Issue 4, 2023

A machine learning-integrated stepwise method to discover novel anti-obesity phytochemicals that antagonize the glucocorticoid receptor

Abstract

As a type of stress hormone, glucocorticoids (GCs) affect numerous physiological pathways by binding to the glucocorticoid receptor (GR) and regulating the transcription of various genes. However, when GCs are dysregulated, the resulting hypercortisolism may contribute to various metabolic disorders, including obesity. Thus, attempts have been made to discover potent GR antagonists that can reverse excess-GC-related metabolic diseases. Phytochemicals are a collection of valuable bioactive compounds that are known for their wide variety of chemotypes. Recently, various computational methods have been developed to obtain active phytochemicals that can modulate desired target proteins. In this study, we developed a workflow comprising two consecutive quantitative structure–activity relationship-based machine learning models to discover novel GR-antagonizing phytochemicals. These two models collectively identified 65 phytochemicals that bind to and antagonize GR. Of these, nine commercially available phytochemicals were validated for GR-antagonist and anti-obesity activities. In particular, we confirmed that demethylzeylasteral, a phytochemical of the Tripterygium wilfordii Radix, exhibits potent anti-obesity activity in vitro through GR antagonism.

Graphical abstract: A machine learning-integrated stepwise method to discover novel anti-obesity phytochemicals that antagonize the glucocorticoid receptor

Supplementary files

Article information

Article type
Paper
Submitted
12 Nov 2022
Accepted
14 Jan 2023
First published
30 Jan 2023

Food Funct., 2023,14, 1869-1883

A machine learning-integrated stepwise method to discover novel anti-obesity phytochemicals that antagonize the glucocorticoid receptor

S. H. Shin, G. Hur, N. R. Kim, J. H. Y. Park, K. W. Lee and H. Yang, Food Funct., 2023, 14, 1869 DOI: 10.1039/D2FO03466B

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