MetaboLLM: a metabolomics-specialized large language model for biochemical knowledge integration and predictive metabolite graph construction

TL;DR - MetaboLLM is a metabolomics-specialized LLM (continual pretraining + SFT + structured retrieval) paired with MetaboLLM-GIN, which turns generated biochemical descriptions into metabolite graphs for patient-level clinical prediction. It shows domain adaptation can convert scattered biochemical knowledge into predictive, interpretable graph representations.

• Adaptation pipeline combines continual pretraining, supervised fine-tuning, and structured retrieval; evaluated across four backbone model families, beating both base and medically adapted counterparts on metabolomics knowledge, relational, and description tasks, with transfer to an external public benchmark.
• MetaboLLM-GIN converts LLM-generated descriptions into metabolite graphs consumed by a graph isomorphism network for patient-level prediction.
• Clinical results: AUC 0.8616 for stress hyperglycemia after coronary artery bypass grafting and 0.8123 for postmenopausal hormone-regimen classification, ahead of conventional models, alternative graph constructions, and graphs from unadapted or non-retrieval LLM configurations.
• Ablations implicate both domain adaptation and retrieval as necessary; model interpretation reportedly yielded biologically meaningful findings in both applications.