Generating Knowledge Graph Based Explanations for Drug Repurposing Predictions

Over the past years, computer assisted drug repurposing methods have started to gain more attention as they offer a faster and a more effective way to treat many diseases. While these methods are quite promising in terms of power of prediction, the hesitation regarding the use of these methods in practice still remains due to their highly complex working mechanisms, which limits their interpretability. Explainable Artificial Intelligence (XAI), which takes transparency, interpretability, informativeness as its main foundations, could address the limitations of the black-box models. In this context, Knowledge Graphs (KGs) could leverage the explanations provided to the user in the biomedical domain, as they are capable of represent relations between the entities in a semantically consistent way. Knowledge Graphs have the potential to generate graph-based representations, while providing the context, which make it easily interpretable by humans. In this paper, we propose an approach, which is a KG based explainable AI framework in the field of drug repurposing as an extension of the PREDICT Method. The approach is centered on generating similarity-based explanations by extracting the relevant paths from the input, which consists of a disease and a predicted drug for the treatment of the disease. To demonstrate the utility of this approach, we demonstrate how the graphical operations used in the KG could be used to generate plausible explanations, by conducting a use case on Alzheimer Disease. Our findings suggest that the utilization of biomedical KGs and this approach has a great potential to provide transparent explanations as it is able to illustrate the relations between drug, disease entities which are quite relevant to the target input. Application of this approach to the drug repurposing and to other similar domains, could be helpful to overcome the limitations caused by the black-box nature of the computational drug repurposing models and could be a powerful tool to enhance the understanding of decision making process of models and simplify scientific communication among domain experts and computer scientists.