Predictive modeling of nanoparticle-cell interactions using deep learning

Abstract

Nanoparticle-cell interactions are fundamental to the development of nanomedicine, influencing delivery efficacy, toxicity, and biological compatibility. Despite advances in experimental techniques, understanding these interactions remains a complex task due to the multifactorial nature of nanoparticle design and cellular diversity. Deep learning, a data-driven approach within artificial intelligence, has emerged as a transformative tool in modeling and predicting these interactions. By identifying patterns across large  datasets and learning non-linear relationships, deep learning enables accurate prediction of nanoparticle behavior in biological  systems. This article explores the conceptual foundation and application of deep learning in predicting nanoparticle-cell interactions, discusses current methodological strategies, and outlines future directions to enhance the integration of computational intelligence in nanomedicine development.