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MetaboT: an LLM-based multi-agent framework for interactive analysis of mass spectrometry metabolomics knowledge graphs
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DOI：10.1186/s13321-026-01257-8.png)
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Abstract
En 中文
Mass spectrometry-based metabolomics generates complex, high-dimensional data that holds vast potential for biological discovery but remains difficult to integrate and interpret. Knowledge graphs (KGs) unify this heterogeneous information by representing spectra, annotations, taxa, chemical classes, and biological activities as a single interoperable network; however, their practical use is limited by the steep learning curve of corresponding specialized representation and query languages. Here we introduce MetaboT, an open-source multi-agent Large Language Model (LLM) framework that translates natural-language questions into executable SPARQL queries over metabolomics knowledge graphs. MetaboT is designed to mitigate the hallucination and schema-compliance limitations of single-model approaches through a modular architecture in which specialised agents handle scope validation, entity resolution against authoritative resources, schema-aware query generation, iterative refinement, and result interpretation. We validated MetaboT on the Experimental Natural Products Knowledge Graph (ENPKG), using an expert-authored benchmark of natural-language questions paired with reference SPARQL queries, and demonstrate its ability to answer complex questions about plant–metabolite relationships and biological activities, achieving 83.67% accuracy versus 8.16% for a single-shot baseline and reducing schema-mismatch errors to a single residual case in our benchmark. MetaboT lowers the technical barrier for metabolomics researchers and enables semantic data mining without specialised programming expertise. Scientific contribution MetaboT introduces a multi-agent LLM architecture that grounds each step of natural-language-to-SPARQL translation in authoritative identifier resolution and schema-aware validation, substantially reducing the hallucinations observed with single-model baselines. The framework couples orchestrated agents with domain-specific tools for chemical, taxonomic, and biological target resolution, and with an iterative refinement loop that distinguishes query-construction errors from genuine data absence. Released as open-source software with an archived evaluated version, a reusable benchmark, and a public web demonstrator, MetaboT provides a reproducible reference implementation for knowledge-graph-driven exploration of mass spectrometry metabolomics data.
Keywords:
Multi-agent LLM
Large language models
Knowledge graph
SPARQL
Metabolomics
Mass spectrometry
Natural products
Entity resolution
ENPKG
Cheminformatics software
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