AI-guided design of nanocarriers for targeted drug delivery in tumors

Abstract

Targeted drug delivery represents a critical advancement in cancer treatment, offering improved efficacy and minimized systemic toxicity. Nanocarriers have emerged as promising vehicles for site-specific delivery of anticancer drugs due to their customizable physicochemical properties. However, designing nanocarriers capable of effective tumor targeting remains a complex challenge, given the diverse variables involved in tumor biology, drug kinetics, and nanoparticle interactions with biological environments. The  integration of artificial intelligence (AI) into the nanocarrier design process is transforming the landscape of drug delivery research. This article explores the use of AI, particularly machine learning and deep learning models, in guiding the rational design of nanocarriers for tumor-targeted drug delivery. A framework is proposed for utilizing AI tools to optimize design parameters, predict biological interactions, and improve formulation outcomes, thus accelerating the development of effective cancer therapies.