Artificial intelligence in preclinical research: enhancing digital twins and organ-on-chip to reduce animal testing

• AI revolutionizes preclinical research, offering alternatives to animal testing. • Techniques like ML, DL, OoC, and DTs enable precise drug safety simulations. • AI-driven methods adhere to the 3Rs principle: Replace, Reduce, Refine. • Real-world tools like Tox-GAN enhance drug discovery efficiency. • This review bridges AI advancements with ethical and regulatory standards. Artificial intelligence (AI) is reshaping preclinical drug research offering innovative alternatives to traditional animal testing. Advanced techniques, including machine learning (ML), deep learning (DL), AI-powered digital twins (DTs), and AI-enhanced organ-on-a-chip (OoC) platforms, enable precise simulations of complex biological systems. AI plays a critical role in overcoming the limitations of DTs and OoC, improving their predictive power and scalability. These technologies facilitate early-stage, reliable evaluations of drug safety and efficacy, addressing ethical concerns, reducing costs, and accelerating drug development while adhering to the 3Rs principle (Replace, Reduce, Refine). By integrating AI with these advanced models, preclinical research can achieve greater accuracy and efficiency in drug discovery. This review examines the transformative impact of AI in preclinical research, highlighting its advancements, challenges, and the critical steps needed to establish AI as a cornerstone of ethical and efficient drug discovery.