An Integrated Artificial Intelligence Framework for Nanomedicine Engineering

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.