DHGT-DTI:通过使用GraphSAGE和图形转换器的双视图异构网络,推进药物向相互作用预测
Mengdi Wang1, Xiujuan Lei1, Ling Guo2
1School of Computer Science, Shaanxi Normal University, Xi'an, 710119, China.
Journal of pharmaceutical analysis
|November 17, 2025
概括
DHGT-DTI是一种新的深度学习方法,通过整合本地和全球网络数据来增强药物向相互作用的预测. 这种方法通过准确识别潜在的药物标关系来改善药物发现.
科学领域:
- 计算生物学是一种计算生物学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 预测药物向相互作用 (DTI) 对药物发现至关重要.
- 现有的方法难以有效地整合本地和全球网络信息.
研究的目的:
- 提出DHGT-DTI,一种用于增强DTI预测的新型深度学习方法.
- 为了提高准确性,全面整合本地和全球网络信息.
主要方法:
- 使用异质图形神经网络 (HGNN) 来进行局部结构学习.
- 雇佣了一个图形变换器,专注于基于元路径的全球结构学习.
- 从双重视角使用矩阵分解和重建的辅助网络来整合特征.
主要成果:
- 与对基准数据集的现有方法相比,DHGT-DTI表现优越.
- 对帕金森病药物的案例研究证实了该方法的实际实用性和潜力.
结论:
- DHGT-DTI为准确的DTI预测提供了一个强大的新工具.
- 该方法对加速各种疾病的药物发现有希望.
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