An Integrated Artificial Intelligence Framework for Nanomedicine Engineering.

Authors

Meichen Dong, Qin Luo, Xinyi Ding, Chaoqi Lu, Ru Zhang, Qingqing Tan, Zhiqing Pang, Defang Ouyang, Dongsheng Li, Jiaxuan Xia, Jianxin Wang

Published in

Advanced science (Weinheim, Baden-Wurttemberg, Germany). Oct 26, 2026. Epub Oct 26, 2026.

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.

PMID:
42934944
Bibliographic data and abstract were imported from PubMed on 27 Oct 2026.

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