AGRL-DSE:适应式图表表示学习在异质图表上的药物副作用预测
He Tan1, Xiangmin Ji1,2, Chen-Zhen Xu3
1School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China.
ACS omega
|September 8, 2025
概括
这项研究引入了AGRL-DSE,这是通过增强图形表示学习来预测药物副作用的新框架. 它提高了已知和未知的药物副作用预测的准确性.
科学领域:
- 计算生物学是一种计算生物学.
- 药理学 药理学是指药理学的学科.
- 人工智能的人工智能是人工智能.
背景情况:
- 准确识别药物的副作用对于药物开发和安全监测至关重要.
- 图形神经网络 (GNN) 显示出希望,但与复杂的异质网络和过度平滑作斗争.
- 现有的方法往往无法在复杂的网络结构中完全捕获语义信息.
研究的目的:
- 提出AGRL-DSE,一个适应性图表表示学习框架,用于在药物副作用预测中增强节点特征学习.
- 针对异质网络的现有基于GNN的方法的局限性.
- 提高计算药物副作用预测模型的准确性和通用性.
主要方法:
- 构建了一个异质图形,用于药物和副作用的内层和间层连接.
- 集成图形卷积网络 (GCN),GraphSAGE和图形注意网络 (GAT) 模块在不同的图形层面.
- 引入了适应性层注意力机制,用于动态融合多层次特征.
主要成果:
- 与最先进的模型相比,AGRL-DSE在预测药物副作用方面表现优越.
- 在热启动和冷启动预测场景中实现了高精度.
- 适应层注意力机制有效地融合了多层次的特征,以提高预测.
结论:
- AGRL-DSE为药物副作用预测提供了一个强大的和可通用的方法.
- 该框架捕捉复杂关系的能力可以显著影响药物评估和开发.
- 这种方法具有提高患者安全和优化药物发现过程的潜力.
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