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. 2023 Jun 9;14:3390. doi: 10.1038/s41467-023-38859-x

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© The Author(s) 2023

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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — A Cells are treated with the compound of interest, and metabolite changes are measured with high throughput metabolomics. B Machine learning is used to identify drug-specific perturbations by comparison to publicly available drug response metabolomic profiles. C Key perturbed metabolites are provided as supplements to attempt growth rescue, and metabolic modeling is used to analyze the results to determine whether patterns are consistent with inhibition of certain metabolic pathways by the compound. D Structural analysis identifies candidate enzymes through homology to known targets of the drug. E Candidate genes are validated through a combination of overexpression of the target to rescue growth, activity assays to verify binding and inhibition of the target, and imaging to determine phenotypic effect.