COVID-19 Knowledge Extractor (COKE): A Curated Repository of Drug–Target Associations Extracted from the CORD-19 Corpus of Scientific Publications on COVID-19
Daniel Korn, Vera Pervitsky, Tesia Bobrowski, Vinícius M. Alves, Charles Schmitt, Chris Bizon, Nancy Baker, Rada Y. Chirkova, Artem R Cherkasov, Eugene N. Muratov, Alexander Tropsha · Journal of Chemical Information and Modeling · 2021
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 CO VID-19 K nowledge E xtractor (COKE), a web application to extract, curate, and annotate essential drug–target relationships from the research literature on COVID-19. SciBiteAI ontological tagging of the COVID Open Research Data set (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 the target protein and drug terms, and confidence scores were calculated for each entity pair. COKE processing of the current CORD-19 database identified about 3000 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. The COKE repository and web application can serve as a useful resource for drug repurposing against SARS-CoV-2. COKE is freely available at https://coke.mml.unc.edu/, and the code is available at https://github.com/DnlRKorn/CoKE .