An Integrated Artificial Intelligence Framework for Nanomedicine Engineering

ABSTRACT

The translation of nanomedicines is frequently bottlenecked by trial‐and‐error optimization. Integrating artificial intelligence (AI) transforms this process into a closed‐loop, experimentally grounded discovery ecosystem. This Review provides a roadmap for this paradigm, demonstrating how AI, high‐throughput testing, and nanoscale design converge to generate clinically translatable nanotherapeutics. We emphasize the necessity of standardized multimodal data—spanning pharmacokinetic, biodistribution, and drug‐release profiles—anchored strictly to validated biological phenomena. Furthermore, we evaluate an algorithmic spectrum, ranging from classical machine learning to physics‐informed neural networks (PINNs) and reinforcement learning (RL), to align models with specific formulation objectives. To bridge computational design and empirical reality, we examine how high‐throughput technologies, such as robotic laboratories and in vivo DNA‐barcoding multiplexing, operationalize the “Design‐Make‐Test‐Analyze” (DMTA) cycle for dynamic model recalibration. Ultimately, this review demonstrates how integrating AI, physical constraints, and automated validation accelerates the rational design of next‐generation nanomedicines.