AI Article Synopsis
- Despite extensive research, much of the mammalian metabolome is still unexplored, with mass spectrometry detecting many small molecules but revealing few identified metabolites.
- A novel approach using DeepMet, a chemical language model, has been developed to uncover previously unknown metabolites by learning the biosynthetic logic of known compounds.
- By combining DeepMet with tandem mass spectrometry, the research enables automated discovery of a wide range of metabolites, demonstrating the potential of language models to enhance our understanding of the metabolome.
Article Abstract
Despite decades of study, large parts of the mammalian metabolome remain unexplored. Mass spectrometry-based metabolomics routinely detects thousands of small molecule-associated peaks within human tissues and biofluids, but typically only a small fraction of these can be identified, and structure elucidation of novel metabolites remains a low-throughput endeavor. Biochemical large language models have transformed the interpretation of DNA, RNA, and protein sequences, but have not yet had a comparable impact on understanding small molecule metabolism. Here, we present an approach that leverages chemical language models to discover previously uncharacterized metabolites. We introduce DeepMet, a chemical language model that learns the latent biosynthetic logic embedded within the structures of known metabolites and exploits this understanding to anticipate the existence of as-of-yet undiscovered metabolites. Prospective chemical synthesis of metabolites predicted to exist by DeepMet directs their targeted discovery. Integrating DeepMet with tandem mass spectrometry (MS/MS) data enables automated metabolite discovery within complex tissues. We harness DeepMet to discover several dozen structurally diverse mammalian metabolites. Our work demonstrates the potential for language models to accelerate the mapping of the metabolome.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11601323 | PMC |
| http://dx.doi.org/10.1101/2024.11.13.623458 | DOI Listing |
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