Toward Predictive Design of BBB-Crossing Inorganic Nanomaterials: Integrating Nano–Bio Interfacial Mechanisms, Molecular Simulations, and Artificial Intelligence

Abstract

The blood-brain barrier (BBB) severely limits therapeutic delivery to the central nervous system. Inorganic nanomaterials provide tunable platforms for brain-targeted delivery, but generalizable structure–activity relationships (SARs) linking material properties and biological identity to BBB transport remain lacking. This Perspective summarizes major transport routes used by inorganic nanomaterials, including receptor- and adsorptive-mediated transcytosis, transporter-associated uptake, reversible barrier modulation, paracellular transport under compromised conditions, and nasal-brain delivery. We then assess molecular simulations and artificial intelligence (AI) for resolving receptor recognition, protein-corona evolution, glycocalyx screening, membrane wrapping, intracellular trafficking, and transport outcomes. We emphasize that direct membrane-permeation models developed for small molecules cannot be equated with physiological nanoparticle transcytosis. Accordingly, we propose a multiscale framework in which atomistic and coarse-grained simulations quantify discrete interfacial events, kinetic or state-transition models represent endocytosis-sorting-exocytosis, and nanomaterial-specific AI integrates standardized experimental outcomes while controlling data leakage and model heterogeneity. Coupled with advanced human BBB models and external validation, this framework may support mechanism-guided design of safer and more effective brain-targeted nanomaterials.