Researchers have developed DeepMet, a chemical language model designed to identify the "dark matter" of the metabolome by predicting the structures of previously unrecognized metabolites. By training on textual representations of known chemical structures, the model learns metabolic logic to generate plausible new molecules that existing databases often overlook. The study demonstrates that DeepMet can prioritize these hypothetical structures based on their generation frequency and successfully match them to mass spectrometry data from biological samples. This computational approach was validated by discovering dozens of new metabolites in mouse tissues and human biofluids through comparison with synthetic standards. Ultimately, the tool provides a systematic way to fill gaps in metabolic maps and improve the annotation of complex chemical datasets.

References:

• Qiang H, Wang F, Lu W, et al. Language model-guided anticipation and discovery of mammalian metabolites[J]. Nature, 2026: 1-10.