Knowledge and data-driven two-layer networking for accurate metabolite annotation in untargeted metabolomics

Metabolite annotation in untargeted metabolomics remains challenging due to the vast structural diversity of metabolites. Network-based approaches have emerged as powerful strategies, particularly for annotating metabolites lacking chemical standards. Here, we develop a two-layer interactive networking topology that integrates data-driven and knowledge-driven networks to enhance metabolite annotation. A comprehensive metabolic reaction network is curated using graph neural network-based prediction of reaction relationships, enhancing both coverage and network connectivity. Experimental data are pre-mapped onto this network via sequential MS1 matching, reaction relationship mapping, and MS2 similarity constraints. The generated networking topology enables interactive annotation propagation with over 10-fold improved computational efficiency. In common biological samples, it annotates over 1600 seed metabolites with chemical standards and >12,000 putatively annotated metabolites through network-based propagation. Notably, two previously uncharacterized endogenous metabolites absent from human metabolome databases have been discovered. Overall, this strategy significantly improves the coverage, accuracy, and efficiency of metabolite annotation and is freely available as MetDNA3. Accurate metabolite annotation remains a major challenge in untargeted metabolomics. Here, the authors present MetDNA3, a framework that integrates knowledge and data-driven two-layer networking to improve both the accuracy and coverage of known metabolite annotation, while also enabling the discovery of uncharacterized metabolites.