COVID-19 Knowledge Extractor (COKE): A Tool and a Web Portal to Extract Drug - Target Protein Associations from the CORD-19 Corpus of Scientific Publications on COVID-19

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 <a href="https://coke.mml.unc.edu/">https://coke.mml.unc.edu/</a> and the code is available at <a href="https://github.com/DnlRKorn/CoKE">https://github.com/DnlRKorn/CoKE</a>..

Medienart:

Preprint

Erscheinungsjahr:

2021

Erschienen:

2021

Enthalten in:

chemRxiv.org - (2021) vom: 18. Nov. Zur Gesamtaufnahme - year:2021

Sprache:

Englisch

Beteiligte Personen:

Korn, Daniel [VerfasserIn]
Pervitsky, Vera [VerfasserIn]
Bobrowski, Tesia [VerfasserIn]
Alves, Vinicius [VerfasserIn]
Schmitt, Charles [VerfasserIn]
Bizon, Cristopher [VerfasserIn]
Baker, Nancy [VerfasserIn]
Chirkova, Rada [VerfasserIn]
Cherkasov, Artem [VerfasserIn]
Muratov, Eugene [VerfasserIn]
Tropsha, Alexander [VerfasserIn]

Links:

Volltext [kostenfrei]

Themen:

540
Chemistry

doi:

10.26434/chemrxiv.13289222

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

XCH019420102