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?:abstract
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Objective: The COVID-19 pandemic has catalyzed a widespread effort to identify drug candidates and biological targets of relevance to SARS-COV-2 infection, which resulted in large numbers of publications on this subject We have built the COVID-19 Knowledge Extractor (COKE), a web application to extract, curate, and annotate essential drug-target relationships from the research literature on COVID-19 to assist drug repurposing efforts Materials and Methods: SciBiteAI ontological tagging of the COVID Open Research Dataset (CORD-19), a repository of COVID-19 scientific publications, was employed to identify drug-target relationships Entity identifiers were resolved through lookup routines using UniProt and DrugBank A custom algorithm was used to identify co-occurrences of protein and drug terms, and confidence scores were calculated for each entity pair Results: COKE processing of the current CORD-19 database identified about 3,000 drug-protein pairs, including 29 unique proteins and 500 investigational, experimental, and approved drugs Some of these drugs are presently undergoing clinical trials for COVID-19 Discussion: The rapidly evolving situation concerning the COVID-19 pandemic has resulted in a dramatic growth of publications on this subject in a short period These circumstances call for methods that can condense the literature into the key concepts and relationships necessary for insights into SARS-CoV-2 drug repurposing Conclusion: The COKE repository and web application deliver key drug - target protein relationships to researchers studying SARS-CoV-2 COKE portal may provide comprehensive and critical information on studies concerning drug repurposing against COVID-19 COKE is freely available at https://coke mml unc edu/ and the code is available at https://github com/DnlRKorn/CoKE
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