A semantic knowledge-base approach to drug-drug interaction discovery

Ignacio Tripodi, Kevin Bretonnel Cohen, Lawrence E. Hunter

Abstract: We propose a novel, semantic-reasoning-based approach to look for potentially adverse drug-drug interactions (DDIs) by using a knowledge-base of biomedical public ontologies and datasets in a semantic graph representation. This approach makes it possible to find previously unknown relations between different biological entities like drugs, proteins and biological processes, and perform inferences on those relations. Finding nodes that represent drugs in this semantic graph, and intersecting pathways between these nodes (e.g. intersecting at a metabolic pathway step described in Reactome [1] data), can yield to novel drug-drug interactions. The resulting pathways not only describe drug-drug interactions reflected in the literature, but also unstudied interactions that could elucidate reported adverse effects.