Authors: Tando Kulesi
Abstract: Targeted drug delivery represents a transformative approach in modern therapeutics, aiming to precisely deliver pharmaceutical agents to specific tissues, cells, or intracellular compartments. This approach significantly improves therapeutic efficacy while minimizing off-target side effects commonly associated with conventional systemic drug administration. Nanocarriers—engineered nanoscale vehicles such as liposomes, polymeric nanoparticles, dendrimers, and metallic nanostructures—have become central to targeted drug delivery due to their tunable physicochemical properties and ability to navigate complex biological environments. Despite their promise, designing nanocarriers that achieve optimal targeting, stability, and controlled release remains a challenging task involving multifaceted biological and physicochemical interactions. Artificial Intelligence (AI), especially through machine learning and deep learning, is revolutionizing this design process by enabling the analysis and interpretation of complex datasets, predicting nanocarrier behavior in biological systems, and optimizing their design parameters for improved performance. This paper thoroughly reviews the current advances in applying AI for the design of nanocarriers, explores successful case studies, discusses inherent challenges, and envisions future directions that could dramatically accelerate nanomedicine development and personalized healthcare.
DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.544
