人工智能在中枢神经系统疾病药物研发中的应用与展望OA

Drug research and development for central nervous system(CNS)diseases has long been hindered by challenges including difficulties in target identification,restricted blood-brain barrier(BBB)penetration,and low effi-ciency of clinical translation.In recent years,artificial intelligence(AI),combined with single-cell and spatial multi-omics,protein structure prediction,and generative models,has provided new technical strategies for drug research and de-velopment in the field of CNS diseases.This article reviews the advances in the application of AI in drug research and de-velopment for CNS diseases,including disease-related cell state identification,the analysis of cell-cell communication and spatial microenvironment,protein structure prediction and molecular design,as well as the evaluation of BBB permeabil-ity,brain exposure,and drug safety.In addition,this article summarizes the role of perturbomics,brain organoids,and BBB-on-a-chip platforms in validating AI predictions and discusses the closed-loop research framework of"prediction-validation-iteration".Finally,this article analyzes the issues such as insufficient data standardization,limited model inter-pretability,and limited physiological relevance of in vitro models and proposes the future directions of AI-driven drug re-search and development for CNS diseases.