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

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.

FIGURE 1

Schematic diagram of the systematic strategy for identifying hepatoprotective TCMs based on the structure–activity relationship, molecular network, and machine learning techniques. RSs, representative substructures.

FIGURE 2

Efficacy category analysis. (A) Efficacy category of the hepatoprotective and hepatotoxic TCMs. (B) Overlap between hepatoprotective and hepatotoxic TCMs.

FIGURE 3

Frequency distribution of hepatoprotective and non-hepatoprotective TCMs in four properties.

FIGURE 4

Frequency distribution of hepatoprotective and non-hepatoprotective TCMs in five flavors.

FIGURE 5

Frequency distribution of hepatoprotective and non-hepatoprotective TCMs in channel tropism.

FIGURE 6

“TCM-ingredient” network focused on hepatoprotection. The hepatoprotective TCMs and the hepatoprotective ingredients were displayed by green and red nodes, respectively. If a TCM and an ingredient were connected by a gray line, it indicated that the TCM contained the ingredient.

FIGURE 7

Identification of the hepatoprotective ingredients in Chai hu (A), Ju hua (B), Sang ye (C), and Yin xing ye (D) based on the hepatoprotective “TCM-ingredient” network.

FIGURE 8

Undetermined “TCM-ingredient” network. Green and red nodes represented the TCMs and the hepatoprotective ingredients, respectively. The gray lines connecting the nodes indicated that the TCMs contain the ingredients.

FIGURE 9

Cluster analysis based on drug property. The samples with the prefixes of P and T indicated the hepatoprotective TCMs and the non-hepatoprotective TCMs, respectively. U1–U25 represented the 25 samples to be tested.