toxicology · doi:10.1016/j.comtox.2020.100136 · Cited by 45
Huang, R., Xia, M., Sakamuru, S., Zhao, J., Lynch, C., Zhao, T., Zhu, H., Austin, C. P., & Simeonov, A. M. (2018) · Scientific Reports · doi:10.1038/s41598-018-22046-w · Cited by 47
Self-citationPredictive Models for Human Organ Toxicity Based on In Vitro Bioactivity Data and Chemical Structure
Xu, T., Ngan, D. K., Ye, L., Xia, M., Xie, H. Q., Zhao, B., Simeonov, A. M., & Huang, R. (2020) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.9b00305 · Cited by 47
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The PrecisionTox Consortium (2023) · Toxicology Letters · doi:10.1016/j.toxlet.2023.05.004 · Cited by 47
Fischer, F. C., Abele, C., Droge, S. T. J., Henneberger, L., König, M., Schlichting, R., Scholz, S., & Escher, B. I. (2018) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.8b00019 · Cited by 46
Kurosaki, K., Wu, R., & Uesawa, Y. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21217853 · Cited by 44
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Li, S., Zhao, J., Huang, R., Travers, J., Klumpp-Thomas, C. A., Yu, W., MacKerell, A. D., Sakamuru, S., Ooka, M., Xue, F., et al. (2021) · Environmental Health Perspectives · doi:10.1289/ehp6993 · Cited by 43
Self-citationMatsuzaka, Y., & Uesawa, Y. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms20194855 · Cited by 40
Identifying Compounds with Genotoxicity Potential Using Tox21 High-Throughput Screening Assays
Hsieh, J., Smith‐Roe, S. L., Huang, R., Sedykh, A., Shockley, K. R., Auerbach, S. S., Merrick, B. A., Xia, M., Tice, R. R., & Witt, K. L. (2019) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.9b00053 · Cited by 38
Self-citationCorrection of Microplate Data from High-Throughput Screening
Wang, Y., & Huang, R. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-6346-1_13 · Cited by 38
Self-citationStrategies for automating analytical and bioanalytical laboratories
Thurow, K. (2023) · Analytical and Bioanalytical Chemistry · doi:10.1007/s00216-023-04727-2 · Cited by 36
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Deep Learning Model for Identifying Critical Structural Motifs in Potential Endocrine Disruptors
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Environmental Chemical Assessment in Clinical Practice: Unveiling the Elephant in the Room
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Quantitative high-throughput screening data analysis: challenges and recent advances
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Nishihara, K., Huang, R., Zhao, J., Shahane, S. A., Witt, K. L., Smith‐Roe, S. L., Tice, R. R., Takeda, S., & Xia, M. (2015) · Mutagenesis · doi:10.1093/mutage/gev055 · Cited by 26
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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.
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