Download file (3.71 MB)
Download as zip (2.99 MB)

Collection

Citation

Description:

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.

Rights:

• Creative Commons BY-NC Attribution-NonCommercial 4.0 International
  

Keyword: artificial intelligence, in silico optimization, machine learning, nanocarrier design, nanomedicine, targeted drug delivery

Date published: 2025-08-07

Publisher: Wiley

Journal:

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

Funding:

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

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1002/adma.202510239

Related item:

Other identifier(s):

Contact agent:

Updated at: 2025-10-28 12:30:13 +0900

Published on MDR: 2025-10-28 12:16:26 +0900