# MetaboT: A conversational AI agent for accessible Mass Spectrometry Metabolomics Data Mining

Madina Bekbergenova, Lucas Pradi, Emma Tysinger, Benjamin Navet, Olivier Kirchhoffer, Jean-Luc Wolfender, Florence Mehl, Marco Pagni, Wout Bittremieux, Fabien Gandon, et al.

► To cite this version:

Madina Bekbergenova, Lucas Pradi, Emma Tysinger, Benjamin Navet, Olivier Kirchhoffer, et al. MetaboT: A conversational AI agent for accessible Mass Spectrometry Metabolomics Data Mining. Benelux Metabolomics Days 2025, Aug 2025, Leiden, Netherlands. hal-05235376

HAL Id: hal-05235376

https://hal.science/hal-05235376v1

Submitted on 1 Oct 2025

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# MetaboT: A Conversational AI Agent for Accessible Mass Spectrometry Metabolomics Data Mining

Madina Bekbergenova<fnref n="1" />,²,⁶; Lucas Pradi<fnref n="1" />,²; Emma Tysinger<fnref n="1" />,³; Benjamin Navet²; Olivier Kirchhoffer⁴; Jean-Luc Wolfender⁴; Florence Mehl⁵; Marco Pagni⁵; Wout Bittremieux⁶,\*; Fabien Gandon⁷; Louis Felix Nothias<fnref n="1" />,²,\*

## From raw LC-MS data to Knowledge Graph

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## SPARQL query

Question: How many features have the same SIRIUS and ISDB annotation?

```
PREFIX enpkg: <https://enpkg.commons-lab.org/kg/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
```

```
SELECT (COUNT(?feature) AS ?count)
```

WHERE

```
{
?feature rdf:type enpkg:LCMSFeature .
?feature enpkg:has_sirius annotation ?sirius annotation .
?sirius annotation rdf:type enpkg:Sirius StructureAnnotation .
?sirius annotation enpkg:has InChIkey2D ?sirius ik2d .
?feature enpkg:has isdb annotation ?isdb annotation .
?isdb annotation rdf:type enpkg:IsdbAnnotation .
?isdb annotation enpkg:has InChIkey2D ?isdb ik2d .
FILTER(?isdb ik2d = ?sirius ik2d) .
}
```

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RDF

## Background

Knowledge graphs (KGs) provide a powerful way to represent complex information through semantic structures. Mining KGs can be efficiently performed using a specialized query language like SPARQL. With KGs being inherently aligned with natural language, the emerging capabilities of large language models (LLMs) present new opportunities for facilitating the querying of these graphs. We propose an AI framework that integrates KGs and LLMs to effectively interrogate complex metabolomics data encapsulated within KGs.

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## Methods

We developed MetaboT, an AI system that uses LLM agents to translate user questions into SPARQL queries for data mining. MetaboT executes these queries on a KG and provides answers, simplifying interactions with complex metabolomics data. As a proof of concept, it currently accesses a mass spectrometry dataset of 1,600 plant extracts [1]. Evaluation on 50 metabolomics-related questions showed that MetaboT achieved 83.67% accuracy, compared to 8.16% for a baseline LLM (GPT-4o), demonstrating the necessity of our multi-agent approach for accurate SPARQL query generation.

## Researcher

What are the most frequently annotated chemical classes in the MS data of that plant species ?

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MetaboT

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## Metabolomics Knowledge Graph (KG)

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<footnote n="1">¹Université Côte d'Azur, CNRS, ICN, Nice, France; ²Interdisciplinary Institute for Artificial Intelligence (3iA) Côte d'Azur, Sophia-Antipolis, France; ³MIT, Cambridge, MA, USA; ⁴University of Geneva, Geneva, Switzerland; ⁵Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland; ⁶University of Antwerp, Antwerp, Belgium; ⁷INRIA, Université Côte d'Azur, CNRS, I3S, Sophia-Antipolis, France</footnote>