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. 2021 Feb 24;11:4495. doi: 10.1038/s41598-021-84044-9

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Overview of the workflow to identify candidate repurposable drugs to combat COVID-19. (A) Drugs that are currently in use to treat coronavirus and putative COVID-19 treatments were clustered based on mechanism of action and ATC class. (B) Gene expression data of the 978 genes that comprise the Library of Integrated Network-Based Cellular Signature (iLINCS) L1000 genes were extracted from severe acute respiratory syndrome coronavirus 2020 (SARS-CoV-2) (GSE147507) transcriptomic datasets. (C) Consensus iLINCS gene signatures were generated for drug groupings and disease. (D) Connectivity analysis was conducted and a list of chemical perturbagens that are concordant (≥ 0.321 concordance) to the drug target grouping signatures or discordant (≤ − 0.321 discordance) to the disease signatures was generated. Chemical perturbagens are filtered and curated to identify top candidate repurposable drugs.