The development of data-driven and targeted drug delivery systems is essential for advancing precision therapeutics. Despite substantial progress in nanocarrier development, conventional platforms continue to face major challenges in clinical translation due to biological complexity, off-target accumulation, and limited adaptability to dynamic physiological environments. The integration of nanoarchitectonics and artificial intelligence (AI) offers an advanced strategy for engineering delivery systems that are structurally programmable, stimuli-responsive, and autonomously optimized. Nanoarchitectonics enables the construction of hierarchical nanostructures with precise spatial and temporal control, while AI facilitates modeling, prediction, and iterative optimization throughout the development pipeline. In this perspective, an AI-driven nanoarchitectonics framework is introduced for targeted drug delivery, structured around three key phases: 1) molecular target identification through bioinformatic profiling, 2) machine learning (ML)-guided surface engineering to enhance targeting specificity, and 3) in silico modeling of delivery dynamics and systemic distribution. Drawing on recent advances and representative case studies, how AI tools are illustrated, from generative design algorithms to predictive pharmacokinetic models, are transforming the field from empirical formulation toward mechanism-informed and AI-driven intelligent design. By highlighting current limitations and outlining future directions for the integration of AI and nanoarchitectonics, are concluded with a focus on enabling clinically translatable nanomedicine platforms.
Bae et al. (Thu,) studied this question.