计算机科学
药品
可扩展性
药物重新定位
图形
人工智能
代表(政治)
机器学习
理论计算机科学
医学
药理学
政治学
数据库
政治
法学
作者
Hegang Chen,Yuyin Lu,Yuedong Yang,Yanghui Rao
标识
DOI:10.1109/tcbb.2022.3224734
摘要
Combination therapy, which can improve therapeutic efficacy and reduce side effects, plays an important role in the treatment of complex diseases. Yet, a large number of possible combinations among candidate compounds limits our ability to identify effective combinations. Though many studies have focused on predicting potential drug combinations, the existing methods are not entirely satisfactory in terms of performance and scalability. In this study, we propose a new computational pipeline, called DCMGCN, which integrates diverse drug-related information, to predict novel drug combinations. Specifically, DCMGCN first learns low-dimensional representations of drugs from the drug attributes and similarity networks. Then, by quantifying the degree of the nodes in the known drug-drug network and the similarity between connected nodes, we found the drug-drug network has heterophily and sparseness, which may limit the effectiveness of the graph convolutional network (GCN). Therefore, we introduce two designs to modify GCN. Finally, the drug representations are optimized using modified GCN (MGCN) and used to predict drug combinations. The tests on multiple drug combination datasets show that DCMGCN achieved substantial improvements over state-of-the-art methods. Importantly, our model may embed the mechanism of ground-truth drug pairs into the low-dimensional representation of each drug, which may help to further clarify the understanding of mechanisms of drug action.
科研通智能强力驱动
Strongly Powered by AbleSci AI