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阿尔茨海默病药物发现的大型语言模型

Tursun Alkam¹, Ebrahim Tarshizi¹, Andrew H Van Benschoten¹

¹Master's Program of Applied Artificial Intelligence, University of San Diego, San Diego, CA, USA.

Journal of Alzheimer's disease : JAD

|June 2, 2025

概括

大型语言模型 (LLM) 正在通过分析复杂数据来更好地识别目标和设计药物来彻底改变阿尔茨海默病 (AD) 药物发现. 这种人工智能集成有望为AD提供更有效,更精确的治疗解决方案.

科学领域:

生物医学信息学生物医学信息学
神经科学是一个神经科学.
药用化学医学化学

背景情况:

阿尔茨海默病 (AD) 给全球健康和经济带来了重大挑战.
对阿尔茨海默病的传统药物发现面临由于疾病的复杂性和多因素性质的局限性.

研究的目的:

探索大型语言模型 (LLM) 在促进AD药物发现的药物化学方面的潜力.
突出LLMs如何克服患者异质性,临床前模型和临床试验失败方面的挑战.

主要方法:

文学挖掘和综合使用LLMs大量的生物医学数据集.
整合多模式数据,用于产生假设,确定目标和新的药物设计.
评估LLM在蛋白质结构预测和ADME-Tox属性评估中的应用.

主要成果:

在AD研究中,LLM证明了增强假设生成和目标识别能力.
在 de novo 药物设计和预测药物特性 (ADME-Tox) 中,LLM 是有前途的.
案例研究说明了LLMs在提高AD药物发现效率和精度方面的作用.

结论:

通过整合人工智能和生物医学,LLMs为阿尔茨海默病研究提供了范式的转变.
尽管存在数据质量和可解释性等挑战,但LLM为创新的治疗解决方案铺平了道路.
这种人工智能驱动的方法促进跨学科的合作,以打击AD并改善患者的治疗结果.

关键词:

阿尔茨海默氏症的疾病是阿尔茨海默氏症."新药设计"的新药设计发现药物的发现.假设的产生是假设的产生.大型语言模型.药用化学药用化学目标识别目标识别

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