提高草药与药物相互作用的预测,使用大型语言模型
IEEE journal of biomedical and health informatics
|April 7, 2025
概括
我们使用大语言模型 (LLM) 和图形自编码器开发了一种新的草药药物相互作用 (HDI) 预测模型. 这种方法通过提高药物和草药之间的相互作用预测的准确性来增强精确医学.
科学领域:
- 药理学 药理学是指药理学的学科.
- 生物信息学是一种生物信息学.
- 人工智能的人工智能
背景情况:
- 药物-草药相互作用对于优化治疗和推进个性化医学的发展至关重要.
- 用于交互预测的深度学习模型面临着数据质量和分布的挑战.
- 大型语言模型 (LLM) 由于其广泛的知识基础,提供了强大的解决方案.
研究的目的:
- 为预测草药与药物相互作用 (HDIs) 提出一个综合模型.
- 利用LLM,一次性编码和变量图形自编码器 (VGAEs) 来提高预测准确性和可解释性.
- 在现有的交互预测方法中解决数据质量和分布问题.
主要方法:
- 利用LLM从药物SMILES字符串中提取高质量的分子特征.
- 应用一热编码来表示多组分草药,提高模型的可解释性.
- 使用VGAEs重建草药与药物相互作用图表并预测未知的相互作用.
- 整合了节点度差异化,以减轻VGAE消息传递中高度节点的偏差.
主要成果:
- 提出的模型在预测草药与药物相互作用方面表现出显著的表现.
- 实验验证了个别组件的有效性,包括LLM特征提取和VGAE图形重建.
- 该方法成功地解决了与数据质量和分布不均相关的挑战.
- 对于高度节点主导的缓解策略在提高预测稳定性方面被证明是有效的.
结论:
- 集成的LLM-VGAE模型为预测草药与药物相互作用提供了一种强大的新方法.
- 这种方法具有优化传统中医药配方和帮助新药开发的巨大潜力.
- 这些发现支持通过更准确的相互作用预测来推进精准医学.
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