MetaboliteChat: Unifying Metabolite Analysis with Multimodal AI

Accurately characterizing the mechanisms and properties of metabolite molecules is essential for advancing metabolomics and systems biology. However, existing approaches often fall short, being narrow, task-specific models that struggle to transfer knowledge across different tasks. Crucially, they cannot fully express the rich, multimodal biological context of a metabolite in natural language, leaving a significant gap in comprehensive analysis.

To address these critical challenges, researchers have developed MetaboliteChat, a multimodal ChatGPT-like large language model (LLM). This innovative tool offers a unified and interactive framework specifically designed for advanced metabolite analysis. MetaboliteChat uniquely integrates molecular-graph and image reasoning capabilities with natural language understanding, allowing it to generate comprehensive, free-form predictions about complex 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. As lead author Guo notes in the paper, "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."