Explainable Drug Repurposing Approach From Biased Random Walks

Drug repurposing is a highly active research area, aiming at finding novel uses for drugs that have been previously developed for other therapeutic purposes. Despite the flourishing of methodologies, success is still partial, and different approaches offer, each, peculiar advantages. In this composite landscape, we present a novel methodology focusing on an efficient mathematical procedure based on gene similarity scores and biased random walks which rely on robust drug-gene-disease association data sets. The recommendation mechanism is further unveiled by means of the Markov chain underlying the random walk process, hence providing explainability about how findings are suggested. Performances evaluation and the analysis of a case study on rheumatoid arthritis show that our approach is accurate in providing useful recommendations and is computationally efficient, compared to the state of the art of drug repurposing approaches.

Automated summary

Drug repurposing is an active research area aiming to find new uses for drugs developed for other purposes. This study proposes a novel methodology based on gene similarity scores and biased random walks using robust drug-gene-disease association data sets. The recommendation mechanism is explained through Markov chain analysis, providing explainability for the suggested findings. Evaluation and a case study on rheumatoid arthritis demonstrate the accuracy and computational efficiency of our approach compared to existing drug repurposing methods.