赋权图 神经网络基于计算的药物重新定位与大语言模型推断的知识表示
Yaowen Gu1, Zidu Xu2, Carl Yang3
1Department of Chemistry, New York University, New York, NY, 10003, USA.
Interdisciplinary sciences, computational life sciences
|September 26, 2024
概括
大型语言模型 (LLM) 通过整合生物医学知识来增强药物重新定位,改善药物-疾病关联预测. 拟议的LLM-DDA模型显著优于现有方法,展示了LLM.
科学领域:
- 计算生物学是一种计算生物学.
- 医学中的人工智能.
- 药理学 药理学是指药理学的学科.
背景情况:
- 药物重新定位旨在确定现有药物的新治疗用途.
- 目前用于药物疾病关联 (DDA) 预测的图形神经网络 (GNN) 方法严重依赖网络拓学,网络拓学可能不完整或杂.
- 这些GNN方法往往忽视了对准确预测至关重要的丰富的生物医学知识.
研究的目的:
- 调查大型语言模型 (LLM) 在生成用于药物重新定位和DDA预测的知识表示中的有效性.
- 开发和评估新的计算模型,将LLM生成的知识集成到DDA预测框架中.
- 确定合并基于LLM的嵌入式与现有网络数据的最佳方法,以改进DDA预测.
主要方法:
- 使用LLM的零射击提示模板提取药物和疾病实体的高质量知识描述.
- 从LLM衍生文本描述中生成连续的数值表示 (嵌入式).
- 拟议的LLM-DDA,一个包含LLM嵌入的模型,有三个架构 (LLM-DDANode Feat,LLM-DDA双GN,LLM-DDAGNN-AE) 探索融合策略.
- 对四个DDA基准进行了广泛的实验,并将性能与11种基线方法进行了比较.
主要成果:
- 该LLM-DDAGNN-AE架构实现了最佳性能,超过了11个基准方法的性能.
- 显示了显著的相对改善:精度回忆曲线 (AUPR) 下面面积为23.22%,F1-Score为17.20%,精度为25.35%.
- 案例研究,包括对过敏性鼻炎的普雷尼松,验证了该模型能够识别可靠的DDA和由文献支持的知识描述的能力.
结论:
- 通过提供丰富,综合的生物医学知识表示,LLM显著提高了药物重新定位.
- 拟议的LLM-DDAGNN-AE模型代表了用于DDA预测的最先进的方法.
- 在药物重新定位之外,LLM为各种生物医学关系预测任务提供了广泛的实用性和适用性.
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