DD-RDL: Drug-Disease Relation Discovery and Labeling

Abstract

Drug repurposing, which is concerned with the study of the effectiveness of existing drugs on new diseases, has been growing in importance in the last few years. One of the core methodologies for drug repurposing is text-mining, where novel biological entity relationships are extracted from existing biomedical literature and publications, whose number skyrocketed in the last couple of years. This paper proposes an NLP approach for drug-disease relation discovery and labeling (DD-RDL), which employs a series of steps to analyze a corpus of abstracts of scientific biomedical research papers. The proposed ML pipeline restructures the free text from a set of words into drug-disease pairs using state-of-the-art text mining methodologies and natural language processing tools. The model’s output is a set of extracted triplets in the form (drug, verb, disease), where each triple describes a relationship between a drug and a disease detected in the corpus. We evaluate the model based on a gold standard dataset for drug-disease relationships, and we demonstrate that it is possible to achieve similar results without requiring a large amount of annotated biological data or predefined semantic rules. Additionally, as an experimental case, we analyze the research papers published as part of the COVID-19 Open Research Dataset (CORD-19) to extract and identify relations between drugs and diseases related to the ongoing pandemic.