ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025
Machine Learning-Enabled Drug-Induced Toxicity Prediction.
Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed.
- Artificial Intelligence-Powered One Health: A Predictive Framework for Managing Persistent Chemical Threats across Human, Animal, and Environmental Systems.Global challenges (Hoboken, NJ) · 2026Review
- Predictive modeling of heavy metal pollution and ecological risk for sustainable water quality management in the NY-NJ harbor system.Environmental monitoring and assessment · 2026Article
- Integrating Artificial Intelligence Into Drug Discovery From Medicinal Plants: Current Applications and Infrastructural Challenges.Chemical biology & drug design · 2026Review
- Targeting the osteoporotic bone microenvironment: Mechanistic insight and therapeutic biomaterials for accelerating bone regeneration.Bioactive materials · 2026Review
- Domain-generalized representation learning for cross-chemical-family toxicity prediction.Scientific reports · 2026Article
- Multi-omics and genetic prioritization identify candidate molecular links between micro- and nanoplastics-associated signatures and chronic kidney disease.Molecular diversity · 2026Article
- Machine Learning-Based Models to Predict Drug-Induced Liver Injury (DILI) to Assist Medicinal Chemistry.Journal of medicinal chemistry · 2026Review
- Multi-omics and artificial intelligence for precision drug discovery and potential clinical applications.Signal transduction and targeted therapy · 2026Review
- Exploring the Nutraceutical Potential ofFood science & nutrition · 2026Review
- Machine Learning Model Predicts Clinical Adverse Events of Small Molecule Kinase Inhibitors in Cancer Patients Using On-/Off-Target Engagement and Tissue Selectivity.Clinical pharmacology and therapeutics · 2026Article
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- A new era of precision diagnosis and treatment for lung cancer: artificial intelligence-driven multimodal data integration and clinical applications.Cell death & disease · 2026Review
- Focusing on toxicity management: Challenges and strategies for HER2-targeted antibody-drug conjugates in breast cancer.Breast (Edinburgh, Scotland) · 2026Review
- Integrating Human Intestinal Organoids into FDA's New Approach Methodologies for Drug Discovery.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Integrating Network Toxicology, Machine Learning, and Molecular Dynamics to Explore the Molecular Network of Triclosan-Induced Acute Myocardial Infarction.International journal of molecular sciences · 2026Article
- Research progress in animal models of dry eye disease: Types, mechanisms, and application prospects.Animal models and experimental medicine · 2026Review
- Molecular docking approaches in mycetoma: Toward improved patient management.PLoS neglected tropical diseases · 2026Review
- Machine learning models for drug-drug interaction prediction from computational discovery to clinical application.NPJ digital medicine · 2026Review
- Exploring the toxicological network in diabetic microvascular disease: a commentary.International journal of surgery (London, England) · 2026Article
- From Policy to Practice: Advancing Environmental Toxicology through New Approach Methodologies.Environment & health (Washington, D.C.) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Unexpected toxicity has become a significant obstacle to drug candidate development, accounting for 30% of drug discovery failures. Traditional toxicity assessment through animal testing is costly and time-consuming. Big data and artificial intelligence (AI), especially machine learning (ML), are robustly contributing to innovation and progress in toxicology research. However, the optimal AI model for different types of toxicity usually varies, making it essential to conduct comparative analyses of AI methods across toxicity domains. The diverse data sources also pose challenges for researchers focusing on specific toxicity studies. In this review, 10 categories of drug-induced toxicity is examined, summarizing the characteristics and applicable ML models, including both predictive and interpretable algorithms, striking a balance between breadth and depth. Key databases and tools used in toxicity prediction are also highlighted, including toxicology, chemical, multi-omics, and benchmark databases, organized by their focus and function to clarify their roles in drug-induced toxicity prediction. Finally, strategies to turn challenges into opportunities are analyzed and discussed. This review may provide researchers with a valuable reference for understanding and utilizing the available resources to bridge prediction and mechanistic insights, and further advance the application of ML in drugs-induced toxicity prediction.
Indexed as
Identifiers
What Socratic 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 Socratic graph.