{"doi":"10.1021/acs.jcim.1c01285","title":"COVID-19 Knowledge Extractor (COKE): A Curated Repository of Drug–Target Associations Extracted from the CORD-19 Corpus of Scientific Publications on COVID-19","abstract":"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.","journal":"Journal of Chemical Information and Modeling","year":2021,"id":203923,"datarank":0.37047765978846375,"base_score":1.791759469228055,"endowment":1.791759469228055,"self_citation_contribution":0.26876392038420827,"citation_network_contribution":0.10171373940425549,"self_endowment_contribution":0.26876392038420827,"citer_contribution":0.10171373940425549,"corpus_percentile":51.57422449137464,"corpus_rank":6261,"citation_count":5,"citer_count":3,"citers_with_citation_signal":3,"citers_with_endowment":3,"datacite_reuse_total":0,"is_dataset":true,"is_dataset_confidence":0.9309,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":572351,"name":"Vera Pervitsky","orcid":"0000-0002-1939-4420","position":1,"is_corresponding":false},{"id":268255,"name":"Tesia Bobrowski","orcid":"0000-0002-6177-374X","position":2,"is_corresponding":false},{"id":12568,"name":"Vinícius M. Alves","orcid":"0000-0002-6182-1748","position":3,"is_corresponding":false},{"id":292564,"name":"Charles Schmitt","orcid":"0000-0002-3148-2263","position":4,"is_corresponding":false},{"id":6475,"name":"Chris Bizon","orcid":"0000-0002-9491-7674","position":5,"is_corresponding":false},{"id":272340,"name":"Nancy Baker","orcid":"0000-0002-8351-9435","position":6,"is_corresponding":false},{"id":398296,"name":"Rada Chirkova","orcid":"0000-0003-4249-9690","position":7,"is_corresponding":false},{"id":38418,"name":"Artem Cherkasov","orcid":"0000-0002-1599-1439","position":8,"is_corresponding":false},{"id":12435,"name":"Eugene Muratov","orcid":"0000-0003-4616-7036","position":9,"is_corresponding":false},{"id":108077,"name":"Alexander Tropsha","orcid":"0000-0003-3802-8896","position":10,"is_corresponding":false},{"id":292562,"name":"Daniel Korn","orcid":"0000-0002-1780-9872","position":0,"is_corresponding":true}],"reference_count":42,"raw_metadata":null,"created_at":"2026-07-18T23:51:22.166488Z","pmid":"34783553","pmcid":"PMC13138251","fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}