LAUSR

Transition metal complexes (TMCs) have emerged as promising agents in neurotherapeutics due to their redox activity, coordination flexibility, and ability to interact with biomolecular targets. However, their cytotoxic effects on neural tissues remain insufficiently understood, posing challenges for safe and targeted applications. Computational approaches provide powerful tools for unraveling the mechanisms underlying TMC-induced cytotoxicity, enabling the prediction of biological behavior at the molecular level. This study explores how advanced in silico methods, such as molecular docking, density functional theory (DFT), and molecular dynamics (MD) simulations, are applied to assess the structure, reactivity, and interaction profiles of TMCs in neurological contexts. Particular focus is placed on modeling neurotoxicity mechanisms, evaluating blood-brain barrier penetration, and identifying structure-activity relationships (SARs) relevant to neurodegenerative diseases and pediatric brain cancers. Comparative analyses across different metal centers and ligand frameworks are presented, revealing how variations in electronic structure influence biological outcomes. Moreover, limitations of current computational methodologies are addressed, along with challenges in accurately modeling the neural microenvironment. Opportunities for future research include the integration of machine learning to enhance predictive accuracy, automate compound screening, and guide rational design of neuroactive metal-based drugs. The review also emphasizes the need for standardized protocols to improve reproducibility and biological relevance in computational neurotoxicology. By aligning the capabilities of computational chemistry with the demands of neurobiology, this study highlights a strategic framework for advancing safe, targeted, and effective transition metal-based therapies in the nervous system.