当东方与西方相遇:跨域药物相互作用注释与大型语言模型和双向神经网络
IEEE journal of biomedical and health informatics
|April 22, 2025
概括
本研究介绍了TEmbed-DDI,一个使用大型语言模型嵌入的新框架,以改善药物相互作用 (DDI) 注释. 它准确地预测了已知的和未知的相互作用,包括传统中国医学 (TCM) 中的相互作用.
科学领域:
- 药理学和计算机生物学
- 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:
- 药物组合疗法对复杂疾病具有前景,但存在药物相互作用 (DDI) 的风险.
- 当前的DDI注释方法往往忽略了药物上下文信息,缺乏可扩展性.
- 现有的方法主要依赖于原子级特征,限制了它们的有效性.
研究的目的:
- 开发一个新的框架,TEmbed-DDI,用于使用上下文表示和大型语言模型 (LLM) 嵌入的增强DDI注释.
- 通过结合丰富的语义和上下文特征来提高DDI预测的准确性和可扩展性.
- 将传统中医 (TCM) 整合到DDI注释任务中.
主要方法:
- 利用从医疗资源中获取的有意义的上下文文本来丰富每个药物的语义特征.
- 使用预先训练的LLM嵌入来捕捉来自远程上下文表示的复杂特征.
- 整合双向学习神经网络,用于西方和TCM DDI注释.
主要成果:
- 在西方DDI基准中,TEmbed-DDI实现了最先进的性能,AUC分数为0.992 (CHCH) 和0.95 (DEEP).
- 该框架在新的TCM DDI基准上表现出强烈的概括性,达到0.956.95的AUC.
- 案例研究证实TEmbed-DDI能够识别以前未知的药物相互作用.
结论:
- TEmbed-DDI代表了DDI注释的重大进步,提供了高精度和广泛的适用性.
- 该框架的LLM嵌入和上下文信息的整合增强了对西方和TCM的DDI预测.
- TEmbed-DDI具有促进药物发现和支持跨领域医学研究的潜力,通过使新药组合的注释成为可能.
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