# Artificial Intelligence‐Driven Nanoarchitectonics for Smart Targeted Drug Delivery

https://mdr.nims.go.jp/datasets/e8190a99-4e8f-4352-a69b-28d183c36428

## File

- [AdvMater.25_37_e10239.pdf](https://mdr.nims.go.jp/filesets/2b6fe203-5ac0-4891-8305-7f59e5e9085d/download) ([Detail](https://mdr.nims.go.jp/filesets/2b6fe203-5ac0-4891-8305-7f59e5e9085d.md))

## Id

e8190a99-4e8f-4352-a69b-28d183c36428

## Local identifier



## Visibility

open_to_public

## State

published

## Created at

2025-10-24T06:47:06.166447Z

## Updated at

2025-10-28T03:30:13.273548Z

## Published at

2025-10-28T03:16:26.679077Z

## Doi



## First published url

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

## Date published

2025-08-07

## Recorded date published

2025-10

## Resource type

journal_article

## Manuscript type

vor

## Collection



## Title

- title: Artificial Intelligence‐Driven Nanoarchitectonics for Smart Targeted Drug
    Delivery
  title_type: original
  lang: en

## Description

- 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.'
  description_type: abstract
  lang: und

## Creator

- name: Hayeon Bae
  role: author
- name: Hyunsub Ji
  role: author
- name: Konstantin Konstantinov
  role: author
- name: Ronald Sluyter
  role: author
- name: Katsuhiko Ariga
  role: author
  orcid: https://orcid.org/0000-0002-2445-2955
  organization: National Institute for Materials Science
- name: Yong Ho Kim
  role: author
- name: Jung Ho Kim
  role: author

## Contact agent



## Publisher

organization: Wiley

## Managing organization



## Keyword

- subject: artificial intelligence
  schema: not_defined
- subject: in silico optimization
  schema: not_defined
- subject: machine learning
  schema: not_defined
- subject: nanocarrier design
  schema: not_defined
- subject: nanomedicine
  schema: not_defined
- subject: targeted drug delivery
  schema: not_defined

## Rights

- identifier: https://creativecommons.org/licenses/by-nc/4.0/

## Other identifier(s)



## Data origin

- data_origin_type: other

## Embargo



## Journal

- title: Advanced Materials
  issn: '09359648'
  volume: '37'
  issue: '42'
  article_number: e10239

## Conference



## Related item



## Funding

- identifier: RS‐2023‐00331900
  funder_name: Ministry of Food and Drug Safety
- identifier: 2023R1A2C3005731
  funder_name: National Research Foundation of Korea

## Instrument



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## Specimen



## Chemical composition



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## Fileset

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  filename: AdvMater.25_37_e10239.pdf
  content_type: application/pdf
  size: 3889055
  md5: d5cb557d35db9d97cabc43b12cafab03

## Thumbnail

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filename: AdvMater.25_37_e10239.pdf