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Here, we show that the N\u2010terminal domain (NTD) of the SARS\u2010CoV\u20102 spike protein substantially induces multiple inflammatory molecules in myeloid cells and human PBMCs. Using a combination of phenotypic screening with machine learning\u2010based modeling, we identified and experimentally validated several protein kinases, including JAK1, EPHA7, IRAK1, MAPK12, and MAP3K8, as essential downstream mediators of NTD\u2010induced cytokine production, implicating the role of multiple signaling pathways in cytokine release. Further, we found several FDA\u2010approved drugs, including ponatinib, and cobimetinib as potent inhibitors of the NTD\u2010mediated cytokine release. Treatment with ponatinib outperforms other drugs, including dexamethasone and baricitinib, inhibiting all cytokines in response to the NTD from SARS\u2010CoV\u20102 and emerging variants. Finally, ponatinib treatment inhibits lipopolysaccharide\u2010mediated cytokine release in myeloid cells\n                    <jats:italic>in\u00a0vitro<\/jats:italic>\n                    and lung inflammation mouse model. Together, we propose that agents targeting multiple kinases required for SARS\u2010CoV\u20102\u2010mediated cytokine release, such as ponatinib, may represent an attractive therapeutic option for treating moderate to severe COVID\u201019.\n                  <\/jats:p>","DOI":"10.15252\/msb.202110426","type":"journal-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T11:00:46Z","timestamp":1628852446000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Machine learning identifies molecular regulators and therapeutics for targeting SARS\u2010CoV2\u2010induced cytokine release"],"prefix":"10.1038","volume":"17","author":[{"given":"Marina","family":"Chan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siddharth","family":"Vijay","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9893-9256","authenticated-orcid":false,"given":"John","family":"McNevin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2276-7117","authenticated-orcid":false,"given":"M Juliana","family":"McElrath","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3792-7120","authenticated-orcid":false,"given":"Eric C","family":"Holland","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4453-3031","authenticated-orcid":false,"given":"Taranjit S","family":"Gujral","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,9,6]]},"reference":[{"key":"BFMSB202110426_CR1","unstructured":"Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M (2016) Tensorflow: Large\u2010scale machine learning on heterogeneous distributed systems. arXivhttps:\/\/arxiv.org\/abs\/1603.04467 [PREPRINT]"},{"key":"BFMSB202110426_CR2","doi-asserted-by":"crossref","unstructured":"Allen IC (2014) The utilization of oropharyngeal intratracheal PAMP administration and bronchoalveolar lavage to evaluate the host immune response in mice. 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