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

Concordance of the toxicity of pharmaceuticals in humans and in animals

Olson, H., Betton, G., Robinson, D., Thomas, K., Monro, A., Kolaja, G., Lilly, P., Sanders, J., Sipes, G., Bracken, W., et al. (2000) · Regulatory Toxicology and Pharmacology · doi:10.1006/rtph.2000.1399 · Cited by 2,050

Idiosyncratic drug hepatotoxicity

Kaplowitz, N. (2005) · Nature Reviews Drug Discovery · doi:10.1038/nrd1750 · Cited by 1,054

Timing of new black box warnings and withdrawals for prescription medications

Lasser, K. E. (2002) · JAMA · doi:10.1001/jama.287.17.2215 · Cited by 1,005

Features and outcomes of 899 patients with drug-induced liver injury: the DILIN Prospective Study

Chalasani, N., Bonkovsky, H. L., Fontana, R., Lee, W., Stolz, A., Talwalkar, J., Reddy, K. R., Watkins, P. B., Navarro, V., Barnhart, H., et al. (2015) · Gastroenterology · doi:10.1053/j.gastro.2015.03.006 · Cited by 925

Drug-Induced Liver Injury: An Analysis of 461 Incidences Submitted to the Spanish Registry Over a 10-Year Period

ANDRADE, R., LUCENA, M., FERNANDEZ, M., PELAEZ, G., PACHKORIA, K., GARCIARUIZ, E., GARCIAMUNOZ, B., GONZALEZGRANDE, R., PIZARRO, A., & DURAN, J. (2005) · Gastroenterology · doi:10.1016/j.gastro.2005.05.006 · Cited by 856

Cellular Imaging Predictions of Clinical Drug-Induced Liver Injury

Xu, J. J., Henstock, P. V., Dunn, M. C., Smith, A. R., Chabot, J., & de Graaf, D. (2008) · Toxicological Sciences · doi:10.1093/toxsci/kfn109 · Cited by 505

Mitochondrial toxicity of nrti antiviral drugs: an integrated cellular perspective

Lewis, W., Day, B. J., & Copeland, W. C. (2003) · Nature Reviews Drug Discovery · doi:10.1038/nrd1201 · Cited by 496

In vitro models for liver toxicity testing

Soldatow, V. Y., LeCluyse, E. L., Griffith, L. G., & Rusyn, I. (2012) · Toxicology Research · doi:10.1039/c2tx20051a · Cited by 482

FDA-approved drug labeling for the study of drug-induced liver injury

Chen, M., Vijay, V., Shi, Q., Liu, Z., Fang, H., & Tong, W. (2011) · Drug Discovery Today · doi:10.1016/j.drudis.2011.05.007 · Cited by 406

Relationship between daily dose of oral medications and idiosyncratic drug-induced liver injury: Search for signals

Lammert, C. S., Einarsson, S., Saha, C., Niklasson, A., Bjornsson, E. S., & Chalasani, N. (2008) · Hepatology · doi:10.1002/hep.22272 · Cited by 389

Integration of temporal single cell cellular stress response activity with logic-ODE modeling reveals activation of ATF4-CHOP axis as a critical predictor of drug-induced liver injury

Wijaya, L. S., Trairatphisan, P., Gábor, A., Niemeijer, M., Keet, J., Morera, A. A., Snijders, K. E., Wink, S., Yang, H., Schildknecht, S., et al. (2021) · Biochemical Pharmacology · doi:10.1016/j.bcp.2021.114591 · Cited by 27

Development of an AI model for DILI-level prediction using liver organoid brightfield images

Tan, S., Ding, Y., Wang, W., Rao, J., Cheng, F., Zhang, Q., Xu, T., Hu, T., Hu, Q., Ye, Z., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-08205-6 · Cited by 26

Deep Learning Algorithm Based on Molecular Fingerprint for Prediction of Drug‐Induced Liver Injury

Yang, Q., Zhang, S., & Li, Y. (2024) · Toxicology · doi:10.1016/j.tox.2024.153736 · Cited by 27

Comprehensive hepatotoxicity prediction: ensemble model integrating machine learning and deep learning

Khan, M. Z. I., Ren, J.-N., Cao, C., Ye, H.-Y.-X., Wang, H., Guo, Y.-M., Yang, J., & Chen, J. (2024) · Frontiers in Pharmacology · doi:10.3389/fphar.2024.1441587 · Cited by 31

Predicting drug-induced hepatotoxicity based on biological feature maps and diverse classification strategies

Su, R., Wu, H., Liu, X., & Wei, L. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bbz165 · Cited by 27

Association of CYP1A1 and CYP1B1 inhibition in in vitro assays with drug-induced liver injury

Shimizu, Y., Sasaki, T., Yonekawa, E., Yamazaki, H., Ogura, R., WATANABE, M., Hosaka, T., Shizu, R., Takeshita, J., & Yoshinari, K. (2021) · The Journal of Toxicological Sciences · doi:10.2131/jts.46.167 · Cited by 26

ChemBioSim: Enhancing Conformal Prediction of In Vivo Toxicity by Use of Predicted Bioactivities

de Lomana, M. G., Morger, A., Norinder, U., Buesen, R., Landsiedel, R., Volkamer, A., Kirchmair, J., & Mathea, M. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00451 · Cited by 25

Predictive Model for Drug-Induced Liver Injury Using Deep Neural Networks Based on Substructure Space

Kang, M.-G., & Kang, N. S. (2021) · Molecules · doi:10.3390/molecules26247548 · Cited by 29

Drug-induced QT Prolongation Atlas (DIQTA) for enhancing cardiotoxicity management

Li, S., Xu, Z., Guo, M., Li, M., & Wen, Z. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.10.009 · Cited by 25

Recent Advances in Toxicity Prediction: Applications of Deep Graph Learning

Miao, Y., Ma, H., & Huang, J. (2023) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.2c00384 · Cited by 29

Control compounds for preclinical drug-induced liver injury assessment: Consensus-driven systematic review by the ProEuroDILI network

Segovia‐Zafra, A., Paz, M. V., Serras, A. S., Matilla‐Cabello, G., Bodoque-García, A., Zeo-Sánchez, D. E. D., Niu, H., Álvarez‐Álvarez, I., Sanz‐Villanueva, L., Godec, S., et al. (2024) · Journal of Hepatology · doi:10.1016/j.jhep.2024.04.026 · Cited by 24

Physiological liver microtissue 384-well microplate system for preclinical hepatotoxicity assessment of therapeutic small molecule drugs

Fäs, L., Chen, M., Tong, W., Wenz, F., Hewitt, N. J., Tu, M., Sanchez, K., Zapiórkowska-Blumer, N., Varga, H., Kaczmarska, K., et al. (2024) · Toxicological Sciences · doi:10.1093/toxsci/kfae123 · Cited by 25

Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis

Lejal, V., Cerisier, N., Rouquié, D., & Taboureau, O. (2023) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.2c00381 · Cited by 24

An in vitro coculture system of human peripheral blood mononuclear cells with hepatocellular carcinoma-derived cells for predicting drug-induced liver injury

Oda, S., Uchida, Y., Aleo, M. D., Koza‐Taylor, P., Matsui, Y., Hizue, M., Marroquin, L. D., Whritenour, J., Uchida, E., & Yokoi, T. (2020) · Archives of Toxicology · doi:10.1007/s00204-020-02882-4 · Cited by 22

Investigating ADR mechanisms with Explainable AI: a feasibility study with knowledge graph mining

Bresso, E., Monnin, P., Cédric Bousquet, B., Calvier, F.-É., Ndiaye, N. C., Petitpain, N., Smaïl‐Tabbone, M., & Coulet, A. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01518-6 · Cited by 23

Predicting Drug-Induced Liver Injury Using Machine Learning on a Diverse Set of Predictors

Adeluwa, T., McGregor, B. A., Guo, K., & Hur, J. (2021) · Frontiers in Pharmacology · doi:10.3389/fphar.2021.648805 · Cited by 24

Toxicity prediction using target, interactome, and pathway profiles as descriptors

Füzi, B., Mathai, N., Kirchmair, J., & Ecker, G. F. (2023) · Toxicology Letters · doi:10.1016/j.toxlet.2023.04.005 · Cited by 22

In vitro cell-based models of drug-induced hepatotoxicity screening: progress and limitation

Mirahmad, M., Sabourian, R., Mahdavi, M. R., Larijani, B. A., & Safavi, M. (2022) · Drug Metabolism Reviews · doi:10.1080/03602532.2022.2064487 · Cited by 21

The landscape of hepatobiliary adverse reactions across 53 herbal and dietary supplements reveals immune-mediated injury as a common cause of hepatitis

Zhu, J., Chen, M., Borlak, J., & Tong, W. (2020) · Archives of Toxicology · doi:10.1007/s00204-019-02621-4 · Cited by 18

Image-based molecular representation learning for drug development: a survey

Li, Y., Liu, B., Deng, J., Guo, Y., & Du, H. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae294 · Cited by 23

Machine Learning Models for Predicting Liver Toxicity

Liu, J., Guo, W., Sakkiah, S., Ji, Z., Yavaş, G., Zou, W., Chen, M., Tong, W., Patterson, T. A., & Hong, H. (2022) · Methods in molecular biology · doi:10.1007/978-1-0716-1960-5_15 · Cited by 20

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning

Yan, B., Ye, X., Wang, J., Han, J., Wu, L., He, S., Liu, K., & Bo, X. (2022) · Molecules · doi:10.3390/molecules27103112 · Cited by 22

The safety of oral antifungals for the treatment of onychomycosis

Gupta, A. K., Haas-Neill, S., & Talukder, M. (2023) · Expert Opinion on Drug Safety · doi:10.1080/14740338.2023.2280137 · Cited by 20

Development of Prediction Models for Drug-Induced Cholestasis, Cirrhosis, Hepatitis, and Steatosis Based on Drug and Drug Metabolite Structures

Shin, H. K., Kang, M.-G., Park, D., Park, T., & Yoon, S. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.00067 · Cited by 21

A large-scale human toxicogenomics resource for drug-induced liver injury prediction

Bergen, V., Kodella, K. R., Srikrishnan, S., Barrandon, O., Anderson, S., Rogers-Grazado, M., Fowler, C. C., Beyene, H., Robichaud, N., Fulton, T., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-65690-3 · Cited by 30

In silico modeling-based new alternative methods to predict drug and herb-induced liver injury: A review

Shin, H. K., Huang, R., & Chen, M. (2023) · Food and Chemical Toxicology · doi:10.1016/j.fct.2023.113948 · Cited by 19

Unraveling the mechanisms underlying drug-induced cholestatic liver injury: identifying key genes using machine learning techniques on human in vitro data sets

Jiang, J., van Ertvelde, J., Ertaylan, G., Peeters, R., Jennen, D. G. J., de Kok, T. M. C. M., & Vinken, M. (2023) · Archives of Toxicology · doi:10.1007/s00204-023-03583-4 · Cited by 23

Machine Learning to Predict Drug-Induced Liver Injury and Its Validation on Failed Drug Candidates in Development

Mostafa, F. B., Howle, V. E., & Chen, M. (2024) · Toxics · doi:10.3390/toxics12060385 · Cited by 19

Hepatotoxicity Modeling Using Counter-Propagation Artificial Neural Networks: Handling an Imbalanced Classification Problem

Bajželj, B., & Drgan, V. (2020) · Molecules · doi:10.3390/molecules25030481 · Cited by 19

Mechanism-based integrated assay systems for the prediction of drug-induced liver injury

Kawaguchi, M., Nukaga, T., Sekine, S., Takemura, A., Susukida, T., Oeda, S., Kodama, A., Hirota, M., Kouzuki, H., & Ito, K. (2020) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2020.114958 · Cited by 19

The prediction approach of drug-induced liver injury: response to the issues of reproducible science of artificial intelligence in real-world applications

Zhao, C. X., Jiang, Y., Zhang, X., Zheng, R., Qiu, R., Sun, Y., Zhao, C. X., & Shang, H. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac196 · Cited by 18

Text summarization with ChatGPT for drug labeling documents

Ying, L., Liu, Z., Fang, H., Kusko, R. L., Wu, L., Harris, S., & Tong, W. (2024) · Drug Discovery Today · doi:10.1016/j.drudis.2024.104018 · Cited by 19

Propensity Score-Based Approaches in High Dimension for Pharmacovigilance Signal Detection: an Empirical Comparison on the French Spontaneous Reporting Database

Courtois, É., Pariente, A., Salvo, F., Volatier, É., Tubert‐Bitter, P., & Ahmed, I. (2018) · Frontiers in Pharmacology · doi:10.3389/fphar.2018.01010 · Cited by 20

Drug-Induced Liver Injury in Critically Ill Children Taking Antiepileptic Drugs: A Retrospective Study

Sridharan, K., Daylami, A. A., Ajjawi, R., & Ajooz, H. A. A. (2020) · Current Therapeutic Research · doi:10.1016/j.curtheres.2020.100580 · Cited by 18

Leveraging FDA Labeling Documents and Large Language Model to Enhance Annotation, Profiling, and Classification of Drug Adverse Events with AskFDALabel

Wu, L., Fang, H., Qu, Y., Xu, J., & Tong, W. (2025) · Drug Safety · doi:10.1007/s40264-025-01520-1 · Cited by 20

New approach methodologies (NAMs) for drug-induced liver injury (DILI): Where are we now?

Shrimali, S., Chen, M., Li, D., & Tong, W. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104452 · Cited by 18

The OASIS Consortium: integrating multi-omics technologies to transform chemical safety assessment

Rouquié, D., Bender, A., Cheah, J. H., Crute, C., Dalmas, D. A., Ewald, J., Fullerton, A. M., Harrill, J., Kadri, S., Kleinstreuer, N., et al. (2025) · Toxicological Sciences · doi:10.1093/toxsci/kfaf128 · Cited by 17

New adaptive lasso approaches for variable selection in automated pharmacovigilance signal detection

Courtois, É., Tubert‐Bitter, P., & Ahmed, I. (2021) · BMC Medical Research Methodology · doi:10.1186/s12874-021-01450-3 · Cited by 17

Consideration of predicted small-molecule metabolites in computational toxicology

de Lomana, M. G., Svensson, F., Volkamer, A., Mathea, M., & Kirchmair, J. (2022) · Digital Discovery · doi:10.1039/d1dd00018g · Cited by 17

The hepatotoxic potential of protein kinase inhibitors predicted with Random Forest and Artificial Neural Networks

Schöning, V., Krähenbühl, S., & Drewe, J. (2018) · Toxicology Letters · doi:10.1016/j.toxlet.2018.10.009 · Cited by 17

Automated Integration of Structural, Biological and Metabolic Similarities to Sustain Read-Across

Gadaleta, D. (2020) · ALTEX · doi:10.14573/altex.2002281 · Cited by 17

Machine‐Learning Prediction of Oral Drug‐Induced Liver Injury (DILI) via Multiple Features and Endpoints

Liu, X., Zheng, D., Zhong, Y., Xia, Z., Luo, H., & Weng, Z. (2020) · BioMed Research International · doi:10.1155/2020/4795140 · Cited by 17

An ensemble learning approach for modeling the systems biology of drug-induced injury

Aguirre‐Plans, J., Piñero, J., de Souza, T. M., Callegaro, G., Kunnen, S. J., Sanz, F., Fernández‐Fuentes, N., Furlong, L. I., Güney, E., & Oliva, B. (2021) · Biology Direct · doi:10.1186/s13062-020-00288-x · Cited by 17

Toxicological data bank bridges the gap between environmental risk assessment and green organic chemical design in One Health world

Shi, X.-X., Wang, Z.-Z., Sun, X., Wang, Y., Liu, H., Wang, F., Hao, G., & Yang, G. (2023) · Green Chemistry · doi:10.1039/d2gc03973g · Cited by 16

Hybrid non-animal modeling: A mechanistic approach to predict chemical hepatotoxicity

Chung, E., Wen, X., Jia, X., Ciallella, H. L., Aleksunes, L. M., & Zhu, H. (2024) · Journal of Hazardous Materials · doi:10.1016/j.jhazmat.2024.134297 · Cited by 15

Artificial Intelligence: An Emerging Tool for Studying Drug‐Induced Liver Injury

Niu, H., Álvarez‐Álvarez, I., & Chen, M. (2025) · Liver International · doi:10.1111/liv.70038 · Cited by 18

Impact of halogenation on scaffold toxicity assessed using HD-GEM machine learning model

Boya, B. R., Lee, J., Choi, J.-M., & Lee, J. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf347 · Cited by 15

Improving drug-induced liver injury prediction using graph neural networks with augmented graph features from molecular optimisation

Lee, T., & Posma, J. M. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-01068-3 · Cited by 19

Cell Painting for cytotoxicity and mode-of-action analysis in primary human hepatocytes

Ewald, J., Titterton, K., Bäuerle, A., Beatson, A., Boiko, D. A., Cabrera, Á. A., Cheah, J. H., Cimini, B. A., Gorissen, B. L., Harrill, J., et al. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101566 · Cited by 19

StackDILI: Enhancing Drug-Induced Liver Injury Prediction through Stacking Strategy with Effective Molecular Representations

Guan, J., Dong, D., Xie, P., Zhao, Z., Guo, Y., Lee, T.-Y., Yao, L., & Chiang, Y. (2025) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.4c02079 · Cited by 17

Studying and mitigating the effects of data drifts on ML model performance at the example of chemical toxicity data

Morger, A., de Lomana, M. G., Norinder, U., Svensson, F., Kirchmair, J., Mathea, M., & Volkamer, A. (2022) · Scientific Reports · doi:10.1038/s41598-022-09309-3 · Cited by 18

ToxSTAR: drug-induced liver injury prediction tool for the web environment

Shin, H. K., Chun, H., Lee, S., Park, S., Park, D., Kang, M.-G., Hwang, S., Oh, J., Han, H., Kim, W., et al. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac490 · Cited by 14

The involvement of the Stat1/Nrf2 pathway in exacerbating Crizotinib-induced liver injury: implications for ferroptosis

Guo, L., Ma, J., Xiao, M., Liu, J., Hu, Z., Xia, S., Li, N., Yang, Y., Gong, H., Xi, Y., et al. (2024) · Cell Death and Disease · doi:10.1038/s41419-024-06993-z · Cited by 15

Identifying multiscale translational safety biomarkers using a network-based systems approach

Callegaro, G., Schimming, J. P., Piñero, J., Kunnen, S. J., Wijaya, L. S., Trairatphisan, P., van den Berk, L., Beetsma, K., Furlong, L. I., Sutherland, J. J., et al. (2023) · iScience · doi:10.1016/j.isci.2023.106094 · Cited by 14

Novel in Vitro Method Reveals Drugs That Inhibit Organic Solute Transporter Alpha/Beta (OSTα/β)

Malinen, M. M., Kauttonen, A., Beaudoin, J. J., Sjöstedt, N., Honkakoski, P., & Brouwer, K. L. R. (2018) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.8b00966 · Cited by 14

Development of a Battery of In Silico Prediction Tools for Drug-Induced Liver Injury from the Vantage Point of Translational Safety Assessment

Rathman, J. F., Yang, C., Ribeiro, J. V., Mostrąg, A., Thakkar, S., Tong, W., Hobocienski, B., Sacher, O., Magdziarz, T., & Bienfait, B. (2020) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.0c00423 · Cited by 14

A network-based transcriptomic landscape of HepG2 cells uncovering causal gene-cytotoxicity interactions underlying drug-induced liver injury

Wijaya, L. S., Gábor, A., Pot, I., van de Have, L., Sáez-Rodríguez, J., Stevens, J. L., Dévédec, S. E. L., Callegaro, G., & van de Water, B. (2023) · Toxicological Sciences · doi:10.1093/toxsci/kfad121 · Cited by 17

Live-cell painting: Image-based profiling in live cells using acridine orange

Garcia-Fóssa, F., Moraes-Lacerda, T., Rodrigues-da-Silva, M., Diaz-Rohrer, B. B., Singh, S., Carpenter, A. E., Cimini, B. A., & de Jesus, M. B. (2025) · Molecular Biology of the Cell · doi:10.1091/mbc.e24-07-0308 · Cited by 18

Linking chemicals, genes and morphological perturbations to diseases

Cerisier, N., Dafniet, B., Badel, A., & Taboureau, O. (2023) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2023.116407 · Cited by 13

A Novel Lipidomics-Based Approach to Evaluating the Risk of Clinical Hepatotoxicity Potential of Drugs in 3D Human Microtissues

Goracci, L., Valeri, A., Sciabola, S., Aleo, M. D., Moritz, W., Lichtenberg, J., & Cruciani, G. (2019) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.9b00364 · Cited by 13

Human Liver Organoid Models for Assessment of Drug Toxicity at the Preclinical Stage

Karabicici, M., Akbari, S., Ertem, Ö., Gümüştekin, M., & Erdal, E. (2023) · Endocrine Metabolic & Immune Disorders - Drug Targets · doi:10.2174/1871530323666230411100121 · Cited by 12

Editor’s Highlight: Identification of Any Structure-Specific Hepatotoxic Potential of Different Pyrrolizidine Alkaloids Using Random Forests and Artificial Neural Networks

Schöning, V., Hammann, F., Peinl, M., & Drewe, J. (2017) · Toxicological Sciences · doi:10.1093/toxsci/kfx187 · Cited by 12

A systematic comparison of hepatobiliary adverse drug reactions in FDA and EMA drug labeling reveals discrepancies

Wu, Y., Xiao, W., Tong, W., Borlak, J. T., & Chen, M. (2021) · Drug Discovery Today · doi:10.1016/j.drudis.2021.09.009 · Cited by 13

Beyond the hype and toward application: liver complex in vitro models in preclinical drug safety

Jadalannagari, S., & Ewart, L. C. (2024) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1080/17425255.2024.2328794 · Cited by 11

HepatoToxicity Portal (HTP): an integrated database of drug-induced hepatotoxicity knowledgebase and graph neural network-based prediction model

Han, J., Zhung, W., Jang, I., Lee, J., Kang, M. J., Lee, T. D., Kwack, S. J., Kim, K., Hwang, D., Lee, B.-W., et al. (2025) · Journal of Cheminformatics · doi:10.1186/s13321-025-00992-8 · Cited by 16

Chemistry-Based Modeling on Phenotype-Based Drug-Induced Liver Injury Annotation: From Public to Proprietary Data

Moein, M., Heinonen, M., Mesens, N., Chamanza, R., Amuzie, C. J., Will, Y., Ceulemans, H., Samuel Kaski, A., & Herman, D. (2023) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.2c00378 · Cited by 12

Computational Toxicology

Thakkar, S., Perkins, R., Hong, H., & Tong, W. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-801238-3.64317-9 · Cited by 11

Extrapolation of drug induced liver injury responses from cancer cell lines using machine learning approaches

Aguayo‐Orozco, A., Brunak, S., & Taboureau, O. (2020) · Computational Toxicology · doi:10.1016/j.comtox.2020.100147 · Cited by 11

Transform Drug Discovery and Development With Generative Artificial Intelligence

Lavecchia, A. (2025) · Wiley eBooks · doi:10.1002/9781394280735.ch25 · Cited by 14

Computational Strategies for Assessing Adverse Outcome Pathways: Hepatic Steatosis as a Case Study

Ortega-Vallbona, R., Palomino‐Schätzlein, M., Tolosa, L., Benfenati, E., Ecker, G. F., Gozalbes, R., & Serrano‐Candelas, E. (2024) · International Journal of Molecular Sciences · doi:10.3390/ijms252011154 · Cited by 10

Risk Assessment of Psychotropic Drugs on Mitochondrial Function Using In Vitro Assays

Rosell-Hidalgo, A., Eakins, J. A., Walker, P., Moore, A. L., & Ghafourian, T. (2023) · Biomedicines · doi:10.3390/biomedicines11123272 · Cited by 10

Synthesis and preliminary hepatotoxicity evaluation of new caffeine-8-(2-thio)-propanoic hydrazid-hydrazone derivatives

Mitkov, J., Kondeva-Burdina, M., & Zlatkov, A. (2019) · Pharmacia · doi:10.3897/pharmacia.66.e37263 · Cited by 10

In vitro assessment of farnesoid X receptor antagonism to predict drug-induced liver injury risk

Norona, L. M., Fullerton, A. M., Lawson, C., Leung, L., Brumm, J., Kiyota, T., Maher, J. M., Khojasteh, C., & Proctor, W. R. (2020) · Archives of Toxicology · doi:10.1007/s00204-020-02804-4 · Cited by 10

Novel clinical phenotypes, drug categorization, and outcome prediction in drug-induced cholestasis: Analysis of a database of 432 patients developed by literature review and machine learning support

Moreno‐Torres, M., López-Pascual, E., Rapisarda, A. S., Quintás‐Cardama, A., Drees, A., Steffensen, I., Luechtefeld, T. H., Serrano‐Candelas, E., de Lomana, M. G., Gadaleta, D., et al. (2024) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2024.116530 · Cited by 9

Deep Learning Prediction of Drug-Induced Liver Toxicity by Manifold Embedding of Quantum Information of Drug Molecules

Li, T., Li, J., Jiang, H., & Skiles, D. B. (2024) · Pharmaceutical Research · doi:10.1007/s11095-024-03800-4 · Cited by 10

Efficient analysis of drug interactions in liver injury: a retrospective study leveraging natural language processing and machine learning

Ma, J., Chen, H., Sun, J., Huang, J., He, G., & Yang, G. (2024) · BMC Medical Research Methodology · doi:10.1186/s12874-024-02443-8 · Cited by 10

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

Olubamiwa, A. O., Qu, Y., Connor, S., Tong, W., Li, D., & Chen, M. (2025) · Drug Discovery Today · doi:10.1016/j.drudis.2025.104485 · Cited by 9

UniTox: Leveraging LLMs to Curate a Unified Dataset of Drug-Induced Toxicity from FDA Labels

Silberg, J., Swanson, K., Simon, E. P., Zhang, A., Ghazizadeh, Z., Ogden, S., Hamadeh, H. K., & Zou, J. (2024) · neural information processing systems · doi:10.52202/079017-0385 · Cited by 9

A scalable human liver-on-a-chip platform for predictive safety assessment

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