Identifying Drug - Disease Interactions Through Link Prediction in Heterogeneous Graphs

Unlike traditional development of new drugs that rely on labor- and time-intensive research and clinical trials, computational approaches, deep learning technologies, in particular, have been prominent in recent research on the topic. By utilizing the ever-growing biomedical knowledge repositories and exploiting the relationship between diverse types of information (e.g., proteins, genes, molecular, diseases, drugs), graph neural networks (GNNs) primed for processing graph-structured data have a real potential for advancing the critical endeavor of drug discovery. Safe and effective drug therapy would also rely on early identification of unwanted and potentially harmful adverse effects a certain drug has on patient’s health. Hence, two, rather contrastive tasks that pertain to the process of drug discovery have been of special interest in this research. The first one is drug repurposing and the second one, a closely-related task of identifying drugs that have an adverse or negative effect on patient health namely drug-induced diseases. In this research, the task of discovering new links between drugs and diseases has been formalized as a link prediction task in a heterogenous graph. The predictive models for drug discovery proposed in this paper were tested on the ogbl-biokg ( https://ogb.stanford.edu/docs/linkprop/#ogbl-biokg ) dataset from the collection of large benchmark dataset Open Graph Benchmark (OGB) [15]. The openness and multi-source heterogeneity of the OGB dataset has provided us with an opportunity to experiment with HinSage [28], a method for inductive representational learning in heterogenous graphs. Two models based on HinSage, have been proposed proving their superior performance when compared with more traditional similarity-based baseline methods. Furthermore, a selected newly discovered relationship with a potential for drug repurposing has been discussed through the lenses of related clinical-experimental trials.