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Identification of potential treatments for COVID-19 through artificial intelligence-enabled phenomic analysis of human cells infected with SARS-CoV-2

Heiser, K.; McLean, P. F.; Davis, C. T.; Fogelson, B.; Gordon, H. B.; Jacobson, P.; Hurst, B. L.; Miller, B. J.; Alfa, R. W.; Earnshaw, B. A.; Victors, M. L.; Chong, Y. T.; Haque, I. S.; Low, A. S.; Gibson, C. C.

2020-04-23 systems biology
10.1101/2020.04.21.054387 bioRxiv
Show abstract

To identify potential therapeutic stop-gaps for SARS-CoV-2, we evaluated a library of 1,670 approved and reference compounds in an unbiased, cellular image-based screen for their ability to suppress the broad impacts of the SARS-CoV-2 virus on phenomic profiles of human renal cortical epithelial cells using deep learning. In our assay, remdesivir is the only antiviral tested with strong efficacy, neither chloroquine nor hydroxychloroquine have any beneficial effect in this human cell model, and a small number of compounds not currently being pursued clinically for SARS-CoV-2 have efficacy. We observed weak but beneficial class effects of {beta}-blockers, mTOR/PI3K inhibitors and Vitamin D analogues and a mild amplification of the viral phenotype with {beta}-agonists.

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.