通过环境学习和使用大型语言模型进行判断来改善药物相互作用预测
He Qi1,2, Xiaoqiang Li3, Chengcheng Zhang4
1School of Medicine and Health, Harbin Institute of Technology, Harbin, China.
Frontiers in pharmacology
|June 18, 2025
概括
本研究介绍了DDI-JUDGE,这是一种用于预测药物相互作用的新型大语言模型 (LLM) 方法. DDI-JUDGE显著提高了DDI预测的准确性,推进了药物发现应用.
科学领域:
- 人工智能的人工智能
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
背景情况:
- 大型语言模型 (LLM) 在各种领域显示出希望,但它们在药物发现中的应用,特别是用于药物相互作用 (DDI) 预测,需要进一步探索.
- 准确的DDI预测对于药物安全性和有效性至关重要.
研究的目的:
- 开发和评估一种新的基于LLM的方法,DDI-JUDGE,用于增强药物相互作用 (DDI) 预测.
- 为了证明基于LLM的方法在DDI预测任务中优于现有的方法.
主要方法:
- 拟议的DDI-JUDGE,将判断和上下文学习 (ICL) 提示用于DDI预测.
- 引入了一种新的ICL提示范式,使用高相似性样本进行正负提示.
- 开发了一个基于ICL的提示模板结构输入,任务,因素和示例.
- 利用GPT-4作为一个区分器来改进来自多个LLM的预测.
主要成果:
- 在零射击 (AUC: 0.642,AUPR: 0.629) 和少射击 (AUC: 0.788,AUPR: 0.801) 设置中,DDI-JUDGE实现了最先进的性能.
- 与现有的LLM方法相比,具有卓越的预测能力和稳定性.
- 验证了ICL提示范式和提示模板的有效性.
结论:
- 临床医学具有促进药物发现的巨大潜力,特别是提高DDI预测准确度.
- DDI-JUDGE框架,凭借其模块化提示结构和整体推理,为知识密集型生物医学应用提供了一个可扩展的解决方案.
- 开发的方法和代码是公开的,以促进进一步的研究.
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