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説明:

In this perspective, we introduce an AI-driven nanoarchitectonics framework 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, we illustrate how AI tools, from generative design algorithms to predictive pharmacokinetic models, are transforming the field from empirical formulation toward mechanism-informed and AI-driven intelligent design. We conclude by highlighting current limitations and outlining future directions for the integration of AI and nanoarchitectonics, with a focus on enabling clinically translatable nanomedicine platforms.

権利情報:

• Creative Commons BY-NC Attribution-NonCommercial 4.0 International
  

キーワード: artificial intelligence, in silico optimization, machine learning, nanocarrier design, nanomedicine, targeted drug delivery

刊行年月日: 2025-08-07

出版者: Wiley

掲載誌:

• Advanced Materials (ISSN: 09359648) vol. 37 issue. 42 e10239

研究助成金:

• Ministry of Food and Drug Safety RS‐2023‐00331900
• National Research Foundation of Korea 2023R1A2C3005731

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1002/adma.202510239

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更新時刻: 2025-10-28 12:30:13 +0900

MDRでの公開時刻: 2025-10-28 12:16:26 +0900