Mining biomedical literature and ontologies for drug repositioning discovery

Abstract

Drug development is time-consuming, costly, and risky. Approximate 80% to 90% of drug development projects fail before they ever get into clinical trials. To reduce the high risk of failure for drug development, pharmaceutical companies are exploring the drug repositioning approach for drug development. Previous studies have shown the feasibility of using computational methods to help extract plausible drug repositioning candidates, but they all encountered some limitations. In this study, we propose a novel drug-repositioning discovery method that takes into account multiple information sources, including more than 18,000,000 biomedical research articles and some existing ontologies that cover detailed relations between drugs, proteins and diseases. We design two experiments to evaluate our proposed drug repositioning discovery method. Overall, our evaluation results demonstrate the capability and superiority of our proposed drug repositioning method for discovering potential, novel drug-disease relationships. © 2014 Springer International Publishing.