Evidence map›Paper›PMID 40772340›Full record

ReviewAdvanced materials (Deerfield Beach, Fla.)2025

Artificial Intelligence-Driven Nanoarchitectonics for Smart Targeted Drug Delivery.

Hayeon Bae, Hyunsub Ji, Konstantin Konstantinov, Ronald Sluyter, Katsuhiko Ariga, Yong Ho Kim, Jung Ho Kim

Abstract readReview
In one paragraph

Review in Advanced materials (Deerfield Beach, Fla.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

42 citing papers in PubMed.

  1. Bioactive lipid-derived nanoparticles for RNA delivery.Materials today (Kidlington, England) · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Hayeon BaeInstitute for Superconducting & Electronic Materials (ISEM), Faculty of Engineering and Information Sciences, University of Wollongong Innovation Campus, Squires Way, North Wollongong, NSW, 2500, Australia.
Hyunsub JiDepartment of Nano Science and Technology, SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, 16419, Republic of Korea.
Konstantin KonstantinovInstitute for Superconducting & Electronic Materials (ISEM), Faculty of Engineering and Information Sciences, University of Wollongong Innovation Campus, Squires Way, North Wollongong, NSW, 2500, Australia.
Ronald SluyterSchool of Science, Faculty of Science, Medicine and Health and Molecular Horizons, Faculty of Science, Medicine and Health, University of Wollongong, Wollongong, NSW, 2522, Australia.
Katsuhiko ArigaDepartment of Advanced Materials Science, Graduate School of Frontier Sciences, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8561, Japan.
Yong Ho KimDepartment of Nano Science and Technology, SKKU Advanced Institute of Nanotechnology (SAINT), Sungkyunkwan University (SKKU), 2066 Seobu-ro, Jangan-gu, Suwon, Gyeonggi-do, 16419, Republic of Korea.
Jung Ho KimInstitute for Superconducting & Electronic Materials (ISEM), Faculty of Engineering and Information Sciences, University of Wollongong Innovation Campus, Squires Way, North Wollongong, NSW, 2500, Australia.ORCID 0000-0003-4931-3553

Funding

Ministry of Food and Drug Safety RS-2023-00331900National Research Foundation of Korea 2023R1A2C3005731
6 · The paper itself

Abstract

The development of data-driven and targeted drug delivery systems is essential for advancing precision therapeutics. Despite substantial progress in nanocarrier development, conventional platforms continue to face major challenges in clinical translation due to biological complexity, off-target accumulation, and limited adaptability to dynamic physiological environments. The integration of nanoarchitectonics and artificial intelligence (AI) offers an advanced strategy for engineering delivery systems that are structurally programmable, stimuli-responsive, and autonomously optimized. Nanoarchitectonics enables the construction of hierarchical nanostructures with precise spatial and temporal control, while AI facilitates modeling, prediction, and iterative optimization throughout the development pipeline. In this perspective, an AI-driven nanoarchitectonics framework is introduced 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, how AI tools are illustrated, from generative design algorithms to predictive pharmacokinetic models, are transforming the field from empirical formulation toward mechanism-informed and AI-driven intelligent design. By highlighting current limitations and outlining future directions for the integration of AI and nanoarchitectonics, are concluded with a focus on enabling clinically translatable nanomedicine platforms.

Indexed as

Artificial IntelligenceDrug Delivery SystemsNanostructuresAnimalsDrug CarriersHumansNanomedicineDrug Carriersartificial intelligencein silico optimizationmachine learningnanocarrier designnanomedicinetargeted drug delivery

Identifiers

PMID40772340
PMCPMC12548523

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.