GraphDPA: Predicting drug-pathway associations by graph convolutional networks. (August 2022) - Zhang, Zhong-Rui; Jiang, Zhen-Ran
Abstract: Pathway-based drug discovery is a promising strategy for the discovery of drugs with low toxicity and side effects. However, identifying the associations between drug and targeting pathways is challenging for this method. The formation of various biomolecular interaction databases and the development of neural network technology provide new ways for the large-scale prediction of drug-pathway associations. This article proposes a new model called GraphDPA, which represents the drug and pathway-gene association as a graph. We use graph convolutional networks (GCN) to learn the features of the drug and pathway and predict the drug-pathway association. The results show that GraphDPA can predict drug-pathway associations with high accuracy, which verify the potential of the GCN in drug discovery. Graphical Abstract: ga1 Highlights: A new model called GraphDTA is proposed to predict potential drug-pathway associations. GraphDTA use graph convolutional networks (GCN) to learn the features of the drug and pathway. GraphDTA can be used to predict the drug-pathway association of unknown drug molecules.