ARTIFICIAL INTELLIGENCE-DRIVEN SMART NANOCARRIERS FOR PRECISION DRUG DELIVERY: DESIGN, OPTIMIZATION, AND CLINICAL TRANSLATION

Abstract

The development of nanocarrier-based drug delivery systems has historically relied on empirical trial-and-error approaches, resulting in prolonged timelines, excessive resource consumption, and frequent clinical translation failures. This review examines the transformative integration of artificial intelligence, machine learning, and deep learning architectures into precision nanomedicine engineering. We systematically analyze computational frameworks spanning supervised ensemble methods, graph neural networks, generative models, and physics-informed neural networks that decipher complex structure-property-performance relationships across high-dimensional pharmaceutical datasets. AI-driven nano-QSAR platforms incorporating specialized nanodescriptors including physicochemical, morphological, surface chemistry, quantum mechanical, and biological corona parameters enable accurate prediction of critical quality attributes such as encapsulation efficiency, release kinetics, and organ-specific biodistribution prior to wet-lab synthesis. The convergence of AI with microfluidic manufacturing, high-throughput automation, and self-driving laboratories facilitates closed-loop optimization of lipid nanoparticles, polymeric carriers, and nucleic acid therapeutics, compressing multi-year development timelines to months while improving encapsulation efficiency by 25–40%. Hybrid machine learning-physiologically based pharmacokinetic models bridge molecular design to systemic therapeutic outcomes, predicting protein corona dynamics, blood-brain barrier translocation, and tissue-specific accumulation. We address critical challenges including dataset heterogeneity, model interpretability through explainable AI frameworks, and regulatory considerations via the "Rule of Five" principles for AI formulation reliability. Future perspectives highlight foundation models, domain-specific large language models, and patient-specific digital twins for personalized nanomedicine. This comprehensive review establishes AI-driven smart nanocarriers as a paradigm-shifting approach for accelerating clinical translation and achieving precision drug delivery with enhanced therapeutic efficacy and reduced off-target toxicity.