ArticleGenes & genomics2021
Screening druggable targets and predicting therapeutic drugs for COVID-19 via integrated bioinformatics analysis.
Article in Genes & genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed, 5 citations in OpenAlex.
- Artificial Intelligence-Driven Innovations in Oncology Drug Discovery: Transforming Traditional Pipelines and Enhancing Drug Design.Drug design, development and therapy · 2025Review
- Genome skimming-based simple sequence repeat (SSR) marker discovery and characterization inPhysiology and molecular biology of plants : an international journal of functional plant biology · 2021Article
- The importance of genomic analysis in cracking the coronavirus pandemic.Expert review of molecular diagnostics · 2021Review
- Profiling COVID-19 Genetic Research: A Data-Driven Study Utilizing Intelligent Bibliometrics.Frontiers in research metrics and analytics · 2021Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundSince the outbreak of coronavirus disease 2019 (COVID-19) in China, numerous research institutions have invested in the development of anti-COVID-19 vaccines and screening for efficacious drugs to manage the virus.
objectiveTo explore the potential targets and therapeutic drugs for the prevention and treatment of COVID-19 through data mining and bioinformatics.
methodsWe integrated and profoundly analyzed 10 drugs previously assessed to have promising therapeutic potential in COVID-19 management, and have been recommended for clinical trials. To explore the mechanisms by which these drugs may be involved in the treatment of COVID-19, gene-drug interactions were identified using the DGIdb database after which functional enrichment analysis, protein-protein interaction (PPI) network, and miRNA-gene network construction were performed. We adopted the DGIdb database to explore the candidate drugs for COVID-19.
resultsA total of 43 genes associated with the 10 potential COVID-19 drugs were identified. Function enrichment analysis revealed that these genes were mainly enriched in response to other invasions, toll-like receptor pathways, and they play positive roles in the production of cytokines such as IL-6, IL-8, and INF-β. TNF, TLR3, TLR7, TLR9, and CXCL10 were identified as crucial genes in COVID-19. Through the DGIdb database, we predicted 87 molecules as promising druggable molecules for managing COVID-19.
conclusionsFindings from this work may provide new insights into COVID-19 mechanisms and treatments. Further, the already identified candidate drugs may improve the efficiency of pharmaceutical treatment in this rapidly evolving global situation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.