Preprint
Machine Learning

Artificial Intelligence‐Driven Nanoarchitectonics for Smart Targeted Drug Delivery

Hayeon Bae(Institute for Superconducting & Electronic Materials (ISEM) Faculty of Engineering and Information Sciences University of Wollongong Innovation Campus Squires Way North Wollongong NSW 2500 Australia), Hyunsub Ji(Department 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 Konstantinov(Institute for Superconducting & Electronic Materials (ISEM) Faculty of Engineering and Information Sciences University of Wollongong Innovation Campus Squires Way North Wollongong NSW 2500 Australia), Ronald Sluyter(School 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 Ariga(Department of Advanced Materials Science Graduate School of Frontier Sciences The University of Tokyo 5‐1‐5 Kashiwanoha Kashiwa Chiba 277–8561 Japan), Yong Ho Kim(Department 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 Kim(Institute for Superconducting & Electronic Materials (ISEM) Faculty of Engineering and Information Sciences University of Wollongong Innovation Campus Squires Way North Wollongong NSW 2500 Australia)
August 7, 2025Advanced Materials75 citations
DOI

75

Citations

2

Influential Citations

Advanced Materials

Venue

2025

Year

Abstract

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.

Analysis

Why This Paper Matters

This paper addresses a critical bottleneck in precision therapeutics: the gap between nanocarrier development and clinical translation. Despite decades of research, conventional drug delivery systems suffer from off-target accumulation and poor adaptability to dynamic physiological environments. By proposing an AI-driven nanoarchitectonics framework, the authors offer a systematic pathway to overcome these challenges through data-driven design and optimization.

The integration of AI with nanoarchitectonics is timely. As biological datasets grow and machine learning models mature, the opportunity to move from empirical formulation to mechanism-informed design becomes tangible. This perspective provides a structured blueprint that could guide both researchers and practitioners in leveraging AI for smarter drug delivery systems.

Technical Contributions

The paper's key innovations include:

  • Three-phase framework: A clear pipeline from molecular target identification (bioinformatics) through ML-guided surface engineering to in silico modeling of delivery dynamics.
  • Generative design algorithms: Application of AI to propose novel nanocarrier architectures with desired properties.
  • Predictive pharmacokinetic models: Use of ML to forecast systemic distribution and optimize dosing.
  • Mechanism-informed design: Shift from trial-and-error to data-driven optimization, integrating biological complexity into the design loop.

Results

The paper is a perspective piece and does not present new experimental results. Instead, it synthesizes recent advances and representative case studies to illustrate the potential of AI tools. No quantitative metrics (e.g., targeting efficiency improvements, model accuracy) are reported. The value lies in the conceptual framework and future directions rather than empirical validation.

Significance

For the AI community, this work highlights an underexplored application domain: drug delivery system design. It demonstrates how machine learning, generative models, and predictive simulation can be applied to a complex engineering problem with high clinical impact. The framework could inspire new research at the intersection of AI and nanomedicine, particularly in areas like multi-objective optimization of nanocarrier properties and real-time adaptive delivery systems. For practitioners, it offers a roadmap for integrating AI tools into the drug development pipeline, potentially reducing costs and accelerating time-to-clinic for targeted therapies.

machine-learningdrug-deliverynanoarchitectonicstargeted-therapypharmacokineticsgenerative-designbioinformatics

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