药物数据库
计算机科学
交互网络
图形
异构网络
图嵌入
机器学习
注意力网络
人工智能
分类器(UML)
化学信息学
数据挖掘
理论计算机科学
生物信息学
药品
医学
生物
基因
无线网络
精神科
电信
化学
无线
生物化学
作者
Xiaohan Qu,Guoxia Du,Jing Hu,Yongming Cai
出处
期刊:Current Computer - Aided Drug Design
[Bentham Science Publishers]
日期:2023-07-14
卷期号:20 (6): 1013-1024
被引量:5
标识
DOI:10.2174/1573409919666230713142255
摘要
In this study, we aimed to develop a new end-to-end learning model called Graph-Drug-Target Interaction (DTI), which integrates various types of information in the heterogeneous network data, and to explore automatic learning of the topology-maintaining representations of drugs and targets, thereby effectively contributing to the prediction of DTI. Precise predictions of DTI can guide drug discovery and development. Most machine learning algorithms integrate multiple data sources and combine them with common embedding methods. However, the relationship between the drugs and target proteins is not well reported. Although some existing studies have used heterogeneous network graphs for DTI prediction, there are many limitations in the neighborhood information between the nodes in the heterogeneous network graphs. We studied the drug-drug interaction (DDI) and DTI from DrugBank Version 3.0, protein-protein interaction (PPI) from the human protein reference database Release 9, drug structure similarity from Morgan fingerprints of radius 2 and calculated by RDKit, and protein sequence similarity from Smith-Waterman score.
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