通过将异质信息与相互关注网络相结合,预测药物向相互作用
Yuanyuan Zhang1, Yingdong Wang1, Chaoyong Wu2
1Shanxi Key Lab for Modernization of TCVM, College of Basic Sciences, Shanxi Agricultural University, Taigu, 030801, China.
BMC bioinformatics
|November 20, 2024
概括
药物MAN是一种新的深度学习模型,通过整合多重异质网络来增强药物向相互作用的预测. 这种方法提高了适应新型药物结构的适应性,并在现实场景中表现出卓越的性能.
科学领域:
- 计算生物学是一种计算生物学.
- 药物的发现和开发.
- 生物信息学是一种生物信息学.
背景情况:
- 准确的药物向相互作用 (DTI) 预测对于药物发现至关重要.
- 现有的机器学习方法与新的化学结构作斗争.
- 利用大规模的生物和药理数据为DTI预测提供了一个有希望的途径.
研究的目的:
- 开发一个深度学习模型,DrugMAN,用于增强DTI预测.
- 使用相互关注网络 (MAN) 集成多重异质功能网络.
- 提高DTI预测模型的适应性和通用性.
主要方法:
- 药物MAN采用基于图表注意力网络的集成算法.
- 它从四个药物网络和七个基因/蛋白质网络中学习低维特征.
- 一个相互注意网络 (MAN) 捕获了药物和目标表征之间的相互作用信息.
主要成果:
- 在四种场景中,DrugMAN在DTI预测方面表现优于现有的方法 (SVM,RF,DeepPurpose,DTINet,NeoDT).
- 它表现出卓越的性能,特别是在现实世界的应用中.
- 在冷启动场景中,DrugMAN表现出最小的性能退化,表明强大的泛化能力.
结论:
- 药物MAN有效地利用异质信息来挖掘药物目标相互作用.
- 该模型显示出作为加速药物发现和重新利用的工具的巨大潜力.
- 它强大的概括能力使其适合于多样化和具有挑战性的DTI预测任务.
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