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

Shuaibing He; Yanfeng Yi; Diandong Hou; Xuyan Fu; Juan Zhang; Xiaochen Ru; Jinlu Xie; Juan Wang

doi:10.3389/fphar.2022.969979

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

Front Pharmacol. 2022 Aug 29:13:969979. doi: 10.3389/fphar.2022.969979. eCollection 2022.

Authors

Shuaibing He¹, Yanfeng Yi², Diandong Hou¹, Xuyan Fu¹, Juan Zhang³, Xiaochen Ru¹, Jinlu Xie¹, Juan Wang⁴

Affiliations

¹ Key Laboratory of Vector Biology and Pathogen Control of Zhejiang Province, School of Medicine, Huzhou University, Huzhou Central Hospital, Huzhou, China.
² Department of Life Sciences and Health, School of Science and Engineering, Huzhou College, Huzhou, China.
³ XinJiang Institute of Chinese Materia Medica and Ethnodrug, Urumqi, China.
⁴ School of Traditional Chinese Medicine, Zhejiang Pharmaceutical University, Ningbo, China.

Abstract

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.

Keywords: drug discovery; hepatoprotection; machine learning; molecular network; predictive model; structure–activity relationship; traditional Chinese medicine.