These entries have been added as a result of routine curation efforts, including those for both new drugs and updates to existing drugs. Additionally, the DrugBank team has built several natural language processing (NLP) models capable of scraping PubMed abstracts to identify potential drug–entities and drug–target relationships. These NLP models are integrated into curator workflows in a way that increases the speed with which curators can review predicted and established relationships as well as the associated reference(s).
