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General Intelligence-based Fragmentation (GIF): A framework for peak-labeled spectra simulation.

Margaret R Martin1, Soha Hassoun1,2

  • 1Department of Computer Science, Tufts University, Medford, MA 02155, USA.

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|November 26, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Large language models (LLMs) can now aid metabolomics by simulating mass spectra for improved annotation. A new framework, General Intelligence-based Fragmentation (GIF), guides LLMs for better spectral prediction and reasoning.

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Area of Science:

  • Computational chemistry
  • Metabolomics
  • Artificial intelligence

Background:

  • Metabolomics research is limited by low spectral annotation rates, despite advanced tools.
  • Large language models (LLMs) show promise for scientific applications like mass spectra annotation.

Purpose of the Study:

  • To introduce a novel framework, General Intelligence-based Fragmentation (GIF), for guiding LLMs in mass spectra simulation.
  • To evaluate the performance of generalist LLMs in molecular fragmentation and intensity prediction using GIF.

Main Methods:

  • Developed the GIF framework utilizing structured prompting, tagging, and iterative refinement.
  • Fine-tuned pretrained LLMs and evaluated their performance on the MassSpecGym QA-sim dataset.
  • Benchmarked GIF against other LLMs and deep learning models.

Main Results:

  • GPT-4o and GPT-4o-mini achieved high cosine similarity (0.36 and 0.35) in spectral simulation.
  • GIF outperformed other LLMs (GPT-5, Llama-3.1) and domain-specific models (ChemDFM).
  • The framework demonstrated superior performance compared to several deep learning baselines.

Conclusions:

  • GIF provides a structured approach for LLM-guided spectra simulation, enhancing molecular fragmentation analysis.
  • LLMs, guided by GIF, can facilitate human-in-the-loop workflows and enable explainable reasoning in metabolomics.
  • The study highlights the potential of systematic LLM guidance for complex scientific tasks in metabolomics.