Big data in chemical toxicity research: The use of high-throughput screening assays to identify potential toxicants
Open accessAbstract
High-throughput screening (HTS) assays that measure the in vitro toxicity of environmental compounds have been widely applied as an alternative to in vivo animal tests of chemical toxicity. Current HTS studies provide the community with rich toxicology information that has the potential to be integrated into toxicity research. The available in vitro toxicity data is updated daily in structured formats (e.g., deposited into PubChem and other data-sharing web portals) or in an unstructured way (papers, laboratory reports, toxicity Web site updates, etc.). The information derived from the current toxicity data is so large and complex that it becomes difficult to process using available database management tools or traditional data processing applications. For this reason, it is necessary to develop a big data approach when conducting modern chemical toxicity research. In vitro data for a compound, obtained from meaningful bioassays, can be viewed as a response profile that gives detailed information about the compound's ability to affect relevant biological proteins/receptors. This information is critical for the evaluation of complex bioactivities (e.g., animal toxicities) and grows rapidly as big data in toxicology communities. This review focuses mainly on the existing structured in vitro data (e.g., PubChem data sets) as response profiles for compounds of environmental interest (e.g., potential human/animal toxicants). Potential modeling and mining tools to use the current big data pool in chemical toxicity research are also described.
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Machine learning techniques may provide much more information about toxicity than ever before.
“Together with “Big Data” science [159], machine learning techniques may provide much more information about toxicity than ever before.”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [159]
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Ciallella, H. L., & Zhu, H. (2019) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.8b00393 · Cited by 227 · see citing works
Self-citationWu, Y., & Wang, G. (2018) · International Journal of Molecular Sciences · doi:10.3390/ijms19082358 · Cited by 193 · see citing works
Comparing Multiple Machine Learning Algorithms and Metrics for Estrogen Receptor Binding Prediction
Russo, D. P., Zorn, K. M., Clark, A. M., Zhu, H., & Ekins, S. (2018) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.8b00546 · Cited by 152 · see citing works
Self-citationGellatly, N., & Sewell, F. (2019) · Computational Toxicology · doi:10.1016/j.comtox.2019.03.003 · Cited by 43 · see citing works
- Authors
- Zhu, H., Zhang, J., Kim, M. T., Boison, A., Sedykh, A., & Moran, K.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Center for Forensic Science Research and Education , , ,
- Corresponding author
- Zhu, Hao
- Venue
- Chemical Research in Toxicology
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-09-07
- Volume
- 27
- Issue
- 10
- Pages
- 1643-1651
- ISSN
- 0893-228X (ISSN-L)
- 1520-5010
- PMC
- PMC4203392
- OpenAlex
- W2322262466
- Cited by
- 154
- References
- 95
- Citations by year
- 2026: 7 · 2025: 8 · 2024: 12 · 2023: 11 · 2022: 14 · 2021: 12 · 2020: 16 · 2019: 20 · 2018: 15 · 2017: 14 · 2016: 14 · 2015: 9 · 2014: 1 · 2012: 1
- FWCI
- 1.02
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Chemical Research in Toxicology (publishedVersion, other-oa)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Colgate-Palmolive Company
- National Institutes of Health (R15ES023148)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Environmental Toxicology and Ecotoxicology
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Keywords
- big data, chemical toxicity, high-throughput screening, in vitro toxicity testing, environmental toxins, PubChem, response profiles, toxicity modeling, alternatives to animal testing, data integration
- MeSH terms
- Animals, Biological Assay, Humans, Inorganic Chemicals, Organic Chemicals, Databases, Factual, Toxicity Tests, Toxicogenetics, High-Throughput Screening Assays
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