The Paradigm Shift in Therapeutics: A Comprehensive Review of Artificial Intelligence in Drug Delivery Systems

Abstract

The integration of Artificial Intelligence (AI) into the pharmaceutical landscape is heralding a new era of precision medicine, particularly in the domain of drug delivery. Traditional drug development is notoriously time-consuming, expensive, and prone to high attrition rates. AI, with its subfields of machine learning (ML) and deep learning (DL), is poised to de-bottleneck this process by enabling the rational design of intelligent, targeted, and responsive drug delivery systems (DDS). This review meticulously outlines the transformative role of AI across the entire spectrum of advanced drug delivery. We explore how AI algorithms leverage vast chemical and biological datasets to design novel nanocarriers, predict their physicochemical properties, and optimize their formulation for enhanced efficacy and safety. A significant focus is placed on AI-driven targeted and stimuli-responsive DDS for oncology, neurological, and inflammatory diseases. Furthermore, we delve into the emergence of AI-powered closed-loop systems for autonomous drug release. The review is supplemented with detailed tables summarizing key algorithms, recent clinical trials, and a landscape analysis of patents, highlighting the intense commercial and academic interest. Finally, we address the current challenges-including data quality, regulatory hurdles, and model interpretability-and propose future directions for the clinical translation of AI-engineered therapeutics. This synthesis underscores AI not merely as a tool but as a disruptive force, poised to unlock personalized, predictive, and precise drug delivery paradigms.