Self-Supervised Learning with Heterogeneous Graph Neural Network for COVID-19 Drug Recommendation

ABSTRACT

The emergence and spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have created an enormous socioeconomic impact. Although there are several promising drug candidates in clinical trials, none of them are approved yet. Thus, the drug repositioning approach may help to overcome the current pandemic. However, the sparse dataset of COVID-19 limits the accuracy of existing drug repositioning. To overcome this problem, we propose a novel drug repositioning framework (named Drug2Cov). Drug2Cov can learn an effective representation via integrating self-supervised learning with sparse data. Meanwhile, Drug2Cov uses a heterogeneous graph neural network to capture the complex interaction between viruses, targets, and drugs that enhance the accuracy of drug repositioning. The experimental results demonstrate the effectiveness and feasibility of our proposed Drug2Cov framework. Source code and dataset are freely available at https//github.com/lhf3291109/Drug2Cov. © 2021 IEEE.