2021 journal article
COVID-19 Knowledge Extractor (COKE): A Curated Repository of Drug-Target Associations Extracted from the CORD-19 Corpus of Scientific Publications on COVID-19
JOURNAL OF CHEMICAL INFORMATION AND MODELING, 61(12), 5734–5741.
MeSH headings : Antiviral Agents; COVID-19; Drug Repositioning; Humans; Pandemics; Pharmaceutical Preparations; SARS-CoV-2
TL;DR:
The CO VID-19 Knowledge Extractor (COKE), a web application to extract, curate, and annotate essential drug–target relationships from the research literature on COVID-19, can serve as a useful resource for drug repurposing against SARS-CoV-2.
(via Semantic Scholar)
www.ncbi.nlm.nih.gov (repository)
UN Sustainable Development Goal Categories
3. Good Health and Well-being
(Web of Science; OpenAlex)
Source: Web Of Science
Added: February 28, 2022
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. 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.
