ArticleMolecular pharmaceutics2020
Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI).
Article in Molecular pharmaceutics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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Who cites it
37 citing papers in PubMed, 97 citations in OpenAlex.
- Context-Aware Multilevel Classification of Semantic Relations in Drug-Adverse Drug Reaction (ADR) Networks-Predicting Drug-Induced Liver Injury (DILI) as a Case Study.Chemical research in toxicology · 2026Article
- Machine Learning-Based Models to Predict Drug-Induced Liver Injury (DILI) to Assist Medicinal Chemistry.Journal of medicinal chemistry · 2026Review
- Integration of in vitro and in silico approaches enables prediction of drug-induced liver injury.Archives of toxicology · 2026Article
- Evolution of artificial intelligence and machine learning in DILI toxicogenomics: from descriptive profiling to mechanistic insights.Frontiers in pharmacology · 2026Review
- Drug-induced liver injury prediction based on graph convolutional networks and toxicogenomics.PLoS computational biology · 2025Article
- Leveraging FDA Labeling Documents and Large Language Model to Enhance Annotation, Profiling, and Classification of Drug Adverse Events with AskFDALabel.Drug safety · 2025Article
- Mechanisms of drug induced liver injury.Cellular and molecular life sciences : CMLS · 2025Review
- Deep Learning Prediction of Drug-Induced Liver Toxicity by Manifold Embedding of Quantum Information of Drug Molecules.Pharmaceutical research · 2025Article
- Real-world data-driven early warning system for risk-stratified liver injury in hospitalized COVID-19 patients-Machine learning models for clinical decision support.Frontiers in public health · 2025Article
- Pharmacist-Led Assessment of Drug-Induced Liver Injury in Hospitalized Patients.The Canadian journal of hospital pharmacy · 2025Article
- Future Perspectives on Radiomics in Acute Liver Injury and Liver Trauma.Journal of personalized medicine · 2024Review
- Mining Real-World Big Data to Characterize Adverse Drug Reaction Quantitatively: Mixed Methods Study.Journal of medical Internet research · 2024Article
- Advancing Adverse Drug Reaction Prediction with Deep Chemical Language Model for Drug Safety Evaluation.International journal of molecular sciences · 2024Article
- Interpretable machine learning in predicting drug-induced liver injury among tuberculosis patients: model development and validation study.BMC medical research methodology · 2024Article
- A comprehensive overview of advanced dynamicTissue barriers · 2024Review
- Review of machine learning and deep learning models for toxicity prediction.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- Advancements in small molecule drug design: A structural perspective.Drug discovery today · 2023Review
- The evolving role of investigative toxicology in the pharmaceutical industry.Nature reviews. Drug discovery · 2023Review
- The protein disulfide isomerase inhibitor 3-methyltoxoflavin inhibits Chikungunya virus.Bioorganic & medicinal chemistry · 2023Article
- Combining machine learning and structure-based approaches to develop oncogene PIM kinase inhibitors.Frontiers in chemistry · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Drug-induced liver injury (DILI) is one the most unpredictable adverse reactions to xenobiotics in humans and the leading cause of postmarketing withdrawals of approved drugs. To date, these drugs have been collated by the FDA to form the DILIRank database, which classifies DILI severity and potential. These classifications have been used by various research groups in generating computational predictions for this type of liver injury. Recently, groups from Pfizer and AstraZeneca have collated DILI
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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.