DILIrank: the largest reference drug list ranked by the risk for developing drug-induced liver injury in humans

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The Liver Toxicity Knowledge Base: A Systems Approach to a Complex End Point

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Antidepressant-induced hepatotoxicity

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Construction and analysis of a human hepatotoxicity database suitable for QSAR modeling using post-market safety data

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Detection of hepatotoxicity potential with metabolite profiling (metabolomics) of rat plasma

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Developing a QSAR model for hepatotoxicity screening of the active compounds in traditional Chinese medicines

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A comprehensive study of the association between drug hepatotoxicity and daily dose, liver metabolism, and lipophilicity using 975 oral medications

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A testing strategy to predict risk for drug-induced liver injury in humans using high-content screen assays and the ‘rule-of-two’ model

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Is Toxicogenomics a More Reliable and Sensitive Biomarker than Conventional Indicators from Rats To Predict Drug-Induced Liver Injury in Humans?

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Predictivity of dog co-culture model, primary human hepatocytes and HepG2 cells for the detection of hepatotoxic drugs in humans

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Analysis of Pfizer Compounds in EPA’s ToxCast Chemicals-Assay Space

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Predicting idiosyncratic drug-induced liver injury – some recent advances

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63 Years and 715 Days to the “Boxed Warning”: Unmasking of the Propylthiouracil Problem

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Multiparametric assay using HepaRG cells for predicting drug-induced liver injury

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Drug-induced liver disease

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Hepatotoxicity of Psychotropic Drugs and Drugs of Abuse

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63 Years and 715 Days to the "Boxed Warning": Unmasking of the Propylthiouracil Problem

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Drug-induced liver injury classification model based on in vitro human transcriptomics and in vivo rat clinical chemistry data

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DRUG-INDUCED HEPATIC INJURY: MARPLAN HEPATITIS

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EASL Clinical Practice Guidelines: Drug-induced liver injury

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Predicting Drug-Induced Liver Injury with Bayesian Machine Learning

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Drug Induced Liver Injury (DILI). Mechanisms and Medicinal Chemistry Avoidance/Mitigation Strategies

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ResNet18DNN: prediction approach of drug-induced liver injury by deep neural network with ResNet18

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Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI)

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TOXRIC: a comprehensive database of toxicological data and benchmarks

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Development of Decision Forest Models for Prediction of Drug-Induced Liver Injury in Humans Using A Large Set of FDA-approved Drugs

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Review of machine learning and deep learning models for toxicity prediction

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The evolution of strategies to minimise the risk of human drug-induced liver injury (DILI) in drug discovery and development

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Predicting drug-induced liver injury: The importance of data curation

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Calcium Signaling in Liver Injury and Regeneration

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Study of serious adverse drug reactions using FDA-approved drug labeling and MedDRA

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Dynamic imaging of adaptive stress response pathway activation for prediction of drug induced liver injury

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Recent advances in AI-based toxicity prediction for drug discovery

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Associations of Drug Lipophilicity and Extent of Metabolism with Drug-Induced Liver Injury

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Measures of BSEP Inhibition In Vitro Are Not Useful Predictors of DILI

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Predicting Drug-Induced Liver Injury Using Convolutional Neural Network and Molecular Fingerprint-Embedded Features

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Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury

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