ReviewChemical research in toxicology2019
Advancing Computational Toxicology in the Big Data Era by Artificial Intelligence: Data-Driven and Mechanism-Driven Modeling for Chemical Toxicity.
Review in Chemical research in toxicology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 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
61 citing papers in PubMed.
- Artificial intelligence in toxicology: current advances, challenges and future directions.Archives of toxicology · 2026Review
- A Narrative Review of Artificial Intelligence for Drug Repurposing: Lessons From COVID-19 and Oncology (2020-2025).CPT: pharmacometrics & systems pharmacology · 2026Review
- Democratizing Artificial Intelligence in Toxicology: Real-World Applications and Automated Computational Workflows.Chemical research in toxicology · 2026Review
- Artificial Intelligence Across the Drug Development Lifecycle.Medical sciences (Basel, Switzerland) · 2026Review
- Essence of toxicology research to Saudi Arabia Vision 2030 and beyond: Current state and future perspectives.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2026Article
- Machine Learning Models for High-Throughput Screening of Obesogens Based on Pathway Network.Environment & health (Washington, D.C.) · 2026Article
- Artificial Intelligence Revolution in Pharmaceutical Sciences : Advancements, Clinical Impacts, and Applications.Current pharmaceutical biotechnology · 2026Review
- Machine Learning Modeling for ABC Transporter Efflux and Inhibition: Data Curation, Model Development, and New Compound Interaction Predictions.Molecular pharmaceutics · 2025Article
- Uncovering the role of chemical pollutants in shaping biological invasions.Proceedings. Biological sciences · 2025Review
- Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives.European journal of medical research · 2025Review
- Developmental toxicity: artificial intelligence-powered assessments.Trends in pharmacological sciences · 2025Review
- The Potential of Artificial Intelligence in Pharmaceutical Innovation: From Drug Discovery to Clinical Trials.Pharmaceuticals (Basel, Switzerland) · 2025Review
- Machine Learning for Toxicity Prediction Using Chemical Structures: Pillars for Success in the Real World.Chemical research in toxicology · 2025Review
- Advancing the Spatiotemporal Dimension of Wildlife-Pollution Interactions.Environmental science & technology letters · 2025Review
- Article
- AI in Clinical Trials and Drug Development: Challenges and Potential Advancements.Current drug discovery technologies · 2025Review
- Role of Artificial Intelligence in Drug Discovery to Revolutionize the Pharmaceutical Industry: Resources, Methods and Applications.Recent patents on biotechnology · 2025Review
- Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives.Current drug targets · 2025Review
- Recent Development, Applications, and Patents of Artificial Intelligence in Drug Design and Development.Current drug discovery technologies · 2025Review
- Drug repurposing of natural depsipeptide fromFrontiers in pharmacology · 2025Article
1 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
2 authors.
Funding
Abstract
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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What OpenQuestion holds
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.