Foundation Models for Liquid Chromatography-High-Resolution Mass Spectrometry: A New Era beyond Labeled Datasets

Area of Science:

• Analytical Chemistry
• Computational Chemistry
• Data Science

Background:

• Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is crucial for analyzing organic sample compositions.
• Untargeted LC-HRMS generates complex datasets requiring advanced computational interpretation, often using machine learning.
• Current machine learning and deep learning methods face limitations due to data complexity and scarcity of labeled samples.

Purpose of the Study:

• To explore the potential of foundation models for enhancing LC-HRMS data analysis.
• To address the challenges of data complexity and limited labeled data in LC-HRMS interpretation.
• To highlight foundation models as a promising advancement for chemical annotation and property prediction.

Main Methods:

• Leveraging foundation models for their ability to learn transferable representations from large unlabeled datasets.
• Adapting foundation models to downstream tasks using limited labeled LC-HRMS data.
• Comparing the performance of foundation models against conventional machine learning approaches.

Main Results:

• Foundation models demonstrate superior performance in chemical annotation and molecular property prediction compared to traditional methods.
• These models effectively handle complex, data-rich LC-HRMS datasets.
• Foundation models show promise in overcoming the limitations of data scarcity in machine learning for LC-HRMS.

Conclusions:

• Foundation models represent a significant advancement for LC-HRMS data analysis.
• Future work should focus on expanding data repositories, developing privacy-preserving methods, and improving model explainability.
• Foundation models are poised to revolutionize the interpretation of complex chemical data from LC-HRMS.