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- Here, we proposed a general method of a knowledge-based hybrid molecular network (KHMN) to screen and annotate specialized metabolites involved in important biological functions in complex plant extracts. Based on a similar structure with a similar function, KHMN was orientated by heterogeneous MS/MS spectra of the known functional metabolites as initial seeds to trigger molecular networks with untargeted metabolomics MS/MS spectra. Highly efficient capture of the molecular families of seed metabolites could be achieved. Then, structural knowledge (MS fragmentation patterns and biotransformation reactions) was derived from spectra of seed metabolites or metabolic pathways. Then it was integrated into the KHMN and contributed to the attributes of nodes and edges. The integration of captured structural knowledge into the network increased the efficiency and accuracy of annotation propagation and benefitted the annotation of new metabolites
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