Artificial Intelligence-Guided Polymeric Nanomedicine: Design, Optimization, and Clinical Translation

Polymeric nanomedicine has emerged as a promising platform for improving drug delivery, therapeutic efficacy, and patient outcomes through the development of nanoscale carriers with controlled and targeted release capabilities. However, the design and optimization of polymeric nanocarriers remains complex due to the large number of formulation variables and intricate biological interactions involved. In recent years, artificial intelligence (AI) has gained significant attention as a transformative tool for accelerating nanomedicine development through data-driven prediction, optimization, and decision-making. Machine learning and deep learning algorithms enable the rapid analysis of complex datasets, facilitating the prediction of nanocarrier properties, drug loading efficiency, release kinetics, pharmacokinetic behavior, and safety profiles. Furthermore, AI supports the rational design of polymeric nanocarriers, reduces experimental burden, and enhances formulation reproducibility. This review provides a comprehensive overview of the integration of AI into polymeric nanomedicine, covering fundamental aspects of polymeric nanocarriers, AI-based design strategies, formulation optimization, biological performance assessment, and clinical translation. The article also discusses current challenges related to data quality, model interpretability, regulatory considerations, and translational barriers. Finally, emerging concepts such as digital twins, generative AI, autonomous experimentation, and self-optimizing drug delivery systems are highlighted as key drivers of next-generation precision nanomedicine. The convergence of AI and polymeric nanotechnology is expected to accelerate pharmaceutical innovation and facilitate the development of safer, smarter, and more personalized therapeutic systems.