A systematic approach to identify therapeutic effects of natural products based on human metabolite information

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Background: Natural products have been widely investigated in the drug development field. Their traditional use cases as medicinal agents and their resemblance of our endogenous compounds show the possibility of new drug development. Many researchers have focused on identifying therapeutic effects of natural products, yet the resemblance of natural products and human metabolites has been rarely touched. Methods: We propose a novel method which predicts therapeutic effects of natural products based on their similarity with human metabolites. In this study, we compare the structure, target and phenotype similarities between natural products and human metabolites to capture molecular and phenotypic properties of both compounds. With the generated similarity features, we train support vector machine model to identify similar natural product and human metabolite pairs. The known functions of human metabolites are then mapped to the paired natural products to predict their therapeutic effects. Results: With our selected three feature sets, structure, target and phenotype similarities, our trained model successfully paired similar natural products and human metabolites. When applied to the natural product derived drugs, we could successfully identify their indications with high specificity and sensitivity. We further validated the found therapeutic effects of natural products with the literature evidence. Conclusions: These results suggest that our model can match natural products to similar human metabolites and provide possible therapeutic effects of natural products. By utilizing the similar human metabolite information, we expect to find new indications of natural products which could not be covered by previous in silico methods.
Publisher
BIOMED CENTRAL LTD
Issue Date
2018-06
Language
English
Article Type
Article; Proceedings Paper
Citation

BMC BIOINFORMATICS, v.19

ISSN
1471-2105
DOI
10.1186/s12859-018-2196-0
URI
http://hdl.handle.net/10203/244048
Appears in Collection
BiS-Journal Papers(저널논문)
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