Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity
Open accessAbstract
In 2016, the Frank R. Lautenberg Chemical Safety for the 21st Century Act became the first US legislation to advance chemical safety evaluations by utilizing novel testing approaches that reduce the testing of vertebrate animals. Central to this mission is the advancement of computational toxicology and artificial intelligence approaches to implementing innovative testing methods. In the current big data era, the terms volume (amount of data), velocity (growth of data), and variety (the diversity of sources) have been used to characterize the currently available chemical, in vitro, and in vivo data for toxicity modeling purposes. Furthermore, as suggested by various scientists, the variability (internal consistency or lack thereof) of publicly available data pools, such as PubChem, also presents significant computational challenges. The development of novel artificial intelligence approaches based on public massive toxicity data is urgently needed to generate new predictive models for chemical toxicity evaluations and make the developed models applicable as alternatives for evaluating untested compounds. In this procedure, traditional approaches (e.g., QSAR) purely based on chemical structures have been replaced by newly designed data-driven and mechanism-driven modeling. The resulting models realize the concept of adverse outcome pathway (AOP), which can not only directly evaluate toxicity potentials of new compounds, but also illustrate relevant toxicity mechanisms. The recent advancement of computational toxicology in the big data era has paved the road to future toxicity testing, which will significantly impact on the public health.
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For 15 absorption, distribution, metabolism, excretion, and toxicity (ADMET) data sets of drug candidates, DL models outperformed classic ML approaches in terms of predictability.
“For 15 absorption, distribution, metabolism, excretion, and toxicity (ADMET) data sets of drug candidates, DL models outperformed classic ML approaches in terms of predictability”
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- Authors
- Ciallella, H. L., & Zhu, H.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- Corresponding author
- Zhu, Hao
- Venue
- Chemical Research in Toxicology
- Publisher
- American Chemical Society (ACS)
- Published
- 2019-03-14
- Volume
- 32
- Issue
- 4
- Pages
- 536-547
- ISSN
- 0893-228X (ISSN-L)
- 1520-5010
- PMC
- PMC6688471
- OpenAlex
- W2922283117
- Cited by
- 227
- References
- 135
- Citations by year
- 2026: 22 · 2025: 45 · 2024: 36 · 2023: 46 · 2022: 29 · 2021: 25 · 2020: 19 · 2019: 5
- FWCI
- 1.62
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- Chemical Research in Toxicology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Colgate-Palmolive Company
- Center for Alternatives to Animal Testing, Johns Hopkins Bloomberg School of Public Health
- National Institute of Environmental Health Sciences (R15ES023148)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- computational toxicology, artificial intelligence, chemical toxicity prediction, adverse outcome pathways, data-driven modeling, big data, alternatives to animal testing
- MeSH terms
- Animals, Artificial Intelligence, Humans, Toxicity Tests, Computational Biology
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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.
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