对大型语言模型进行比较评估,以检索已知药物组合并预测新药组合
Evan J Wang1,2, Çerağ Oğuztüzün1, Rong Xu1
1Center for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
Journal of Alzheimer's disease : JAD
|January 20, 2026
概括
大型语言模型 (LLM) 在识别阿尔茨海默氏症 (AD) 等疾病的药物组合方面表现有前途. 虽然验证至关重要,但GPT-5的准确性很高,这表明有可能发现新的治疗策略.
科学领域:
- 生物医学信息学 生物医学信息学
- 人工智能在医学中的应用
- 药物发现 药物发现 药物发现
背景情况:
- 大型语言模型 (LLM) 是生物医学研究中用于信息处理的新兴工具.
- 它们在识别疾病的药物组合中的应用是一个尚未探索的领域.
- 阿尔茨海默病 (AD) 是一个重大挑战,需要新的治疗方法.
研究的目的:
- 评估LLM在检索已知的药物组合中的有效性.
- 确定用于阿尔茨海默病 (AD) 治疗的新型药物组合.
- 评估不同LLM在这个任务中的表现.
主要方法:
- 开发特定提示,以指导LLMs在药物组合检索.
- 使用FDA批准的组合和文献挖掘进行性能评估.
- 对AD新型候选物鉴定的评估,包括途径丰富分析.
主要成果:
- 在识别FDA批准的药物组合时,GPT-5表现出卓越的性能,精度为0.95和F1得分.
- GPT-5建议用于AD的已知和潜在药物组合,包括FDA批准的一种以及试验或文献中的其他药物组合.
- 确定了10种针对关键AD途径的非标签组合.
结论:
- 在检索药物组合时,LLM是有效的,性能因模型而异;GPT-5表现最好.
- 由LLM生成的药物组合建议需要进一步的实验验证.
- 这种方法有可能加速药物发现,并确定AD的新治疗策略.
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