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Allen Frontiers Group","award":["Allen Distinguished Investigator Award"],"award-info":[{"award-number":["Allen Distinguished Investigator Award"]}]}],"content-domain":{"domain":["portlandpress.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,2,4]]},"abstract":"<jats:p>The integration of single-cell genomics into the chemical genetics paradigm is reshaping how researchers profile drug activity, prioritize lead candidates, and uncover new therapeutic opportunities. Traditional chemical genetic approaches, though instrumental in linking compounds to cellular phenotypes, often rely on bulk measurements that obscure important cellular heterogeneity and limit insight into mechanisms of action. By contrast, single-cell technologies offer a transformative view of how compounds influence diverse cell types and states, capturing nuanced molecular responses that further our understanding of efficacy, resistance, and polypharmacology. From cancer to neurodegenerative disorders and other disease contexts, single-cell chemical profiling enables a more precise annotation of drug-induced effects, revealing differential responses across cellular subpopulations. These methods help identify both beneficial and adverse outcomes that may not be readily predicted by a compound\u2019s structure or known targets, enhancing preclinical prioritization and supporting rational drug repurposing strategies. As these technologies mature, advances in multiplexing, multimodal profiling, and computational analysis are expanding their scalability and applicability to increasingly complex models. The resulting data-rich assays are poised to bridge critical gaps between compound screening and clinical relevance. This review highlights the evolution of chemical genomics toward single-cell resolution and outlines emerging opportunities to leverage these methods throughout the drug discovery pipeline, from early preclinical prioritization to late-stage repurposing, ultimately accelerating the development of safer, more effective therapies.<\/jats:p>","DOI":"10.1042\/bcj20253273","type":"journal-article","created":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T14:09:41Z","timestamp":1769004581000},"page":"211-228","update-policy":"https:\/\/doi.org\/10.1042\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An expanded role for single-cell chemical genomics profiling in drug discovery"],"prefix":"10.1042","volume":"483","author":[{"given":"Adeya","family":"Wyatt","sequence":"first","affiliation":[{"name":"1Department of Biomedical Engineering, Columbia University, New York, NY 10027, U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kevin","family":"Hoffer-Hawlik","sequence":"additional","affiliation":[{"name":"1Department of Biomedical Engineering, Columbia University, New York, NY 10027, U.S.A."},{"name":"2Irving Institute for Cancer Dynamics, Columbia University, New York, NY 10027, U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ross M.","family":"Giglio","sequence":"additional","affiliation":[{"name":"3Department of Molecular Pharmacology and Therapeutics, Columbia University Medical Center, New York, NY 10032, U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elham","family":"Azizi","sequence":"additional","affiliation":[{"name":"1Department of Biomedical Engineering, Columbia University, New York, NY 10027, U.S.A."},{"name":"2Irving Institute for Cancer Dynamics, Columbia University, New York, NY 10027, U.S.A."},{"name":"4Department of Computer Science, Columbia University, New York, NY 10027, U.S.A."},{"name":"5Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY 10032, U.S.A."},{"name":"6Data Science Institute, Columbia University, New York, NY 10027, U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4387-1511","authenticated-orcid":false,"given":"Jos\u00e9 L.","family":"McFaline-Figueroa","sequence":"additional","affiliation":[{"name":"1Department of Biomedical Engineering, Columbia University, New York, NY 10027, U.S.A."},{"name":"2Irving Institute for Cancer Dynamics, Columbia University, New York, NY 10027, U.S.A."},{"name":"5Herbert Irving Comprehensive Cancer Center, Columbia University, New York, NY 10032, U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"288","published-online":{"date-parts":[[2026,1,21]]},"reference":[{"key":"2026020411414918600_BCJ-2025-3273C1","doi-asserted-by":"crossref","first-page":"564","DOI":"10.1016\/j.cell.2017.06.010","article-title":"Defining a cancer dependency map","volume":"170","author":"Tsherniak","year":"2017","journal-title":"Cell"},{"key":"2026020411414918600_BCJ-2025-3273C2","doi-asserted-by":"crossref","first-page":"1437","DOI":"10.1016\/j.cell.2017.10.049","article-title":"A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles","volume":"171","author":"Subramanian","year":"2017","journal-title":"Cell"},{"key":"2026020411414918600_BCJ-2025-3273C3","doi-asserted-by":"crossref","first-page":"1293","DOI":"10.1038\/3282","article-title":"Drug target validation and identification of secondary drug target effects using DNA microarrays","volume":"4","author":"Marton","year":"1998","journal-title":"Nat. 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