Enterprise AI Analysis
Language model-guided anticipation and discovery of mammalian metabolites
This paper introduces DeepMet, a chemical language model that learns from known metabolite structures to anticipate previously uncharacterized metabolites. Integrating DeepMet with mass spectrometry-based metabolomics data facilitates discovery, revealing dozens of new mammalian metabolites.
Executive Impact & Key Findings
DeepMet redefines metabolite discovery, offering unprecedented accuracy and efficiency in mapping the mammalian metabolome.
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DeepMet successfully generated previously uncharacterized metabolites
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Sampling frequency alone prioritized withheld metabolites
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DeepMet + CFM-ID correctly assigned held-out metabolites (positive mode)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Enterprise Process Flow
Train DeepMet on known metabolite structures
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Generate SMILES strings from trained model
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Tabulate sampling frequencies for unique chemical structures
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Sort structures by sampling frequency
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DeepMet generates valid SMILES strings
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DeepMet identified in human biofluids
Uncovering N-carbamyl-proline
DeepMet successfully predicted N-carbamyl-proline, a previously unrecognized human metabolite, and validated its presence in human urine using synthetic standards and LC-MS/MS. This highlights the model's ability to fill gaps in existing metabolic databases.
| Approach | Top-1 Accuracy | Structural Similarity (Tc) |
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| DeepMet + CFM-ID |
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| AddCarbon |
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| Training Set Search |
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Quantify Your AI Impact
Estimate the potential cost savings and efficiency gains your organization could achieve by integrating advanced AI solutions like DeepMet.
Estimated Annual Savings
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Hours Reclaimed Annually
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Your AI Implementation Roadmap
Our phased approach ensures a smooth integration and maximizes the impact of DeepMet on your research capabilities.
Phase 1: Discovery & Strategy
Initial assessment of your current metabolomics workflows and identification of key areas where DeepMet can provide the most impact. Development of a tailored integration strategy.
Phase 2: Data Integration & Model Customization
Seamless integration of DeepMet with your existing mass spectrometry data pipelines. Customization of the model to align with your specific research objectives and data types.
Phase 3: Validation & Deployment
Rigorous testing and validation of DeepMet's predictions against your experimental data. Full-scale deployment and training of your team to leverage the platform effectively.
Ready to transform your metabolomics research? Schedule a consultation to see how DeepMet can accelerate your discoveries and improve data interpretation.
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