预测药物不良反应的组合药物治疗与交叉规模的关联学习通过注意力模块
Boyang Li1,2,3, Yifan Qi4, Bo Li5,6
1School of Interdisciplinary Science, Beijing Institute of Technology, Beijing, China.
Nature computational science
|June 30, 2025
概括
机器ADR可以从药物组合中预测器官水平的不良药物反应 (ADR). 这种可解释的模型整合了分子和网络数据,以提高精准医学中的药物安全性.
科学领域:
- 计算生物学是一种计算生物学.
- 药物基因组学 药物基因组学
- 生物信息学是一种生物信息学.
背景情况:
- 组合药物疗法提供了好处,但增加了药物不良反应 (ADR) 的风险.
- 使用可解释的计算方法准确预测副作用对于药物开发和临床精密医学至关重要.
- 目前的深度学习模型在阐明器官特异性蛋白质-蛋白质相互作用和ADR关联方面面临着挑战.
研究的目的:
- 开发一种协会式学习增强模型,OrganADR,用于预测组合药物治疗中器官级别的ADR.
- 整合多层次的生物医学数据,包括器官级别的副作用,分子药物信息和网络知识.
- 从器官的角度提供对ADR机制的可解释的见解.
主要方法:
- OrganADR利用关联式学习来增强预测.
- 该模型整合了分子水平的药物信息和基于网络的生物医学知识.
- 它采用多个可解释模块,以在器官层面上表示ADR协会.
主要成果:
- 在15个器官中,OrganADR实现了最先进的性能.
- 该模型在器官层面提供可解释的见解.
- 它提供了对ADRs的基于网络的观点.
结论:
- OrganADR是跨度生物医学信息整合的有效工具.
- 它可以预测组合药物治疗的器官级别的副作用.
- 该模型有助于在临床精密医学环境中预防副作用.
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