ReviewTrends in pharmacological sciences2019
Artificial Intelligence for Drug Toxicity and Safety.
Review in Trends in pharmacological sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers, 2 of them syntheses that pooled it.
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
98 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Transformative Role of Artificial Intelligence in Drug Discovery and Translational Medicine: Innovations, Challenges, and Future Prospects.Drug design, development and therapy · 2025Pooled it
- The Use of Artificial Intelligence in Pharmacovigilance: A Systematic Review of the Literature.Pharmaceutical medicine · 2022Pooled it
- Image-guided activation of drugs with electromagnetic radiation.Nature chemical biology · 2026Review
- Beyond Molecular Structures: Investigating Demographic Factors in Drug-Induced Cardiotoxicity Prediction Models.Journal of chemical information and modeling · 2026Article
- Prediction of Ligand Binding to Transthyretin Using Machine Learning Algorithms and Low-Dimensional Molecular Descriptors: A Tox24 Challenge Study.Chemical research in toxicology · 2026Article
- Framework for artificial intelligence implementation research in healthcare: synthesizing current evidence on barriers and facilitators.NPJ digital medicine · 2026Article
- AI-Enabled Surveillance and Modelling for Counterfeit Botulinum Toxin A: Risk Projection, Patient Safety, and Systemic Reform of Pharmacovigilance.Aesthetic plastic surgery · 2026Article
- AI and Big Data in Oncology: A Physician-Centered Perspective on Emerging Clinical and Research Applications.Cancer innovation · 2026Review
- KidneyTox_v1.0 enables explainable artificial intelligence prediction of nephrotoxicity in small molecules.Scientific reports · 2026Article
- Leveraging AI for cell biology discovery.Biochemical Society transactions · 2026Review
- Pharmacoepidemiology unpacked: a roadmap for junior researchers.Frontiers in pharmacology · 2026Review
- Data-driven precision: artificial intelligence redefining immunoradiotherapy in advanced pancreatic cancer.Frontiers in pharmacology · 2026Review
- Artificial Intelligence Revolution in Pharmaceutical Sciences : Advancements, Clinical Impacts, and Applications.Current pharmaceutical biotechnology · 2026Review
- Artificial intelligence for radiopharmaceutical and molecular imaging.Acta pharmaceutica Sinica. B · 2025Review
- Pediatric Developmental Safety Assessment: Are We Ready for the Next Thalidomide?Clinical pharmacology and therapeutics · 2025Article
- A review on adverse drug reaction related to medication in health sector: an account of what we have discovered and implemented-pharmacovigilance.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- A review on adverse drug reaction related to medication in health sector: an account of what we have discovered and implemented-pharmacovigilance.Naunyn-Schmiedeberg's archives of pharmacology · 2025Review
- MediNet: ensemble transfer learning approach for classification of medical drugs-related text reviews using significant combined-embeddings.BioData mining · 2025Article
- Toxicity Profiling andToxins · 2025Article
- The Adoption and Use of Artificial Intelligence and Machine Learning in Clinical Development.Therapeutic innovation & regulatory science · 2025Article
38 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
3 authors.
Funding
Abstract
Interventional pharmacology is one of medicine's most potent weapons against disease. These drugs, however, can result in damaging side effects and must be closely monitored. Pharmacovigilance is the field of science that monitors, detects, and prevents adverse drug reactions (ADRs). Safety efforts begin during the development process, using in vivo and in vitro studies, continue through clinical trials, and extend to postmarketing surveillance of ADRs in real-world populations. Future toxicity and safety challenges, including increased polypharmacy and patient diversity, stress the limits of these traditional tools. Massive amounts of newly available data present an opportunity for using artificial intelligence (AI) and machine learning to improve drug safety science. Here, we explore recent advances as applied to preclinical drug safety and postmarketing surveillance with a specific focus on machine and deep learning (DL) approaches.
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.