In Silico Methods, Simulations, and Design of Drug‐Delivery Nanocarriers

Abstract Nano drug delivery systems (NDDS) hold great promise for enhancing drug targeting, bioavailability, and controlled release. However, their clinical translation is hindered by low delivery efficiency, rapid clearance, and complex biological barriers. To overcome these hurdles, a mechanism‐driven design approach is essential. Computational modeling is essential in this strategy, serving explanatory and predictive roles. Explanatory modeling uses simulations to clarify the mechanisms behind observed phenomena, while predictive modeling aims to make quantitative, a priori forecasts that are later validated with new data. While the field is moving toward predictive frameworks, most current studies are still explanatory. This review examines recent advances in NDDS modeling across four topics: i) drug–carrier interactions, ii) protein corona, iii) nanoparticle‐membrane interactions, and iv) tumor penetration and biodistributions. The current models are critically assessed in terms of their position on the explanatory‐to‐predictive spectrum. Furthermore, the essential prerequisites for achieving reliable prediction, including benchmark datasets, rigorous uncertainty quantification, and prospective validation that connects model outputs to clinically relevant endpoints, are outlined. By integrating multi‐scale simulations with data‐driven tools, a measured path can be charted from mechanism‐based insights toward the development of quantitatively predictive NDDS models for clinical applications.