Untargeted screening of drug-like compounds using high-throughput metabolomics uncovers inhibitors of key biological pathways
Abstract
Metabolomics has demonstrated immense potential in elucidating mechanisms of action and off-target effects of known drugs. Here, we propose that metabolomic screening of cellular response to treatment with drug-like compounds can uncover clinically relevant inhibitors - driven by the extensive catalog of metabolic pathways implicated in human diseases. We developed a high-throughput LC-MS metabolomics pipeline paired with a computational Graph Neural Network for target deconvolution, and applied it to screen a library of 1,020 drug-like compounds in cultured cells in vitro. The screen revealed four drug-like compounds targeting key disease pathways, including inhibitors of purine and pyrimidine biosynthesis and an inducer of glutathione metabolism; validated via comprehensive metabolic assessments and growth rescue experiments. In a muscle atrophy model, driven by redox imbalance, the glutathione metabolism inducer reversed atrophy markers and restored protein content. Our scalable pipeline can be readily expanded to screen thousands of compounds, paving the way for the discovery of clinically relevant metabolic inhibitors.
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BibTeX
@article{sarvin2026untargeted,
title = {{Untargeted screening of drug-like compounds using high-throughput metabolomics uncovers inhibitors of key biological pathways}},
author = {Nikita Sarvin and Ron Kantorovich and Samrat Chakraborty and Shiri Procaccia and Yarden Lev and Boris Sarvin and Avi Rutenberg and Mariam Fokra and Shenhav Shemer and Tomer Shlomi},
journal = {Nature Communications},
year = {2026},
month = oct,
doi = {10.1038/s41467-026-78024-8},
url = {https://www.nature.com/articles/s41467-026-78024-8},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Untargeted screening of drug-like compounds using high-throughput metabolomics uncovers inhibitors of key biological pathways
AU - Nikita Sarvin
AU - Ron Kantorovich
AU - Samrat Chakraborty
AU - Shiri Procaccia
AU - Yarden Lev
AU - Boris Sarvin
AU - Avi Rutenberg
AU - Mariam Fokra
AU - Shenhav Shemer
AU - Tomer Shlomi
PY - 2026
DA - 2026/10/01
JO - Nature Communications
DO - 10.1038/s41467-026-78024-8
UR - https://www.nature.com/articles/s41467-026-78024-8
AB - Metabolomics has demonstrated immense potential in elucidating mechanisms of action and off-target effects of known drugs. Here, we propose that metabolomic screening of cellular response to treatment with drug-like compounds can uncover clinically relevant inhibitors - driven by the extensive catalog of metabolic pathways implicated in human diseases. We developed a high-throughput LC-MS metabolomics pipeline paired with a computational Graph Neural Network for target deconvolution, and applied it to screen a library of 1,020 drug-like compounds in cultured cells in vitro. The screen revealed four drug-like compounds targeting key disease pathways, including inhibitors of purine and pyrimidine biosynthesis and an inducer of glutathione metabolism; validated via comprehensive metabolic assessments and growth rescue experiments. In a muscle atrophy model, driven by redox imbalance, the glutathione metabolism inducer reversed atrophy markers and restored protein content. Our scalable pipeline can be readily expanded to screen thousands of compounds, paving the way for the discovery of clinically relevant metabolic inhibitors.
SN - 2041-1723
ER -