ReviewToxicological sciences : an official journal of the Society of Toxicology2022
Machine Learning and Artificial Intelligence in Toxicological Sciences.
Review in Toxicological sciences : an official journal of the Society of Toxicology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 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
56 citing papers in PubMed, 147 citations in OpenAlex.
- Colonoid-Based Transcriptomics Reveals Conserved and Model-Specific Mechanisms of Doxorubicin-Induced Intestinal Toxicity.International journal of molecular sciences · 2026Article
- A review of machine learning in toxicology: current practices and reporting gaps.Archives of toxicology · 2026Review
- AI/ML-based computational models for toxicity prediction.Environmental science and pollution research international · 2026Review
- Electronic Waste-Derived Nanomaterials: Environmental Release, Toxicity, and Public Health Risks.Nanomaterials (Basel, Switzerland) · 2026Review
- Identifying predictive hematological biomarkers for radiation exposure by machine learning in mouse models.Communications medicine · 2026Article
- Integrated assessment of developmental toxicity of antibiotic pollutants: Machine learning prediction, zebrafish validation, and network toxicology.Molecular diversity · 2026Article
- Advances in the application of computational toxicology in cosmetic safety assessment.Archives of toxicology · 2026Review
- Development of machine learning-based multi-task quantitative structure-activity relationship models for predicting toxicities in six human organ systems.Computational toxicology (Amsterdam, Netherlands) · 2026Article
- Phthalate Esters in Aquatic Ecosystems: A Multiscale Threat from Molecular Disruption to Ecological Risks.Toxics · 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
- In Silico Hazard Assessment of Ototoxicants Through Machine Learning and Computational Systems Biology.Toxics · 2026Article
- Evolution of artificial intelligence and machine learning in DILI toxicogenomics: from descriptive profiling to mechanistic insights.Frontiers in pharmacology · 2026Review
- Human-centered AI in healthcare teams: integrating clinical oversight, EHR auditability, and reimbursement pathways for responsible adoption.Frontiers in digital health · 2026Review
- From animal testing to model-informed drug development: building on ICH M15 and EMA initiatives to make new approach methodologies (NAMs) deliver.Frontiers in pharmacology · 2026Article
- Cancer and Environmental Xenobiotics: Mechanisms, Controversies, and Innovations.Journal of xenobiotics · 2025Review
- Artificial intelligence in clinical toxicology in Africa: Emerging applications and barriers.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2025Review
- Recent Advances in Machine Learning Models for Predicting Toxicity of Inorganic Nanoparticles.Chem & bio engineering · 2025Review
- Advances in Cytotoxicity Testing: From In Vitro Assays to In Silico Models.International journal of molecular sciences · 2025Review
- A Machine Learning-Empowered Quantitative Structure-Activity Relationship Model for Predicting the Plasma Half-life of Drugs in Dogs.The AAPS journal · 2025Article
- AIVIVE: a novel AI framework for enhanced in vitro to in vivo extrapolation (IVIVE) of toxicogenomics data.Toxicological sciences : an official journal of the Society of Toxicology · 2025Article
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 at 1 institution in 1 country.
Funding
Abstract
Machine learning and artificial intelligence approaches have revolutionized multiple disciplines, including toxicology. This review summarizes representative recent applications of machine learning and artificial intelligence approaches in different areas of toxicology, including physiologically based pharmacokinetic (PBPK) modeling, quantitative structure-activity relationship modeling for toxicity prediction, adverse outcome pathway analysis, high-throughput screening, toxicogenomics, big data, and toxicological databases. By leveraging machine learning and artificial intelligence approaches, now it is possible to develop PBPK models for hundreds of chemicals efficiently, to create in silico models to predict toxicity for a large number of chemicals with similar accuracies compared with in vivo animal experiments, and to analyze a large amount of different types of data (toxicogenomics, high-content image data, etc.) to generate new insights into toxicity mechanisms rapidly, which was impossible by manual approaches in the past. To continue advancing the field of toxicological sciences, several challenges should be considered: (1) not all machine learning models are equally useful for a particular type of toxicology data, and thus it is important to test different methods to determine the optimal approach; (2) current toxicity prediction is mainly on bioactivity classification (yes/no), so additional studies are needed to predict the intensity of effect or dose-response relationship; (3) as more data become available, it is crucial to perform rigorous data quality check and develop infrastructure to store, share, analyze, evaluate, and manage big data; and (4) it is important to convert machine learning models to user-friendly interfaces to facilitate their applications by both computational and bench scientists.
Indexed as
Identifiers
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.