Knowledge-Graph-Based Drug Repositioning against COVID-19 by Graph Convolutional Network with Attention Mechanism

Abstract

The current global crisis caused by COVID-19 almost halted normal life in most parts of the world. Due to the long development cycle for new drugs, drug repositioning becomes an effective method of screening drugs for COVID-19. To find suitable drugs for COVID-19, we add COVID-19-related information into our medical knowledge graph and utilize a knowledge-graph-based drug repositioning method to screen potential therapeutic drugs for COVID-19. Specific steps are as follows. Firstly, the information about COVID-19 is collected from the latest published literature, and gene targets of COVID-19 are added to the knowledge graph. Then, the information of COVID-19 of the knowledge graph is extracted and a drug–disease interaction prediction model based on Graph Convolutional Network with Attention (Att-GCN) is established. Att-GCN is used to extract features from the knowledge graph and the prediction matrix reconstructed through matrix operation. We evaluate the model by predicting drugs for both ordinary diseases and COVID-19. The model can achieve area under curve (AUC) of 0.954 and area under the precise recall area curve (AUPR) of 0.851 for ordinary diseases. On the drug repositioning experiment for COVID-19, five drugs predicted by the models have proved effective in clinical treatment. The experimental results confirm that the model can predict drug–disease interaction effectively for both normal diseases and COVID-19.

The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

“…In predicting drug likeliness, drug target relationship, and generation of novel molecules against the desired target, autoencoder approaches help. More DL like examples is Graph Convolutional Network with Attentional mechanism for Drug-Disease Interaction (Att-GCN-DDI) as in Che et al (2021), and Molecule Transformer-Drug Target Interaction (MT-DTI) to predict any commercially available antiviral drugs that could be effective against SARS-CoV-2 (Beck et al 2020).…”

Application of artificial intelligence and machine learning for COVID-19 drug discovery and vaccine design

“…To screen potential therapeutic drugs for COVID-19, Che et al . [ 32 ] embedded five types of entities, including drugs, genes, diseases, channels, side effects and nine relationships into medical knowledge graph. Moreover, the authors used graph convolutional networks (GCNs) with attentional mechanism to extract features from the knowledge graph and construct a prediction matrix.…”

GraphMS: Drug Target Prediction Using Graph Representation Learning with Substructures

“…Gao et al [20] and Duvenaud et al [21] proposed graph convolutional networks with attention mechanisms to model chemical structures and demonstrated good interpretability. Che et al [22] developed Att-GCN to predict drugs for both ordinary diseases and COVID-19.…”