3 Key Takeaways
Plain English Summary- 01Core Finding
Here, we present MetLLM, a transformer-based language model that learns bile acid modification patterns to prioritize candidate bile acid analogs, and MetSpace, an in silico bile acid chemical landscape containing 3,900 prioritized candidates.
- 02Study Evidence
Tested in a laboratory experimental model (preclinical discovery) at the preclinical stage.
- 03Practical Meaning
Early laboratory or animal model findings — human trials are still needed to confirm safety and benefit.
What This Means for You
In vitro functional validation showed that MS2141 modulates M2 receptor-associated Gi/o signaling and alters electrophysiological parameters in human induced pluripotent stem cell-derived cardiomyocytes. This work expands the searchable bile acid metabolome and provides a computational framework for systematic metabolite discovery.
The Discovery
Bile acids represent one of nature's most chemically diverse and therapeutically important metabolite families. However, the human bile acid metabolome remains largely uncharted, with analytical limitations obscuring potentially thousands of biologically active species present in cells and biofluids. Here, we present MetLLM, a transformer-based language model that learns bile acid modification patterns to prioritize candidate bile acid analogs, and MetSpace, an in silico bile acid chemical landscape containing 3,900 prioritized candidates. Liquid chromatography-high resolution mass spectrometry analysis of human feces confirmed multiple newly detected and structurally novel bile acids, including MS2141 and MS1281.
How It Works, Simply
This endocrinology research focuses on stem cells · Receptor targeting · AI model. Researchers evaluated it using a laboratory experimental model (Preclinical discovery) at the preclinical stage of medical research.
3 Key Takeaways
Clinical Takeaways- 01Core Finding
Here, we present MetLLM, a transformer-based language model that learns bile acid modification patterns to prioritize candidate bile acid analogs, and MetSpace, an in silico bile acid chemical landscape containing 3,900 prioritized candidates.
- 02Study Evidence
Tested in a laboratory experimental model (preclinical discovery) at the preclinical stage.
- 03Practical Meaning
Early laboratory or animal model findings — human trials are still needed to confirm safety and benefit.
Biological Mechanism & Study Design
Study Design & Cohort
Lab cell study
Laboratory experimental model (Preclinical discovery) · Preclinical
Biological Pathway & Modality
Stem cells · Receptor targeting · AI model
Intervention: AI-guided discovery and expansion of the human bile acid landscape
Experimental Findings & Endpoints
Bile acids represent one of nature's most chemically diverse and therapeutically important metabolite families.
However, the human bile acid metabolome remains largely uncharted, with analytical limitations obscuring potentially thousands of biologically active species present in cells and biofluids. Here, we present MetLLM, a transformer-based language model that learns bile acid modification patterns to prioritize candidate bile acid analogs, and MetSpace, an in silico bile acid chemical landscape containing 3,900 prioritized candidates.
Liquid chromatography-high resolution mass spectrometry analysis of human feces confirmed multiple newly detected and structurally novel bile acids, including MS2141 and MS1281.
Clinical Translation & Practice Context
In vitro functional validation showed that MS2141 modulates M2 receptor-associated Gi/o signaling and alters electrophysiological parameters in human induced pluripotent stem cell-derived cardiomyocytes.
This work expands the searchable bile acid metabolome and provides a computational framework for systematic metabolite discovery.
The Bottom Line · Clinical Pearl
“This work expands the searchable bile acid metabolome and provides a computational framework for systematic metabolite discovery.”
Published in Cell Metabolism · Current Stage: Preclinical