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

arXiv:2608.06253 · cs.LG · Submitted 2026-08-06 · Read on arXiv

Dohyun Ku, Min Gu Kwak, Francisco J. Pasquel, Jing Li

cs.LG

Submitted: 2026-08-06

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

Code: https://github.com/dohyunku9/MetaboLLM

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Graph Construction — 24× in this paper · explained in 3 episodes
• RAG — 19× in this paper · explained in 3 episodes
• AUC — 17× in this paper · explained in 2 episodes
• Benchmark — 15× in this paper · explained in 2 episodes
• Accuracy — 13× in this paper · explained in 1 episode
• Fine-Tuning — 9× in this paper · explained in 5 episodes
• Embeddings — 8× in this paper · explained in 2 episodes
• Abundance — 7× in this paper · explained in 1 episode
• Hyperparameters — 7× in this paper · explained in 2 episodes
• Ablation — 6× in this paper · explained in 1 episode
• Transformer — 5× in this paper · explained in 2 episodes
• Augmentation — 4× in this paper · explained in 2 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Sparsity — 4× in this paper · explained in 5 episodes
• Domain Adaptation — 3× in this paper · explained in 5 episodes
• Knowledge Graph — 3× in this paper · explained in 8 episodes
• XGBoost — 3× in this paper · explained in 3 episodes
• Benchmarking — 2× in this paper · explained in 4 episodes
• Coherence — 2× in this paper · explained in 2 episodes
• Computational Efficiency — 2× in this paper · explained in 2 episodes
• Cosine Similarity — 2× in this paper · explained in 3 episodes
• Heterogeneous Graph — 2× in this paper · explained in 2 episodes
• Knowledge graphs — 2× in this paper · explained in 2 episodes
• Semantic Similarity — 2× in this paper · explained in 2 episodes
• Task Alignment — 2× in this paper · explained in 2 episodes
• Weight Decay — 2× in this paper · explained in 2 episodes
• Computational Biology — 1× in this paper · explained in 2 episodes
• Evaluation Protocol — 1× in this paper · explained in 2 episodes
• External Validation — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 8 episodes
• Generalization — 1× in this paper · explained in 14 episodes
• Generator — 1× in this paper · explained in 2 episodes
• Grounding — 1× in this paper · explained in 3 episodes
• Language Models (LLMs) — 1× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 9 episodes
• Multilayer Perceptron (MLP) — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Papers — 1× in this paper · explained in 3 episodes
• Parameter-Efficient Fine-Tuning — 1× in this paper · explained in 3 episodes
• Redundancy — 1× in this paper · explained in 2 episodes
• Regularization — 1× in this paper · explained in 3 episodes
• Retrieval-Augmented Generation (RAG) — 1× in this paper · explained in 21 episodes
• Robustness — 1× in this paper · explained in 6 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 13 episodes
• Transferability — 1× in this paper · explained in 5 episodes

Sources

• MEDITRON-70B: Scaling Medical Pretraining for Large Language Models
• MetaboT: An LLM-based Multi-Agent Frameworkfor Interactive Analysis of Mass SpectrometryMetabolomics Knowledge Graphs
• MetaboKG: An Analysis-centric Knowledge Graph Framework for Untargeted Metabolomics
• MedGemma 1.5 Technical Report
• FineMedLM-o1: Enhancing Medical Knowledge Reasoning Ability of LLM from Supervised Fine-Tuning to Test-Time Training
• Qwen3 Technical Report
• Gemma 3 Technical Report
• BERTScore: Evaluating Text Generation with BERT
• ChemBERTa-2: Towards Chemical Foundation Models
• OpenAI GPT-5 System Card

Related papers

• Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation
• AIRL-S: Unifying Reinforcement Learning and Search-Based Test-Time Scaling via Adversarial Inverse Reinforcement Learning
• Transformers as Bayesian In-Context Experimenters: Smoothness-Adaptive Efficient ATE Estimation
• Convergence issues in Relational Concept Analysis based on AOC-posets
• Beliefs Beyond Posteriors: The Predictive Cost of ELBO Binding
• Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks