.png){kind=icon}
.png){kind=icon}
Return
Evolution of artificial intelligence and machine learning in DILI toxicogenomics: from descriptive profiling to mechanistic insights
.png){kind=icon}
OA
AI
M
M
DOI：10.3389/fphar.2026.1907884.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
artificial intelligence
machine learning
transcriptomics
generative AI
hepatotoxicity
drug-induced liver injury
toxicogenomics
mechanism-informed prediction
Journal
IF:
4.8
Papers:
5.7K
Citations:
10.6W
Organization
Cited Papers