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Drug Delivery: Overview

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The selection of a drug's delivery route depends upon its physicochemical properties, including lipid or water solubility and ionization, as well as the therapeutic requirement, such as immediate or sustained effect. These routes can be divided into three primary categories: enteral, parenteral, and topical.
Enteral delivery involves administering drugs directly through swallowing, sublingual placement, or buccal application. Orally administered drugs predominantly navigate the...

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Polymalic Acid-based Nano Biopolymers for Targeting of Multiple Tumor Markers: An Opportunity for Personalized Medicine?

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Artificial Intelligence-Driven Nanoarchitectonics for Smart Targeted Drug Delivery.

Hayeon Bae^1,2, Hyunsub Ji², Konstantin Konstantinov¹

¹Institute for Superconducting & Electronic Materials (ISEM), Faculty of Engineering and Information Sciences, University of Wollongong Innovation Campus, Squires Way, North Wollongong, NSW, 2500, Australia.

Advanced Materials (Deerfield Beach, Fla.)

|August 7, 2025

Summary

Artificial intelligence (AI) and nanoarchitectonics create advanced drug delivery systems. This AI-driven framework enhances targeted therapies by optimizing nanocarrier design for better clinical translation.

Keywords:

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

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Area of Science:

Biomedical Engineering
Nanotechnology
Computational Biology

Background:

Precision therapeutics require advanced drug delivery systems.
Conventional nanocarriers face challenges in clinical translation, including off-target effects and poor adaptability.
Nanoarchitectonics and artificial intelligence (AI) integration offers a novel strategy for programmable and optimized delivery systems.

Purpose of the Study:

To introduce an AI-driven nanoarchitectonics framework for targeted drug delivery.
To outline a strategy for overcoming limitations in nanomedicine clinical translation.
To highlight the transformative role of AI in designing intelligent nanomedicine platforms.

Main Methods:

Bioinformatic profiling for molecular target identification.
Machine learning (ML)-guided surface engineering for enhanced targeting specificity.
In silico modeling for predicting delivery dynamics and systemic distribution.

Main Results:

AI tools, including generative design and predictive pharmacokinetic models, are revolutionizing nanomedicine development.
The proposed framework enables mechanism-informed and AI-driven intelligent design.
Demonstrated potential for creating structurally programmable and stimuli-responsive nanocarriers.

Conclusions:

AI-driven nanoarchitectonics represents a paradigm shift from empirical formulation to intelligent design in drug delivery.
This integrated approach facilitates the development of clinically translatable nanomedicine platforms.
Future directions focus on further integrating AI and nanoarchitectonics for enhanced therapeutic efficacy and safety.