Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Abstract

This review assesses the current state of predictive modeling for forecasting nanoparticles (NPs) in biomedical applications. Significant achievements have been realized through computational tools such as nano‐quantitative structure–activity relationship frameworks and molecular dynamics simulations. These methods successfully accelerate nanocarrier screening by predicting critical parameters, including protein corona formation, cellular uptake kinetics, and initial toxicity profiles. Despite these advances, the efficacy of current models is significantly constrained. A key challenge is the inability of simplified computational platforms to accurately replicate the immense complexity and dynamic nature of the in vivo environment. Furthermore, the development of robust predictive algorithms is hampered by a scarcity of large, standardized experimental datasets, leading to limited accuracy in modeling time‐dependent processes such as NP degradation and long‐term bioaccumulation. To overcome these hurdles, future efforts must prioritize the global adoption of standardized data generation protocols. The review urgently calls for integrating artificial intelligence and machine learning with systems biology approaches, alongside the creation of open‐access predictive databases. Ultimately, these collaborative efforts are essential to bridging the gap between materials science, clinical translation, and regulation, thus achieving predictive and safe NPs.