Molecular profiling of small-molecules offers invaluable insights into the function of compounds and allows for hypothesis generation about small molecule direct targets and secondary effects. However, current profiling methods are either limited in the number of measurable parameters or throughput. Here, we developed a multiplexed, unbiased framework that, by linking genetic to drug-induced changes in nearly a thousand metabolites, allows for high-throughput functional annotation of compound libraries in Escherichia coli. First, we generated a reference map of metabolic changes from (CRISPR) interference with 352 genes in all major essential biological processes. Next, based on the comparison of genetic with 1342 drug-induced metabolic changes we made de novo predictions of compound functionality and revealed antibacterials with unconventional Modes of Action. We show that our framework, combining dynamic gene silencing with metabolomics, can be adapted as a general strategy for comprehensive high-throughput analysis of compound functionality, from bacteria to human cell lines.

In the version of this article initially published, an incorrect file for Supplementary Dataset 3 was inadvertently uploaded. Supplementary Dataset 3 has been updated with a file containing the correct sequencing results and enrichment analysis, which now correctly correlates with the screening summary file provided in Supplemental Dataset 5. We very much appreciate one of the readers of this article for pointing out this error. The changes have been made to the online version of the article.