📘 DDREL: From Drug-Drug RELationships to drug repurposing
Milad Allahgholi (a), Hossein Rahmani (a), Zahra Sadeghi Adl (a) , DelaramJavdani (a), Andreas Bender (b)
a: School of Computer engineering, Iran University of Science and Technology, Tehran, 16846-13114, Iran.
b: Centre forMolecular Informatics, Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge, CB2 1EW, United Kingdom.
📗 Abstract
Analyzing the relationships among various drugs is an important issue in the field of computational biology. Different kinds of informative knowledge such as drug repurposing can be extracted from drug-drug relationships. Scientific literature represents a rich source for the retrieval of knowledge about the relationships between biological concepts, mainly drug-drug, disease-disease and drug-disease relationships. In this paper, we propose DDREL as a general purpose method that applis deep learning on scientific literature to automatically extract the graph of syntactic and semantic relationships among drugs. DDREL remarkably outperformed the existing human drug network method and random network built 100 times with respect to average similarities of drugs anatomical therapeutic chemical (ATC) codes. As an application, DDREL succeeded to discover repurposing drugs with 81% accuracy.
Keywords: Drug-Drug Relationships, Repurposing Drugs, Deep Learning, Text Mining, Word Embedding