The role of artificial intelligence in advancing neuropharmacology

Abstract

The role of artificial intelligence in advancing neuropharmacology - AI is rapidly transforming the field of neuropharmacology by introducing sophisticated computational methodologies capable of addressing the multifactorial complexity of central nervous system (CNS) disorders. By integrating ML, DL, and NLP, AI enables the analysis of high dimensional biomedical data, including genomics, proteomics, metabolomics, neuroimaging, and electronic health records. These capabilities support high throughput screening, accelerate de novo drug design, and enhance target identification by uncovering subtle biological correlations and mechanistic insights that often elude traditional experimental paradigms. AI based frameworks facilitate in silico prediction of pharmacokinetics and pharmacodynamics, drug–target interactions, and blood–brain barrier permeability. This empowers researchers to develop personalized therapeutic strategies for complex neurodegenerative and neuropsychiatric disorders such as Alzheimer’s disease, Parkinson’s disease, schizophrenia, and major depressive disorder. Several case studies underscore AI's translational potential: Benevolent AI’s application of NLP for drug repurposing in amyotrophic lateral sclerosis (ALS), Deep Genomics’ AI driven discovery of RNA targeted molecules, and Atomwise’s structure based compound optimization exemplify AI's impact across discovery and development pipelines. Despite its promise, the integration of AI into neuropharmacology is not without challenges. The opacity of deep learning models (“black box” problem), data heterogeneity, model generalizability, and evolving regulatory frameworks necessitate rigorous validation and interpretability efforts. This review comprehensively explores current applications, technological advancements, and future trajectories of AI in neuropharmacology. As the discipline evolves, AI stands poised as a foundational tool in the advancement of precision medicine, supporting more efficient, targeted, and individualized CNS pharmacotherapies. Keywords: Artificial intelligence, Neuropharmacology, CNS drug discovery, Machine learning, Biomedical informatics.

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