The recent explosion in gut microbiome research has demonstrated the importance of metabolite-target interactions in the development of different pathologies. This suggest that gut-targeted drugs modulating these interactions would provide a new drug modality besides that of systemically bioavailable small molecules, that could tap from this growing knowledge, and would have little distribution and safety issues. In the present work we analyze a large set of gut metabolites in comparison with serum metabolites and drugs. We find structural and physicochemical similarity between the serum metabolites and the drug sets, and dissimilarity with the gut metabolite set. In addition, we find that the inclusion of chemical class is necessary in order to appropriately understand gut permanence, in contrast to classical oral permeation models (e.g. rule-of-five). To help in gut-targeted drug design, we provide a simple scoring scheme for use in medicinal chemistry, plus a machine learning model to use in cheminformatic applications.
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