The Language of Elution: Autoregressive Prediction of the Next Feature in Untargeted LC-HRMS Lipidomics
arXiv:2606.05225v1 Announce Type: cross Abstract: Untargeted liquid chromatography-high-resolution mass spectrometry (LC-HRMS) detects thousands of molecular features per sample, yet only 2-20% receive confident structural annotations. A root cause of this "dark metabolome" is that tandem MS/MS acquisition is reactive: instruments select precursors only after ions appear, blind to what elutes next. We reframe chromatographic elution as an autoregressive sequence prediction task. Because reversed-phase elution order is governed by hydrophobicity, successive features form a physically constraine
The proliferation of advanced AI techniques, particularly in autoregressive modeling, is enabling new applications in complex scientific data analysis like untargeted mass spectrometry.
This development could significantly accelerate drug discovery, biomarker identification, and metabolic profiling by addressing a long-standing bottleneck in lipidomics research.
The ability to predict chromatographic elution will transform untargeted mass spectrometry from a reactive data acquisition process to a more proactive and informative one, improving data completeness.
- · Biotech companies
- · Pharmaceutical R&D
- · AI in life sciences
- · Analytical chemistry instrument manufacturers
- · Traditional manual annotation methods
- · Labs with limited AI integration
Increased identification rate of molecular features in untargeted LC-HRMS experiments.
Faster and more comprehensive understanding of complex biological systems and disease states.
The acceleration of personalized medicine and the development of novel therapeutic targets based on detailed lipidomic profiles.
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Read at arXiv cs.LG