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Retrieval-Augmented Language Models Enable Scalable Chemical Source Classification in Metabolomics Workflows.

Prajit Rajkumar1,2, Runbang Tang1,2, Harshada Sapre1,2

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Automating chemical classification for exposomics and metabolomics is crucial. The new chemsource tool uses large language models (LLMs) and retrieval-augmented generation (RAG) to classify chemicals by exposure, improving data interpretation.

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Area of Science:

  • Environmental chemistry
  • Computational chemistry
  • Bioinformatics

Background:

  • Interpreting exposomics and metabolomics data requires scalable chemical classification.
  • Current functional and exposure-based chemical labeling is manual and labor-intensive.
  • Automating chemical classification is essential for advancing these fields.

Purpose of the Study:

  • To develop and present chemsource, a framework for automated chemical classification.
  • To integrate large language models (LLMs) with retrieval-augmented generation (RAG) for this task.
  • To enable user-defined classification categories for diverse applications.

Main Methods:

  • chemsource retrieves chemical descriptions from Wikipedia or PubMed abstracts.
  • It then prompts LLMs to assign user-defined categories based on retrieved content.
  • Classification was demonstrated across five exposure categories: endogenous metabolites, food molecules, drugs, personal care products, and industrial chemicals.

Main Results:

  • Benchmarking against 4,953 manually curated labels showed 75% overall agreement.
  • Category-level recall exceeded 75% across all tested classes.
  • Application to eight metabolomics datasets revealed distinct exposure patterns in various biospecimens and environmental samples.

Conclusions:

  • chemsource offers a flexible and customizable solution for automated chemical classification.
  • The framework demonstrates utility in metabolomics workflows and identifying exposure patterns.
  • It is available as a Python package, enabling diverse classification tasks without coding expertise.