Nano-QSAR modelling of metal oxide nanoparticles for neurotoxicity prediction: Implications for neurotherapeutic design

Metal oxide nanoparticles have emerged as promising candidates for neurological diagnostics and neurotherapeutics due to their tunable physicochemical properties, multifunctionality, and ability to interact with biological systems. However, their neurological benefits must be balanced against potential adverse effects arising from blood–brain barrier (BBB) interactions, cellular uptake, targeting, and prolonged retention. These nanoparticles have shown adverse neurological effects, including oxidative stress, neuroinflammation, mitochondrial dysfunction, protein aggregation, and disruption of signalling pathways. Nano-Quantitative Structure–Activity Relationship (Nano-QSAR) modelling has gained considerable attention as a predictive computational approach that correlates nanoparticle physicochemical descriptors, such as size, zeta potential, and hydrodynamic diameter, with biological responses and toxicity. This review provides a comprehensive overview of the neurotoxic and neurotherapeutic applications of metal oxide nanoparticles, including iron, zinc, titanium, silicon, copper, manganese, and cerium oxides. It places particular emphasis on physicochemical and electronic descriptors. It links them to BBB behavior and major mechanisms of neuronal injury. We discuss recent advances in artificial intelligence, machine learning, and nanoinformatics, critically comparing traditional QSAR, Nano-QSAR, machine-learning with recent emerging trends. The emerging trends like deep-learning approaches have brought new dimensions in interpretability, validation, and suitability for neurotoxicity prediction. The review further highlights current challenges, including limited neurotoxicity-specific datasets, data imbalance, heterogeneous experimental endpoints, inadequate external validation, lack of descriptor standardization, and long-term safety assessment. Future progress requires mechanism-informed and interpretable models that integrate nanoparticle descriptors, exposure conditions, and neurobiological endpoints with targeted experimental validation. Incorporating FAIR data practices, OECD-aligned testing, and Safe-and-Sustainable-by-Design principles could strengthen the regulatory and clinical translation of Nano-QSAR-guided design for molecular neurotherapeutics.