Drug Target Discovery Using Knowledge Graph Embeddings

The field of drug discovery has entered a plateau stage lately. It is increasingly more expensive and time-demanding to introduce new drugs into the market. One of the main reasons is the slow progress in finding novel targets for drug candidates and the lack of insight in terms of the associated mechanisms of action. Current works in this area mainly utilise different chemical, genetic and proteomic methods, which are limited in terms of the scalability of experimentation and the scope of studied drugs and targets per experiment. This is mainly due to their dependency on laboratory experiments and available physical resource. This has led to an increasing importance of computational methods for the identification of candidate drug targets. In this work, we introduce a novel computational approach for predicting drug target proteins. We approach the problem as a link prediction task on knowledge graphs. We process drug and target information as a knowledge graph of interconnected drugs, proteins, disease, pathways and other relevant entities. We then apply knowledge graph embedding (KGE) models over this data to enable scoring drug-target associations, where we employ a customised version of state-of-the-art KGE model ComplEx. We generate a benchmarking dataset based on KEGG database to train and evaluate our method. Our experiments show that our method achieves best results in comparison to other traditional KGE models. Specifically, the method predicts drug target links with mean reciprocal rank (MRR) of 0.78 and Hits@10 of 0.88. This provides a promising basis for further experimentation and comparisons with domain-specific predictive models.