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

Benzofuran‐Based Uricosuric Agents: History, Roles, and Challenges

Al‐Zoubi, R. M., Hanbali, B., Elaarag, M., Fares, Z. E., Al-Hurani, E. A., Al-Zoubi, S. R., Shkoor, M., & Agouni, A. (2026) · ChemistrySelect · doi:10.1002/slct.73726 · Cited by 1

Integrating multi-type features and knowledge graph for graded prediction of drug-induced liver injury in humans

Liu, Y., Hu, K., Geng, J., Dai, Q., Wei, L., & Su, R. (2026) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013640 · Cited by 1

Case Report: Dual toxicity of voriconazole in a CYP2C19 poor metabolizer: a case analysis of life-threatening neutropenia and hepatocellular injury

Liu, K., & Gong, W. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1875506 · Cited by 1

Elevation of transaminases associated with teriparatide treatment: a case report

Pappalardo, F., Fantini, L., & Caruso, V. (2020) · European Journal of Hospital Pharmacy · doi:10.1136/ejhpharm-2020-002293 · Cited by 1

Protective Effects of Smilax glabra Roxb on Carbon Tetrachloride Induced Rat Acute Liver Injury by Inhibiting the Activation of the NLRP3 Inflammasome

Han, C., KANG, Y. X., FAN, G. T., Liu, Y., Zhang, T., & Liu, C. Y. (2021) · Indian Journal of Pharmaceutical Sciences · doi:10.36468/pharmaceutical-sciences.773 · Cited by 1

Using real-world evidence data and digital monitoring to analyze the hepatotoxic profiles of biologics across more than two million patients

Banerjee, P., Preißner, S., & Preißner, R. (2023) · Scientific Reports · doi:10.1038/s41598-023-37979-0 · Cited by 1

Predicting drug-induced liver injury with artificial intelligence—a minireview

Li, T., Kusko, R. L., Thakkar, S., Liu, Z., & Tong, W. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-99136-0.00012-x · Cited by 3

A gene expression profile-based approach to screen the occurrence and predisposed host characteristics of drug-induced liver injury: a case study of Psoralea corylifolia Linn

Zhang, M.-L., Li, W., Wang, X., Zhang, H., Wu, Y., Yang, L.-Q., Chen, X., Zhang, S., Chen, Y., Feng, K.-R., et al. (2023) · Frontiers in Chemistry · doi:10.3389/fchem.2023.1259569 · Cited by 1

Drug Induced Liver Injury Prediction with Injective Molecular Transformer

Choi, G., Cho, H. J., Kim, S. S., Han, J. E., Cheong, J. Y., & Hong, C. (2023) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi58575.2023.10313508 · Cited by 1

Prediction of DILI in Humans: Investigating Opportunities in DILIst Data, Chemical Features, Machine Learning and Rule-Based Methods

Narayanan, A. N., Das, S. S., & Mirnalinee, T. T. (2023) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm58861.2023.10385311 · Cited by 1

In-silico Docking and ADMET: Recent Innovations of Carvone

Srivastava, S., Sinha, R., Lavhale, P. M., & Kurmi, R. K. (2023) · INTERNATIONAL JOURNAL OF PHARMACEUTICAL EDUCATION AND RESEARCH (IJPER) · doi:10.37021/ijper.v5i1.02 · Cited by 1

Biomarkers for the prediction and monitoring of the antipsychotic/antidepressant-induced hepatotoxicity: study protocol

Kilianova, Z., Stollárová, N., Pivackova, L. B., Křenek, P., Göböová, M., Rihova, Z. J., Aziz, E. K., Kuželički, N. K., Dóka, G., & Klimas, J. (2024) · Pharmacogenomics · doi:10.1080/14622416.2025.2456449 · Cited by 2

Predicting the Risks of Drug-Induced Liver Injury in Humans Utilizing Computational Modeling

Chen, M., Zhu, J., Ashby, K., Wu, L., Liu, Z., Gong, P., Zhang, C., Borlak, J. T., Hong, H., & Tong, W. (2019) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-3-030-16443-0_13 · Cited by 1

Human ES and iPS cells display less drug resistance than differentiated cells, and naïve-state induction further decreases drug resistance

Panina, Y., Yamane, J., Kobayashi, K., Sone, H., & Fujibuchi, W. (2021) · The Journal of Toxicological Sciences · doi:10.2131/jts.46.131 · Cited by 1

Computational Modeling for the Prediction of Hepatotoxicity Caused by Drugs and Chemicals

Chen, M., Liu, J., Liao, T.-J., Ashby, K., Wu, Y., Wu, L., Tong, W., & Hong, H. (2023) · Computational methods in engineering & the sciences · doi:10.1007/978-3-031-20730-3_23 · Cited by 1

Novel insights into toxic and chronic liver disease

Zoubek, M. E. (2021) · Research Publications (Maastricht University) · doi:10.26481/dis.20210929mz · Cited by 1

Comment on: “Methotrexate Cutaneous Ulceration: A Systematic Review of Cases”

Parvathaneni, A., Olesen, A., & Diven, D. G. (2023) · American Journal of Clinical Dermatology · doi:10.1007/s40257-023-00773-9 · Cited by 1

Open Access Databases and Datasets for Computer‐Aided Drug Design. A Short List Used in the Molecular Modelling Group of the SIB

Daina, A., Ojeda, M. J., Bragina, M. E., Cuozzo, A., Röhrig, U. F., Perez, M. A. S., & Zoete, V. (2023) · Methods and principles in medicinal chemistry · doi:10.1002/9783527830497.ch1 · Cited by 1

Using SAFEPATH to Uncover a Novel Mechanism of Hepatotoxicity for GefitinibPreprint

Hosseini-Gerami, L., Masarone, S., & Lane, J. S. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.07.31.605988 · Cited by 1

Direct contact between iPSC-derived macrophages and hepatocytes drives reciprocal acquisition of Kupffer cell identity and hepatocyte maturationPreprint

Lee, C., Tasnim, F., Huang, X., Sethi, R., Song, Y., Kozaki, T., Schepper, S. D., Ang, N., Low, I., Hwang, Y. Y., et al. (2025) · eLife · doi:10.7554/elife.108938.1 · Cited by 1

Direct contact between iPSC-derived macrophages and hepatocytes drives reciprocal acquisition of Kupffer cell identity and hepatocyte maturationPreprint

Lee, C., Tasnim, F., Huang, X., Sethi, R., Song, Y., Kozaki, T., Schepper, S. D., Ang, N., Low, I., Hwang, Y. Y., et al. (2025) · eLife · doi:10.7554/elife.108938 · Cited by 1

Transporters in Hepatotoxicity

Kotsampasakou, E., Jain, S., Digles, D., & Ecker, G. F. (2018) · Computational Toxicology · doi:10.1002/9781119282594.ch6

A Pair Ranking (PRank) Method for Assessing Assay Transferability Among the Toxicogenomics Testing Systems

Liu, Z., Delavan, B., Zhu, L., Robert, R., & Tong, W. (2019) · Challenges and advances in computational chemistry and physics · doi:10.1007/978-3-030-16443-0_9

In silico prediction of drug-induced liver injury: Quo vadis?

Weng, C., & Leong, M. K. (2019) · Advances in molecular toxicology · doi:10.1016/b978-0-444-64293-6.00002-6

Progress of derisking strategies for drug-induced liver injury (DILI) in the last two decades

Mitra, K. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-820018-6.00017-x

Recent advances in evaluation studies for drug-induced liver injury

Yokoi, T. (2020) · Folia Pharmacologica Japonica · doi:10.1254/fpj.20027

Investigating ADR mechanisms with knowledge graph mining and explainable AIPreprint

Bresso, E., Monnin, P., Cédric Bousquet, B., Calvier, F.-É., Ndiaye, N. C., Petitpain, N., Smaïl‐Tabbone, M., & Coulet, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.09077

Transporter, Drug Metabolism, and Drug‐Induced Liver Injury in Marketed Drugs

Chen, M., Ashby, K., & Wu, Y. (2021) · doi:10.1002/9781119171003.ch2

Automated Integration of Structural, Biological and Metabolic Similarities to Sustain Read-Across_suppl1

Gadaleta, D. (2020) · ALTEX · doi:10.14573/altex.2002281s1

Automated Integration of Structural, Biological and Metabolic Similarities to Sustain Read-Across_suppl2

Gadaleta, D. (2020) · ALTEX · doi:10.14573/altex.2002281s2

Assessing risks and mechanisms of idiosyncratic drug toxicity by fingerprints of cell signaling responsesPreprint

Medvedev, A. V., Makarov, S. S., Medvedeva, L. A., Martsen, E., Gorman, K. L., Lin, B., & Makarov, S. S. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.01.518765

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

QSAR modeling for predicting drug-induced liver injury

Liao, T.-J., Zhao, J., & Chen, M. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15339-6.00009-6

P22-22: Towards animal-free risk prioritization: a case study for cholestasis

de Bruijn, V., Bouwmeester, H., & Rietjens, I. M. C. M. (2023) · Toxicology Letters · doi:10.1016/s0378-4274(23)00855-x

Drug-induced liver injury associated with elexacaftor/tezacaftor/ivacaftor from the FDA Adverse Event Reporting System (FAERS)Preprint

Shi, A., Nguyen, H., Kuo, C. B., & Beringer, P. M. (2023) · medRxiv · doi:10.1101/2023.09.16.23295574

New Alternative Methods in Drug Safety Assessment

Yang, X., Shi, Q., Chen, M., & Pang, L. (2023) · Springer eBooks · doi:10.1007/978-981-99-1284-1_14

Ensemble learning methods for drug-induced liver injury identification

Chen, P., Wang, B., Zhang, J., Wang, Y., & Xiao, Q. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-824386-2.00011-0

Effectiveness And Safety Measures Of Benazepril Hydrochloride On Diabetic Nephropathy

Prakash, S., Sarkar, A., Kumar, H., Tamboli, N., Gupta, H., Nidhi, N., Swami, A., & Vashisht, M. (2023) · Journal Of Advanced Zoology · doi:10.53555/jaz.v44is6.3098

Multicellular, fluid flow-inclusive hepatic in vitro models using NANOSTACKS TM : a human-relevant model for drug response predictionPreprint

Talari, A. C. S., Sbordoni, R., Hoti, V., Patel, I. I., Martin, F. L., Raza, A., & Llabjani, V. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.12.607396

Modelling of Drug-Induced Liver Injury with Multiple Machine Learning Algorithms

Lesiński, W., Golińska, A. K., & Rudnicki, W. R. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-70259-4_33

Pharmacoproteomic profiling identifies secreted markers for aberrant drug actionPreprint

Knecht, S., Kalxdorf, M., Korbeń, J., Mathieson, T., Sévin, D. C., Küster, B., Kasprowicz, R., Sakatis, M. Z., Eberl, H. C., & Bantscheff, M. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.16.618637 · Cited by 1

Evaluating chemical effects on human neural cells through calcium imaging and deep learning

Ku, R. Y., Bansal, A., Dutta, D. J., Yamashita, S., Peloquin, J., Vu, D. N., Shen, Y., Uchida, T., Torii, M., & Hashimoto‐Torii, K. (2024) · iScience · doi:10.1016/j.isci.2024.111298

A Simple Information Criterion for Variable Selection in High‐Dimensional Regression

Pluntz, M., Dalmasso, C., Tubert‐Bitter, P., & Ahmed, I. (2024) · Statistics in Medicine · doi:10.1002/sim.10275

Drug-Induced Thrombocytopenia Severity and Toxicity (DITPst): binary classification of drugs by human thrombocytopenia toxicity

Nie, X., Hu, F., Cheng, X., Ma, J., Peng, X., Sun, F., Ni, X., & Zhan, S. (2025) · Expert Opinion on Drug Safety · doi:10.1080/14740338.2025.2460439 · Cited by 1

Multicellular, fluid flow-inclusive hepatic in vitro models using NANOSTACKS TM : human-relevant models for drug response predictionPreprint

Talari, A. C. S., Sbordoni, R., Hoti, V., Jalil, T., Patel, I. I., Martin, F. L., Raza, A., & Llabjani, V. (2025) · Research Square · doi:10.21203/rs.3.rs-6228265/v1

Multicellular hepatic in vitro models using NANOSTACKSTM: human-relevant models for drug response prediction

Talari, A. C. S., Sbordoni, R., Hoti, V., Jalil, T., Patel, I. I., Martin, F. L., Raza, A., & Llabjani, V. (2025) · In vitro models · doi:10.1007/s44164-025-00089-4 · Cited by 1

Direct contact between iPSC-derived macrophages and hepatocytes drives reciprocal acquisition of Kupffer cell identity and hepatocyte maturation

Lee, C. Z. W., Tasnim, F., Huang, X., Sethi, R., Song, Y., Kozaki, T., Schepper, S. D., Ang, N., Low, I., Hwang, Y. Y., et al. (2026) · eLife · doi:10.7554/elife.108938.3

Predicting Organ-Specific Toxicity of Selective Androgen Receptor Modulators, using Transfer Learning on Graph Convolutional NetworksPreprint

Kalian, A. D., Silva, A. C., Lee, J., Dorne, J. L. C. M., Potter, C., Benfenati, E., Osborne, O. J., Guo, M., & Högstrand, C. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.27.672581

Integrative analysis of levofloxacin-induced liver injury: Insights from pharmacovigilance, human liver organoids, and extracellular vesicle proteomics

Tan, S., Yang, Y., Cai, Y., Pu, Y., Gu, Z., & Zhang, J. (2025) · Toxicology and Applied Pharmacology · doi:10.1016/j.taap.2025.117576

ToxAssay: a hierarchical model-driven tool for advanced toxicogenomics biomarker discovery

Rana, M. M., Mollah, M. N. H., Albujja, M. H., Hadi, S. S., & Liu, F. (2025) · Bioinformatics · doi:10.1093/bioinformatics/btaf561

DeepGPT-DILI: Integrating Graph Convolutional Networks and Large Language Model Embeddings for Accurate Drug-Induced Liver Injury Prediction

Minh, L. H. N., Huynh, U. K. M., Ong, H. X., Huynh, P. K., Dinh, M.-T., Huynh, H. H., Kha, H. Q., Nguyen, P. K., Huynh, X.-L., Vo, A. T., et al. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-07502-4_12

Antifungals for onychomycosis and risk of liver injury: A retrospective cohort study using TriNetX

Ye, H., & Lipner, S. R. (2025) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2025.11.058

Additional file 7 of Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure

Liu, A., Walter, M., Wright, P. S. R., Bartosik, A., Dolciami, D., Elbasir, A., Yang, H., & Bender, A. (2021) · Figshare · doi:10.6084/m9.figshare.13605398

Additional file 8 of Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure

Liu, A., Walter, M., Wright, P. S. R., Bartosik, A., Dolciami, D., Elbasir, A., Yang, H., & Bender, A. (2021) · Figshare · doi:10.6084/m9.figshare.13605401

Additional file 4 of Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure

Liu, A., Walter, M., Wright, P. S. R., Bartosik, A., Dolciami, D., Elbasir, A., Yang, H., & Bender, A. (2021) · Figshare · doi:10.6084/m9.figshare.13605389

Additional file 1 of Investigating ADR mechanisms with Explainable AI: a feasibility study with knowledge graph mining

Bresso, E., Monnin, P., Cédric Bousquet, B., Calvier, F.-E., Ndiaye, N. C., Petitpain, N., Smaïl‐Tabbone, M., & Coulet, A. (2021) · Figshare · doi:10.6084/m9.figshare.14684847

Additional file 1 of InterDILI: interpretable prediction of drug-induced liver injury through permutation feature importance and attention mechanism

Lee, S., & Yoo, S. (2024) · Figshare · doi:10.6084/m9.figshare.26660293

Additional file 3 of Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure

Liu, A., Walter, M., Wright, P. S. R., Bartosik, A., Dolciami, D., Elbasir, A., Yang, H., & Bender, A. (2021) · Figshare · doi:10.6084/m9.figshare.13605386

Additional file 2 of Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure

Liu, A., Walter, M., Wright, P. S. R., Bartosik, A., Dolciami, D., Elbasir, A., Yang, H., & Bender, A. (2021) · Figshare · doi:10.6084/m9.figshare.13605383

Additional file 6 of Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure

Liu, A., Walter, M., Wright, P. S. R., Bartosik, A., Dolciami, D., Elbasir, A., Yang, H., & Bender, A. (2021) · Figshare · doi:10.6084/m9.figshare.13605395

Additional file 9 of Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure

Liu, A., Walter, M., Wright, P. S. R., Bartosik, A., Dolciami, D., Elbasir, A., Yang, H., & Bender, A. (2021) · Figshare · doi:10.6084/m9.figshare.13605404

Additional file 1 of KNIME workflow for retrieving causal drug and protein interactions, building networks, and performing topological enrichment analysis demonstrated by a DILI case study

Füzi, B., Malik‐Sheriff, R. S., Manners, E. J., Hermjakob, H., & Ecker, G. F. (2022) · Figshare · doi:10.6084/m9.figshare.20057409

Additional file 1 of Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure

Liu, A., Walter, M., Wright, P. S. R., Bartosik, A., Dolciami, D., Elbasir, A., Yang, H., & Bender, A. (2021) · Figshare · doi:10.6084/m9.figshare.13605380

Additional file 9 of 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., Fernandez-Fuentes, N., Furlong, L. I., Güney, E., & Oliva, B. (2021) · Figshare · doi:10.6084/m9.figshare.13565828

Additional file 5 of Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure

Liu, A., Walter, M., Wright, P. S. R., Bartosik, A., Dolciami, D., Elbasir, A., Yang, H., & Bender, A. (2021) · Figshare · doi:10.6084/m9.figshare.13605392

vToxiNet: a biologically constrained deep learning framework for interpretable prediction of drug-induced hepatotoxicityPreprint

Jia, X., Wang, T., Russo, D. P., Aleksunes, L. M., Xiao, S., & Zhu, H. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.26.708259

Artificial Intelligence based toxicity prediction: Advancing drug safety and risk assessment

Karwasra, R., Khanna, K., Sharma, I., Singh, S. N., & Rajagopal, M. S. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-44415-9.00016-8

Score de propension en grande dimension et régression pénalisée pour la détection automatisée de signaux en pharmacovigilance

Courtois, E. (2020) · theses.fr (ABES) · doi:10.70675/5935aae1zac70z491ez9f58z502933931463

Matching and mining in knowledge graphs of the Web of data : Applications in pharmacogenomics

Monnin, P. (2020) · doi:10.70675/1d7fae51zaf42z43b7z8f2bz7c11503a01d6

Costs, Demographics, and Causative Agents in Patients Hospitalized for Drug Induced Liver Injury: Trends in a Large Academic Healthcare System

Kozar, M., Gonzalez, Y., & Marzio, D. H. (2026) · Clinical and Experimental Gastroenterology · doi:10.2147/ceg.s572371

Sélection de variables en grande dimension par le Lasso et tests statistiques - application à la pharmacovigilance

Pluntz, M. (2024) · doi:10.70675/717c683czab06z4602zb976zb34c8743ee03

INFERMed: A PK/PD-Aware Retrieval-Augmented System for Explainable Drug-Drug Interaction Analysis

Mishra, P., Ganzha, M., Plewczyński, D., & Paprzycki, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-23241-0_9

DILI-MAFNet: A Multimodal Attention Fusion Network for Drug-Induced Liver Injury Prediction

Liyaqat, T., Ahmad, T., Shakeel, H., & Saxena, C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-23241-0_15

Bioprinted Perfusable Multi‐Cellular Liver Model Using Matrix Metalloproteinase ‐Degradable Hydrogel

Lu, T., Lyu, C., Shen, E. N., Berman, E. L., Sun, Y., Roy, R., Zhu, W. Z., Tang, C., & Chen, S. (2026) · Advanced Functional Materials · doi:10.1002/adfm.202600040

MACHINE LEARNING APPLICATIONS IN TOXICOLOGY: CURRENT APPROACHES AND FUTURE PERSPECTIVES

Yaşar, S., Çetin, R., & Kızılkaya, M. B. (2026) · Ankara Universitesi Eczacilik Fakultesi Dergisi · doi:10.33483/jfpau.1655343

Complex in vitro model developers’ perspective on advanced hepatic in vitro culture methods for predictive drug safety

Colombo, M. V., Borgström, A., & Filippi, B. G. H. (2026) · Expert Opinion on Drug Metabolism & Toxicology · doi:10.1080/17425255.2026.2683592

Computational Identification of Natural Product-Derived GSK-3β Inhibitors as Candidate Therapeutics for Type 2 Diabetes and MASHPreprint

Bello, L., Nwankwo, D. O., Shodehinde, S. A., Akerele, G. P., Okuntimehin, B., Ogunjobi, E. G., Awelewa, O. V., Ohilebo, A. A., & Olubode, S. O. (2026) · Research Square · doi:10.21203/rs.3.rs-9763432/v1

A hybrid framework integrating structural machine learning and 3D liver-on-chip assay for drug-induced liver injury predictionPreprint

Zhang, F., Zhou, Y., Ding, D., Zhang, F., Xiao, R., & Ai, X. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.15.732231

Drug transporters in drug disposition – highlights from the year 2025

Sandoval, P., Mitra, P., Nakakariya, M., Ramsden, D., Rotter, C. J., Ding, N., & Chothe, P. P. (2026) · Drug Metabolism Reviews · doi:10.1080/03602532.2026.2690529

DILI-context: a dose- and exposure-enriched knowledge base for translational liver safety assessment

Zandie, R., Betancort, R., & Khodaee, F. (2026) · Toxicological Sciences · doi:10.1093/toxsci/kfag077

Context-Aware Multilevel Classification of Semantic Relations in Drug-Adverse Drug Reaction (ADR) Networks–Predicting Drug-Induced Liver Injury (DILI) as a Case Study

Vos, R., van Mulligen, E. M., & Kors, J. A. (2026) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.5c00252

Development and prospective validation of a machine learning model for risk stratification of drug-induced liver injury using real-world clinical data

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