Discovering drug interactions: a text-mining and reasoning ap- proach based on properties of drug metabolism

Motivation: Identifying drug-drug interactions is a critical process in drug administration and drug development. Clinical support tools often provide comprehensive lists of drug-drug interactions, but they usually lack the supporting scientific evidences and different tools can return inconsistent results. In this paper, we propose a novel approach that integrates text mining and automated reasoning to derive drug-drug interactions. Through the extraction of various facts of drug metabolism, not only the drug-drug interactions that are explicitly mentioned in text can be extracted but also the potential interactions that can be inferred by reasoning. Results: Our approach was able to find several potential drug-drug interactions that are not present in DrugBank. We manually evaluated these interactions based on their supporting evidences, and our analysis revealed that 81.3% of these interactions are determined to be correct. This suggests that our approach can uncover potential drug-drug interactions with scientific evidences explaining the mechanism of the interactions. Contact: Luis Tari ([email protected])