AI and Omics-Based Approaches in Predictive Toxicology: Shaping the Future of Drug Safety Assessment
Akash Kishor Rokade · International Journal of Pharmacy and Pharmaceutical Research · 2025
The latest rapid advancement of artificial intelligence (AI) and omics technologies has brought radical innovations into predictive toxicology.These techniques have the potential to strengthen the detection of toxicological risks at an early stage, reduce reliance on animal models, and make the drug discovery process more efficient and accurate.Artificial intelligence (AI), especially machine learning (ML), makes it possible to examine high-dimensional, sophisticated data produced by omics technologies such as genomics, transcriptomics, proteomics, and metabolomics.When combined, these technologies offer a more mechanistic and precise view of toxicity and assist in data-informed decision-making in pharmacology, environmental science, and regulatory toxicology.The integration of AI-omics systems into existing frameworks poses challenges for data quality, interpretability, ethics, and regulatory acceptance.This review critically discusses the promise and limitations of AI and omics in toxicology, with an emphasis on recent developments, challenges in implementation, and the future.Furthermore, it highlights the importance of interdisciplinary collaboration and standardized validation frameworks to ensure ethical, transparent, and reliable application of these technologies in real-world toxicological contexts.