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

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PubChem 2023 update

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EASL Clinical Practice Guidelines: Drug-induced liver injury

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AASLD practice guidance on drug, herbal, and dietary supplement–induced liver injury

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Utility of spherical human liver microtissues for prediction of clinical drug-induced liver injury

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Artificial Intelligence in Drug Toxicity Prediction: Recent Advances, Challenges, and Future Perspectives

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Drug-induced liver injury: a comprehensive review

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Prediction Is a Balancing Act: Importance of Sampling Methods to Balance Sensitivity and Specificity of Predictive Models Based on Imbalanced Chemical Data Sets

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A 3D microfluidic liver model for high throughput compound toxicity screening in the OrganoPlate®

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An Overview of Machine Learning and Big Data for Drug Toxicity Evaluation

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Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery

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Application of artificial intelligence and machine learning in early detection of adverse drug reactions (ADRs) and drug-induced toxicity

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3D Primary Hepatocyte Culture Systems for Analyses of Liver Diseases, Drug Metabolism, and Toxicity: Emerging Culture Paradigms and Applications

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Drug-induced liver injury severity and toxicity (DILIst): binary classification of 1279 drugs by human hepatotoxicity

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Predicting Drug-Induced Liver Injury with Bayesian Machine Learning

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Cell Painting: a decade of discovery and innovation in cellular imaging

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An In Silico Model for Predicting Drug-Induced Hepatotoxicity

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

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Computational toxicology in drug discovery: applications of artificial intelligence in ADMET and toxicity prediction

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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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Predicting drug-induced liver injury in human with Naïve Bayes classifier approach

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Prediction of adverse drug reactions based on knowledge graph embedding

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

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Performance of preclinical models in predicting drug-induced liver injury in humans: a systematic review

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Artificial Intelligence in Drug Design: Algorithms, Applications, Challenges and Ethics

Arabi, A. A. (2021) · Future Drug Discovery · doi:10.4155/fdd-2020-0028 · Cited by 75

Intense bitterness of molecules: Machine learning for expediting drug discovery

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Recent advances in AI-based toxicity prediction for drug discovery

Lee, H., Kim, J., Kim, J.-W., & Lee, Y. (2025) · Frontiers in Chemistry · doi:10.3389/fchem.2025.1632046 · Cited by 76

Predicting drug-induced liver injury using graph attention mechanism and molecular fingerprints

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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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Current limitations and future opportunities for prediction of DILI from in vitro

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

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Artificial Intelligence in Small-Molecule Drug Discovery: A Critical Review of Methods, Applications, and Real-World Outcomes

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The Liver Toxicity Knowledge Base (LKTB) and drug-induced liver injury (DILI) classification for assessment of human liver injury

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Key Challenges and Opportunities Associated with the Use of In Vitro Models to Detect Human DILI: Integrated Risk Assessment and Mitigation Plans

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Moving beyond Binary Predictions of Human Drug-Induced Liver Injury (DILI) toward Contrasting Relative Risk Potential

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A knowledge graph embedding based approach to predict the adverse drug reactions using a deep neural network

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Ensemble Models Based on QuBiLS-MAS Features and Shallow Learning for the Prediction of Drug-Induced Liver Toxicity: Improving Deep Learning and Traditional Approaches

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Using chemical and biological data to predict drug toxicity

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Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure

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Three-dimensional liver models: state of the art and their application for hepatotoxicity evaluation

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Impact of Physicochemical Properties on Dose and Hepatotoxicity of Oral Drugs

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Systematic Approach to Organizing Structural Alerts for Reactive Metabolite Formation from Potential Drugs

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Liver injury and ulipristal acetate: an overstated tragedy?

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Prediction of Drug-Induced Liver Toxicity Using SVM and Optimal Descriptor Sets

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InterDILI: interpretable prediction of drug-induced liver injury through permutation feature importance and attention mechanism

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Insights into Drug Cardiotoxicity from Biological and Chemical Data: The First Public Classifiers for FDA Drug-Induced Cardiotoxicity Rank

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Organic solute transporter OSTα/β is overexpressed in nonalcoholic steatohepatitis and modulated by drugs associated with liver injury

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Strategies for Early Prediction and Timely Recognition of Drug-Induced Liver Injury: The Case of Cyclin-Dependent Kinase 4/6 Inhibitors

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Mechanism-driven modeling of chemical hepatotoxicity using structural alerts and an in vitro screening assay

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Prediction of drug-induced liver injury and cardiotoxicity using chemical structure and in vitro assay data

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The Promise of AI for DILI Prediction

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Machine learning liver-injuring drug interactions with non-steroidal anti-inflammatory drugs (NSAIDs) from a retrospective electronic health record (EHR) cohort

Datta, A., Flynn, N. R., Barnette, D. A., Woeltje, K. F., Miller, G. P., & Swamidass, S. J. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009053 · Cited by 49

Improved Detection of Drug-Induced Liver Injury by Integrating Predicted In Vivo and In Vitro Data

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A survey on computational taste predictors

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A review of drug-induced liver injury databases

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Machine learning in toxicological sciences: opportunities for assessing drug toxicity

Tonoyan, L., & Siraki, A. G. (2024) · Frontiers in Drug Discovery · doi:10.3389/fddsv.2024.1336025 · Cited by 43

Analytical and Omics-Based Advances in the Study of Drug-Induced Liver Injury

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Developing an artificial intelligence method for screening hepatotoxic compounds in traditional Chinese medicine and Western medicine combination

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Applications of Artificial Intelligence in Drug Design: Opportunities and Challenges

Thomas, M. C., Boardman, A., García-Ortegón, M., Yang, H., de Graaf, C., & Bender, A. (2021) · Methods in molecular biology · doi:10.1007/978-1-0716-1787-8_1 · Cited by 43

In Silico Prediction of Human Organ Toxicity via Artificial Intelligence Methods

Hu, Y., Ren, Q., Liu, X., Gao, L., Xiao, L., & Yu, W. (2023) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.2c00411 · Cited by 42

DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling

Qu, Y., Li, T., Liu, Z., Li, D., & Tong, W. (2023) · Drug Discovery Today · doi:10.1016/j.drudis.2023.103770 · Cited by 41

In silico Identification and Mechanism Exploration of Hepatotoxic Ingredients in Traditional Chinese Medicine

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Deep Graph Learning with Property Augmentation for Predicting Drug-Induced Liver Injury

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Adverse Impacts of PEGylated Protein Therapeutics: A Targeted Literature Review

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Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset

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Drug-Induced Rhabdomyolysis Atlas (DIRA) for idiosyncratic adverse drug reaction management

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Models of Idiosyncratic Drug-Induced Liver Injury

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AI/ML Models to Predict the Severity of Drug-Induced Liver Injury for Small Molecules

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Drug-induced liver injury associated with elexacaftor/tezacaftor/ivacaftor: A pharmacovigilance analysis of the FDA adverse event reporting system (FAERS)

Shi, A., Nguyen, H., Kuo, C. B., & Beringer, P. M. (2024) · Journal of Cystic Fibrosis · doi:10.1016/j.jcf.2024.01.001 · Cited by 36

In Silico Prediction of Drug-Induced Liver Injury Based on Ensemble Classifier Method

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Integrating Drug’s Mode of Action into Quantitative Structure–Activity Relationships for Improved Prediction of Drug-Induced Liver Injury

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The development of a database for herbal and dietary supplement induced liver toxicity

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Epidemiology and Risk Determinants of Drug‐Induced Liver Injury: Current Knowledge and Future Research Needs

Suzuki, A., & MinjunChen (2024) · Liver International · doi:10.1111/liv.16146 · Cited by 37

Characterisation of the NRF2 transcriptional network and its response to chemical insult in primary human hepatocytes: implications for prediction of drug-induced liver injury

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Modeling drug-induced liver injury: current status and future prospects

Zeo-Sánchez, D. E. D., Segovia‐Zafra, A., Matilla‐Cabello, G., Pinazo-Bandera, J. M., Andrade, R. J., Lucena, M. I., & Paz, M. V. (2022) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1080/17425255.2022.2122810 · Cited by 33

Considerations from an international regulatory and pharmaceutical industry (IQ MPS affiliate) workshop on the standardization of complex in vitro models in drug development

Tomlinson, L., Ramsden, D., Leite, S. B., Beken, S., Bonzo, J. A., Brown, P., Candarlioglu, P. L., Chan, T. S., Chen, E., Choi, C. K., et al. (2024) · Advanced Biology · doi:10.1002/adbi.202300131 · Cited by 33

In silico toxicity studies of traditional Chinese herbal medicine: A mini review

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Comparing the performance and coverage of selected in silico (liver) metabolism tools relative to reported studies in the literature to inform analogue selection in read-across: A case study

Boyce, M. W., Meyer, B. N., Grulke, C., Lizarraga, L. E., & Patlewicz, G. Y. (2021) · Computational Toxicology · doi:10.1016/j.comtox.2021.100208 · Cited by 30

Metabolomics-based strategy to assess drug hepatotoxicity and uncover the mechanisms of hepatotoxicity involved

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Preserving mitochondrial homeostasis protects against drug-induced liver injury via inducing OPTN (optineurin)-dependent Mitophagy

Wang, J., Qiu, Y., Yang, L., Wang, J., He, J., Tang, C., Yang, Z., Hong, W., Yang, B., He, Q., et al. (2024) · Autophagy · doi:10.1080/15548627.2024.2384348 · Cited by 30

Medical Therapy for Fibroids: What Next for Ulipristal Acetate?

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Quantitative Structure–Activity Relationship Models for Predicting Risk of Drug-Induced Liver Injury in Humans

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GeoDILI: A Robust and Interpretable Model for Drug-Induced Liver Injury Prediction Using Graph Neural Network-Based Molecular Geometric Representation

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Microfluidic human physiomimetic liver model as a screening platform for drug induced liver injury

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Drug-induced liver injury: Towards early prediction and risk stratification

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Evaluation of a Three-Dimensional Primary Human Hepatocyte Spheroid Model: Adoption and Industrialization for the Enhanced Detection of Drug-Induced Liver Injury

Schofield, C. A., Walker, T. M., Taylor, M. A., Patel, M., Vlachou, D. F., Macina, J. M., Vidgeon-Hart, M., Williams, A., McGill, P., Newman, C. F., et al. (2021) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.1c00227 · Cited by 29

BERT-Based Natural Language Processing of Drug Labeling Documents: A Case Study for Classifying Drug-Induced Liver Injury Risk

Wu, Y., Liu, Z., Wu, L., Chen, M., & Tong, W. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.729834 · Cited by 26

Systematic Identification of Molecular Targets and Pathways Related to Human Organ Level Toxicity

Xu, T., Wu, L., Xia, M., Simeonov, A. M., & Huang, R. (2020) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.0c00305 · Cited by 28

DILIrank dataset for QSAR modeling of drug-induced liver injury

Chen, M., Liao, T.-J., Li, D., Ning, B., Hong, H., & Tong, W. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15339-6.00014-x

FDA use of the black box warning: time for reevaluation as a safety tool

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DILIrank 2.0: An updated and expanded database for drug-induced liver injury risk based on FDA labeling and a literature review

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Drug-induced liver injury severity and toxicity (DILIst): binary classification of 1279 drugs by human hepatotoxicity

Thakkar, S., Li, T., Liu, Z., Wu, L., Roberts, R., & Tong, W. (2020) · Drug Discovery Today · doi:10.1016/j.drudis.2019.09.022 · Cited by 140

Drug-Induced Liver Injury (DILI) Classification and Its Application on Human DILI Risk Prediction

Thakkar, S., Chen, M., Hong, H., Liu, Z., Fang, H., & Tong, W. (2018) · Methods in pharmacology and toxicology · doi:10.1007/978-1-4939-7677-5_3 · Cited by 8