Artificial intelligence-enabled nanomedicine: enhancing drug design and predictive modeling in pharmaceutics

Abstract The integration of artificial intelligence (AI) and nanomedicine has initiated a revolutionary phase in pharmaceutical research, facilitating progress in targeted drug delivery, controlled release, and personalized therapeutics. This review explores how AI-driven methods are integrated with nanocarrier systems such as liposomes, polymeric nanoparticles, and dendrimers. By harnessing high-dimensional datasets and predictive modeling, advanced techniques like deep learning, reinforcement learning, and graph neural networks have greatly enhanced pharmacokinetic predictions. As a result, dose–response forecasts have become more accurate, development timelines have shortened, and the experimental workload has been reduced. These technologies confront challenges in data standardization, algorithmic transparency, and regulatory adherence. While agencies such as the Food and Drug Administration and European Medicines Agency continue to update their guidelines, there remains an urgent need for a unified, flexible framework that can keep pace with rapid technological progress. This article calls for stronger cross-disciplinary cooperation among computer scientists, pharmaceutical researchers, and regulatory experts to address these challenges and fully harness AI-Enabled Nanomedicine for transforming personalized drug development.