神经退行性疾病（NDs），如阿尔茨海默病、帕金森氏病，特征为记忆、思维等功能逐渐受损，这类疾病在老龄化社会中日益普遍，严重威胁人类健康。其病理主要表现为β淀粉样蛋白、Tau蛋白等的错误折叠和聚集，导致脑内蛋白质沉积，开发出能够有效识别和靶向这些蛋白的新型示踪剂，将对神经退行性疾病的早期诊断和治疗产生重大影响。本研究使用三种常用分子对接软件（MOE、AutoDock、AutoDock Vina）进行神经性退行性疾病相关蛋白的小分子示踪剂虚拟筛选，旨在识别在对接分数上表现出色的小分子及其结合位点，并比较各软件的能力与特点，为未来针对这类疾病的小分子示踪剂研究提供理论依据和方向。

Neurodegenerative diseases (NDs), such as Alzheimer's disease and Parkinson's disease, are characterized by gradual impairment of memory, thinking, and other functions, and such diseases are becoming increasingly prevalent in aging societies, posing a serious threat to human health. Their pathology is mainly characterized by misfolding and aggregation of β-amyloid, Tau proteins, etc., leading to protein deposition in the brain, and the development of novel tracers that can effectively recognize and target these proteins will have a significant impact on the early diagnosis and treatment of neurodegenerative diseases. In this study, we used three commonly used molecular docking software (MOE, AutoDock, AutoDock Vina) to perform virtual screening of small molecule tracers for neurodegenerative disease-related proteins, aiming to identify small molecules and their binding sites with excellent performance on docking scores, and compare the ability and characteristics of each software to provide theoretical basis and direction for future research on small molecule tracers targeting such diseases. and direction for future research on small molecule tracers for such diseases.