Towards better drug repositioning using joint learning

Abstract

Drug repositioning offers an economical and efficient alternative to traditional drug discovery. It means that, a drug approved for effect against a particular disease is considered and its applications for novel pharmaceutical purposes are explored in shorter development timelines. Unlike conventional approaches, this work attempts to explore the network of existing drugs and its unmapped indications by treating drug repositioning as a classification problem. The proposed classification model attempts estimation of the relevance of a drug with an unmapped indication. An enhanced word representation model is used for this purpose by integrating knowledge obtained from a structured biological knowledge graph and medical literature. To harvest the structured biological data, we have leveraged multiple biological ontologies to achieve a formal framework in the form of a semantic knowledge graph. Our novelty lies in that we have exploited knowledge from biological knowledge graph and medical corpora to complement each other. This makes our method competent with well established drug repositioning techniques.