An Integrated Artificial Intelligence Framework for Nanomedicine Engineering
4 affiliations
- 1School of Pharmaceutical Sciences, Fudan University & amp, Key Laboratory of Smart Drug Delivery, Ministry of Education State Key Laboratory of Advanced Drug Formulations for Overcoming Delivery Barriers Shanghai China
- 2State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences (ICMS) University of Macau Macau China
- 3Microsoft Research Asia Shanghai China
- 4Quzhou Fudan Institute Quzhou Zhejiang,China
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.
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BibTeX
@article{dong2026integrated,
title = {{An Integrated Artificial Intelligence Framework for Nanomedicine Engineering}},
author = {Meichen Dong and Qin Luo and Xinyi Ding and Chaoqi Lu and Ru Zhang and Qingqing Tan and Zhiqing Pang and Defang Ouyang and Dongsheng Li and Jiaxuan Xia and Jianxin Wang},
journal = {Advanced Science},
year = {2026},
month = oct,
doi = {10.1002/advs.78125},
url = {https://doi.org/10.1002/advs.78125},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - An Integrated Artificial Intelligence Framework for Nanomedicine Engineering
AU - Meichen Dong
AU - Qin Luo
AU - Xinyi Ding
AU - Chaoqi Lu
AU - Ru Zhang
AU - Qingqing Tan
AU - Zhiqing Pang
AU - Defang Ouyang
AU - Dongsheng Li
AU - Jiaxuan Xia
AU - Jianxin Wang
PY - 2026
DA - 2026/10/26
JO - Advanced Science
DO - 10.1002/advs.78125
UR - https://doi.org/10.1002/advs.78125
AB - 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.
SN - 2198-3844
ER -