Abstract

The prediction of three-dimensional protein structures has undergone a paradigm shift, driven primarily by deep learning-based tools. These advances are beginning to permeate neuroscience research, offering new routes for understanding the molecular basis of neurological disease and accelerating early-stage central nervous system (CNS) drug discovery. This article examines the current state of in silico structural modeling as it applies to translational neuroscience, highlighting areas of progress — including G protein-coupled receptor (GPCR) pharmacology, cryo-EM-informed structural neurobiology, protein aggregation in neurodegeneration, and AI-driven small molecule discovery — alongside discussion of limitations. These include the misinterpretation of static computational models, and the continuing gap between structural insight and clinical validation. The argument advanced here is that structural modeling has already meaningfully altered the landscape of early drug discovery, but that its translational promise will only be realised through sustained interdisciplinary integration and rigorous experimental follow-through.