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The diverse metabolic pathways are fundamental to all living organisms, asthey harvest energy, synthesize biomass components, produce molecules tointeract with the microenvironment, and neutralize toxins. While discovery ofnew metabolites and pathways continues, the prediction of pathways for newmetabolites can be challenging. It can take vast amounts of time to elucidatepathways for new metabolites; thus, according to HMDB only 60% of metabolitesget assigned to pathways. Here, we present an approach to identify pathwaysbased on metabolite structure. We extracted 201 features from SMILESannotations, and identified new metabolites from PubMed abstracts and HMDB.After applying clustering algorithms to both groups of features, we quantifiedcorrelations between metabolites, and found the clusters accurately linked 92%of known metabolites to their respective pathways. Thus, this approach could bevaluable for predicting metabolic pathways for new metabolites.

Predicting pathways for old and new metabolites through clustering