MetaboLLM is a metabolomics-specialized LLM adapted via continual pretraining…

2026-08-06 · 16:42 UTC ·Dohyun Ku, Min Gu Kwak, Francisco J. Pasquel... ·1 min read

Brief

MetaboLLM is a domain-specialized language model for metabolomics trained with continual pretraining, supervised fine-tuning, and structured retrieval; MetaboLLM-GIN maps generated biochemical descriptions into metabolite graphs analyzed by a graph isomorphism network. The pipeline outperforms base and medically adapted LLMs, transfers to external benchmarks, attains AUCs 0.8616 and 0.8123 on two clinical tasks, and yields interpretable biological insights.

Why it matters

MetaboLLM is a metabolomics-specialized LLM adapted via continual pretraining, supervised fine-tuning, and structured retrieval; it outperformed corresponding base and medically adapted models across four backbone families on metabolomics knowledge, relational, and description tasks and transferred to an external public benchmark.

Key details

MetaboLLM-GIN converts LLM-generated biochemical descriptions into metabolite graphs for patient-level prediction using a graph isomorphism network, achieving AUC 0.8616 for stress hyperglycemia after coronary artery bypass grafting and AUC 0.8123 for postmenopausal hormone-regimen classification, surpassing conventional models and alternative graph constructions.

Model interpretation produced biologically meaningful findings; paper authors are Dohyun Ku, Min Gu Kwak, Francisco J. Pasquel, and Jing Li, posted to arXiv 2026-08-06 (https://arxiv.org/abs/2608.06253v1).

Abstract

Metabolomics knowledge is distributed across heterogeneous resources and remains difficult to translate into predictive representations. We developed MetaboLLM, a metabolomics-specialized large language model adapted through continual pretraining, supervised fine-tuning, and structured retrieval, together with MetaboLLM-GIN, which converts generated biochemical descriptions into metabolite graphs for patient-level prediction using a graph isomorphism network. Across four backbone families, MetaboLLM outperformed corresponding base and medically adapted models on metabolomics knowledge, relational, and description tasks, and transferred to an external public benchmark. MetaboLLM-GIN achieved the highest AUC for stress hyperglycemia prediction after coronary artery bypass grafting (0.8616) and postmenopausal hormone-regimen classification (0.8123), outperforming conventional models, alternative graph constructions, and graphs generated from unadapted or non-retrieval LLM configurations. Model interpretation further produced biologically meaningful findings in both applications. These results show that domain-specialized language models can organize heterogeneous biochemical knowledge into predictive and interpretable metabolite graph representations.

Comment: 60 pages, 3 figures, 16 tables; includes Supplementary Information