4.7 Article

Identification of hepatoprotective traditional Chinese medicines based on the structure-activity relationship, molecular network, and machine learning techniques

期刊

FRONTIERS IN PHARMACOLOGY
卷 13, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fphar.2022.969979

关键词

machine learning; drug discovery; traditional Chinese medicine; predictive model; molecular network; hepatoprotection; structure-activity relationship

资金

  1. National Natural Science Foundation of China
  2. Natural Science Foundation of Zhejiang Province [82104540]
  3. [LQ22H280015]

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The efforts to discover potential hepatoprotective drugs from Traditional Chinese Medicine (TCM) are critical for alleviating the burden of liver diseases. This study established a large-scale dataset focused on TCM-induced hepatoprotection and developed an in silico model for predicting the hepatoprotective activity of ingredients derived from TCMs. The accuracy of the model exceeded 85%, demonstrating the technical soundness and reliability of the methods used.
The efforts focused on discovering potential hepatoprotective drugs are critical for relieving the burdens caused by liver diseases. Traditional Chinese medicine (TCM) is an important resource for discovering hepatoprotective agents. Currently, there are hundreds of hepatoprotective products derived from TCM available in the literature, providing crucial clues to discover novel potential hepatoprotectants from TCMs based on predictive research. In the current study, a large-scale dataset focused on TCM-induced hepatoprotection was established, including 676 hepatoprotective ingredients and 205 hepatoprotective TCMs. Then, a comprehensive analysis based on the structure-activity relationship, molecular network, and machine learning techniques was performed at molecular and holistic TCM levels, respectively. As a result, we developed an in silico model for predicting the hepatoprotective activity of ingredients derived from TCMs, in which the accuracy exceeded 85%. In addition, we originally proposed a material basis and a drug property-based approach to identify potential hepatoprotective TCMs. Consequently, a total of 12 TCMs were predicted to hold potential hepatoprotective activity, nine of which have been proven to be beneficial to the liver in previous publications. The high rate of consistency between our predictive results and the literature reports demonstrated that our methods were technically sound and reliable. In summary, systematical predictive research focused on the hepatoprotection of TCM was conducted in this work, which would not only assist screening of potential hepatoprotectants from TCMs but also provide a novel research mode for discovering the potential activities of TCMs.

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