Artificial Intelligence-Driven Nanoarchitectonics for Smart Targeted Drug Delivery

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.