一种以知识为导向的图形学习方法,将表型和基于目标的药物发现联系起来
Qing Ye1,2, Yundian Zeng1,2, Linlong Jiang2
1College of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, 310027, China.
Advanced science (Weinheim, Baden-Wurttemberg, Germany)
|March 6, 2025
概括
这项研究介绍了知识引导药物关系预测器 (KGDRP),这是整合多种生物医学数据以加速治疗分子发现的新方法. KGDRP显著提高了药物查和目标优先级,提高了药物发现效率.
科学领域:
- 计算生物学是一种计算生物学.
- 药物发现 药物发现
- 生物信息学是一种生物信息学.
背景情况:
- 整合基于表型的药物发现 (PDD) 和基于目标的药物发现 (TDD) 是至关重要的,但由于生物医学数据的复杂性而具有挑战性.
- 现有的方法与数据异质性,噪声和偏差作斗争,阻碍了有效的治疗分子发现.
研究的目的:
- 开发一种强大的图形表示学习方法,用于整合多式联络生物医学数据.
- 提高药物发现过程的效率和准确性,包括查和目标识别.
主要方法:
- 开发了以知识为导向的药物关系预测器 (KGDRP),是一种图形表示学习模型.
- 将综合多式数据 (网络,基因表达,化学结构) 整合到一个异质图 (HG) 结构中.
- 使用基于异质图神经网络 (HGNN) 的架构,结合了生物医学HG (BioHG).
主要成果:
- 与以前的方法相比,KGDRP在现实世界药物查场景中取得了12%的改进.
- 来自KGDRP的生物知情表现提高了26%的药物标优先级.
- 使用零射击评估识别COVID-19潜在药物的成功率很高,并通过细胞向药物相互作用分析阐明了药物机制.
结论:
- KGDRP提供了一个强大的基础设施,用于无的多式联络数据和生物医学网络集成.
- 该方法有效地加速基于表型的药物发现 (PDD) 并指导治疗点的发现.
- KGDRP加速了对新型治疗分子的整体发现.
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