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ORIGINAL RESEARCH article

Front. Pharmacol.
Sec. Experimental Pharmacology and Drug Discovery
Volume 15 - 2024 | doi: 10.3389/fphar.2024.1500865

Investigating the Anti-Obesity Potential of Nelumbo nucifera Leaf Bioactive Compounds thr ough Machine lear ning and Computational Biology Methods

Provisionally accepted
  • Peking Union Medical College Hospital (CAMS), Beijing, China

The final, formatted version of the article will be published soon.

    Obesity, a growing global health concern, is linked to severe ailments such as cardiovascular diseases, type 2 diabetes, cancer, and neuropsychiatric disorders. Conventional pharmacological treatments often have significant side effects, highlighting the need for safer alternatives. Traditional Chinese Medicine (TCM) offers potential solutions, with plant extracts like those from Nelumbo nucifera leaves showing promise due to their historical use and minimal side effects. This study employs a comprehensive computational biology approach to explore the anti-obesity effects of Nelumbo nucifera Leaf Bioactive Compounds. Sixteen active compounds from Nelumbo nucifera leaves were screened using the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP). Clustering analysis identified three representative molecules, and network pharmacology pinpointed PPARG as a common target gene. Molecular docking and machine learning models were used for inhibitors screening, and molecular dynamics simulations were futher used to investigate the inhibitory effects and mechanisms of these molecules on PPARG. Subsequent cellular assays confirmed the ability of Sitogluside to reduce lipid accumulation and triglyceride levels in 3T3-L1 cells, underscoring its potential as an effective and safer obesity treatment. Our findings provide a molecular basis for the anti-obesity properties of Nelumbo nucifera Leaf Bioactive Compounds and pave the way for developing new, effective, and safer obesity treatments.

    Keywords: Nelumbo nucifera leaves, Network Pharmacology, machine learning, Obesity, Molecular Dynamics Simulation

    Received: 24 Sep 2024; Accepted: 02 Dec 2024.

    Copyright: © 2024 Huang, Liu, Cao, Chen, Li and Yu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jianchun Yu, Peking Union Medical College Hospital (CAMS), Beijing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.