OMICS-based screening offers a promising approach for untargeted drug discovery. Mass-spectrometry metabolomics and proteomics were recently used for inferring drug mechanisms of action and off-target effects, analyzing the cellular response to treatment with numerous clinically approved drugs and tool compounds. In this talk, we present a novel high-throughput LC-MS metabolomics screening pipeline and a deep-learning method specifically designed for cellular metabolism.
Our novel Graph Neural Network (GNN) model fits the unique structure of this domain, enabling accurate identification of target pathways. Learning from 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.
We will dive deep into our machine learning approach, offering valuable insights and comprehensive ablation studies that highlight its strength and domain-specific innovation.