Nature · 2026
Language model-guided anticipation and discovery of mammalian metabolites.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Qiang Hantao, Wang Fei, Lu Wenyun, Xing Xi, Kim Hahn, Mérette Sandrine A M, Ayres Lucas B, Oler Eponine, AbuSalim Jenna E, Roichman Asael, Neinast Michael, Cordova Ricardo A, Lee Won Dong, Herbst Ehud, Gupta Vishu, Neff Samuel L, Hiebert-Giesbrecht Mickel, Young Adamo, Gautam Vasuk, Tian Siyang, Wang Bo, Röst Hannes, Baidwan Jatinder, Greiner Russell, Chen Li, Johnston Chad W, Foster Leonard J, Shapiro Aaron M, Wishart David S, Rabinowitz Joshua D, Skinnider Michael A
Аннотация
Despite decades of study, large parts of the mammalian metabolome remain unexplored1. Mass spectrometry-based metabolomics routinely detects thousands of small molecule-associated peaks in human tissues and biofluids, but typically only a small fraction of these can be identified, and structure elucidation of novel metabolites remains challenging2-4. Biochemical 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 models5-7 to anticipate the existence of previously uncharacterized metabolites. We introduce DeepMet, a chemical language model that learns from the structures of known metabolites to anticipate the existence of previously unrecognized metabolites. Integration of DeepMet with mass spectrometry-based metabolomics data facilitates metabolite discovery. We harness DeepMet to reveal several dozen structurally diverse mammalian metabolites. Our work demonstrates the potential for language models to advance the mapping of the mammalian metabolome.