Bioinspired Nanocarriers for Targeted Drug Delivery

Abstract

Targeted drug delivery remains a major challenge because conventional nanocarriers often exhibit limited biological compatibility, rapid immune clearance, and inconsistent accumulation at diseased tissues. Bioinspired nanocarriers, designed by mimicking natural cellular membranes and biological transport mechanisms, offer a promising alternative for improving therapeutic precision. Despite rapid progress, limited research has integrated artificial intelligence (AI) with bioinspired nanocarrier engineering to optimize carrier selection, targeting efficiency, and therapeutic performance simultaneously. This study addresses that gap by proposing an AI-assisted framework for predicting the performance of bioinspired nanocarriers using multimodal physicochemical and biological characteristics. The framework combines deep feature learning with explainable machine learning to identify critical design parameters influencing delivery efficiency. A digital simulation environment is developed to evaluate targeting accuracy, cellular uptake, and controlled drug release. The proposed approach demonstrates how computational intelligence can accelerate the rational design of bioinspired drug delivery systems while reducing experimental complexity. The findings contribute to the growing intersection of nanomedicine, artificial intelligence, and precision therapeutics