Artificial intelligence-guided nanoparticle design for advanced targeted drug delivery

This editorial aimed to explore the critical role of artificial intelligence (AI) in accurately predicting the structural design of nanoparticles (NPs) during targeted therapy of diseases. Based on experience, it is always surprising that perfect control of NP properties-including size, zeta potential, type, and surface modifications-using smart tools, will be more critical for optimal outcomes than trial and error. It is envisioned that the AI will change the game by predicting NPs' behavior, optimizing formulations, and speeding up clinical trials via the use of supervised learning, deep neural networks, graph neural networks, and generative models. In this context, various AI have led to an increase in drug loading efficiency and mRNA medication delivery. To achieve personalized therapy using NPs, however, issues including data quality, model interpretability, ethical frameworks, and multidisciplinary cooperation should be resolved. To enhance human knowledge and facilitate safer and more precise advancements in healthcare, this editorial urges the proper integration of AI in pharmaceutical/medical nanotechnology.