Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing
This work addresses the challenge of predicting rare interactions in heterogeneous graphs, specifically for drug discovery in novel diseases like Covid-19, representing an incremental improvement over existing methods.
The paper tackles the problem of few-shot link prediction for rare link types, such as drug repurposing for Covid-19, by proposing an inductive RGCN that learns informative relation embeddings, significantly outperforming RGCN and state-of-the-art KGE models in few-shot tasks and identifying several drugs used in clinical trials as candidates.
Predicting interactions among heterogenous graph structured data has numerous applications such as knowledge graph completion, recommendation systems and drug discovery. Often times, the links to be predicted belong to rare types such as the case in repurposing drugs for novel diseases. This motivates the task of few-shot link prediction. Typically, GCNs are ill-equipped in learning such rare link types since the relation embedding is not learned in an inductive fashion. This paper proposes an inductive RGCN for learning informative relation embeddings even in the few-shot learning regime. The proposed inductive model significantly outperforms the RGCN and state-of-the-art KGE models in few-shot learning tasks. Furthermore, we apply our method on the drug-repurposing knowledge graph (DRKG) for discovering drugs for Covid-19. We pose the drug discovery task as link prediction and learn embeddings for the biological entities that partake in the DRKG. Our initial results corroborate that several drugs used in clinical trials were identified as possible drug candidates. The method in this paper are implemented using the efficient deep graph learning (DGL)