MetaboliteChat: A Unified Multimodal Large Language Model for Interactive Metabolite Analysis and Functional Insights
Guo, Z.; Duan, D.; Liang, Y.; Patil, A.; Xie, P.
Show abstract
Accurately characterizing the mechanisms and properties of metabolite molecules is essential for advancing metabolomics and systems biology. Yet most existing approaches are narrow, task-specific models that do not transfer across tasks and cannot express the richer, multimodal biological context of a metabolite in natural language. To address these challenges, we introduce MetaboliteChat, a multimodal ChatGPT-like large language model (LLM) that provides a unified and interactive framework for metabolite analysis. MetaboliteChat integrates molecular-graph and image reasoning with natural language understanding to generate comprehensive, free-form predictions about metabolite mechanisms and properties. The MetaboliteChat architecture consists of a graph neural network (GNN), a convolutional neural network (CNN), a large language model (LLM), and adapters, all trained end to end. This unified, multimodal design enables interactive reasoning over unseen metabolites, allowing the model to integrate structural and contextual cues and support discovery and translational insights across diverse biological systems.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.