{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T10:29:10Z","timestamp":1786530550963,"version":"build-2736575974"},"reference-count":63,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Pharmacol."],"abstract":"<jats:p>Drug-induced liver injury (DILI) is a critical safety issue in drug development, characterized by its idiosyncratic nature, complex mechanisms, and poor predictability in standard preclinical models. High-dimensional omics strategies, particularly toxicogenomics, have attracted increased interest in addressing the complexity of hepatotoxicity, especially in the context of emerging artificial intelligence (AI) technologies. This review traces the evolution of AI and machine learning (ML) within DILI-related omics research, highlighting toxicogenomics as a primary driver of advancement in this field. We first explore the early studies in computational toxicogenomics, which primarily focused on exploratory approaches, utilizing clustering, time-series, co-expression, and basic pathway analyses to identify molecular signatures indicative of nascent liver injury. We then examine how the adoption of supervised machine learning enabled robust predictive modeling, facilitating systematic feature selection, signature refinement, and rigorous validation. More recently, the field has been further transformed by deep learning, biologically informed network architectures, and generative artificial intelligence. Across these methodological eras, AI has enhanced mechanistic interpretation by identifying biologically relevant signatures, integrating multimodal evidence, and strengthening evidence for established DILI mechanisms, including oxidative stress, mitochondrial dysfunction, altered xenobiotic metabolism, inflammation, and cell death. Ultimately, the synergy of AI and DILI toxicogenomics has transitioned the discipline from descriptive profiling toward mechanism-driven predictive toxicology.<\/jats:p>","DOI":"10.3389\/fphar.2026.1907884","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T09:35:21Z","timestamp":1786527321000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Evolution of artificial intelligence and machine learning in DILI toxicogenomics: from descriptive profiling to mechanistic insights"],"prefix":"10.3389","volume":"17","author":[{"given":"Mohammad Sujaur","family":"Rahman","sequence":"first","affiliation":[{"name":"Department of Information Science, University of Arkansas at Little Rock","place":["Little Rock, AR, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minjun","family":"Chen","sequence":"additional","affiliation":[{"name":"Division of Bioinformatics and Biostatistics, US FDA\u2019s National Center for Toxicological Research","place":["Jefferson, AR, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"key":"B1","first-page":"4759","article-title":"Application of complementary DNA microarray technology to carcinogen identification, toxicology, and drug safety evaluation","volume":"59","author":"Afshari","year":"1999","journal-title":"Cancer Res."},{"key":"B2","doi-asserted-by":"publisher","first-page":"100147","DOI":"10.1016\/j.comtox.2020.100147","article-title":"Extrapolation of drug induced liver injury responses from cancer cell lines using machine learning approaches","volume":"17","author":"Aguayo-Orozco","year":"2021","journal-title":"Comput. 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