Untargeted screening for small-molecule enzyme inhibitors via high-throughput metabolomics
OMICS-based screening offers a promising approach for untargeted drug discovery. Recently, mass-spectrometry metabolomics and proteomics was used for inferring drug mechanism of action and off-target effects, analyzing cellular response to treatment with numerous clinically approved drugs and tool compounds. Here, we developed a novel high-throughput LC-MS metabolomics screening pipeline and computational target deconvolution method, enabling the identification of novel compounds modulating cellular metabolism. Training our Graph Neural Network (GNN)-based method on time- and dose-dependent metabolic responses of cultured cells treated with 76 known metabolic inhibitors, we correctly identified the target pathway within the top three ranked pathways for ~50% of the drugs. Applying this approach to a diversity library of 1,020 drug-like compounds, we discovered five novel inhibitors targeting clinically relevant pathways and enzymes involved in purine and pyrimidine biosynthesis and redox metabolism. Our pipeline is readily scalable for screening thousands of compounds to identify new, clinically relevant metabolic inhibitors.
