The rapid expansion of data-driven technologies, particularly machine learning (ML) and artificial intelligence (AI), has substantially influenced biomedical research and drug discovery. In neuropharmacology, the availability of large-scale genomic, proteomic, chemical, and clinical datasets has stimulated the adoption of AI-based approaches to address persistent challenges in neurological drug development, including high attrition rates and the scarcity of disease-modifying therapies. Unlike prior reviews that broadly discuss AI applications in drug discovery or neurology, this narrative review focuses specifically on neuropharmacology, with an emphasis on translational relevance, disease-oriented examples, and real-world constraints. We critically examine the application of AI and ML across key stages of the neuropharmacological drug discovery pipeline, including target identification, drug–target interaction prediction, lead optimization, toxicity assessment, and early-stage clinical translation. Particular attention is given to concrete case studies in neurodegenerative and neurological disorders, illustrating where AI has meaningfully enhanced discovery efficiency and where its anticipated “revolutionary” impact has not yet been realized. In parallel, we analyze the biological, technical, and regulatory barriers that limit the clinical success of AI-driven strategies, including data bias, limited model interpretability, incomplete understanding of brain biology, and translational bottlenecks. By integrating case-based evidence with a critical analytical perspective, this review delineates both the opportunities and limitations of AI in neuropharmacology. We argue that AI is most effective when deployed as a complementary tool alongside mechanistic neuroscience and clinical expertise, rather than as a standalone solution. As AI methodologies continue to mature, their careful, transparent, and ethically governed integration into neuropharmacological research may advance precision medicine and help bridge persistent gaps in the treatment of neurological disorders.
Rathee et al. (Tue,) studied this question.