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.

Machine learning in TCM with natural products and molecules: current status and future perspectives

“…Hu [ 57 ] used SVM and in vitro screening to predict and validate the risk of idiosyncratic drug-induced liver injuries caused by the natural products in Polygonum multiflorum Thunb, and provided a powerful tool to screen large datasets for toxicants. He [ 58 ] established a large-scale dataset focused on TCM-induced hepatoprotection to train machine learning models such as RF and voting models. Their work helped screen potential hepatoprotectants from natural products.…”

“…Natural product development Deep neurol network [47,50], RF [53,54,58,59,93], SVM [51,53,54,57,59,93], DT [59,93], neural network [53,59] RF was better than SVM, neurol network and DT in screening hepatotoxic compounds [59]. RF model is more accurate than SVM and DT in identifying molecular characteristics of natural product compounds with the meridians of TCM [93] Disease diagnosis SVM [10,61,66,[81][82][83], DT [68,[81][82][83], neural network [45, 61-63, 65, 82, 83], RF [61,64,67,82,83], CNN [64,67,[70][71][72][73][74][75][76][77][78]81], RNN…”

The complete plastome of Campsis radicans (L.) Bureau 1864 and its phylogenetic analysis

“…For instance, C. radicans flowers are a rich source of liver-protective compounds, exhibiting similar properties to other hepatoprotective TCMs (He et al. 2022 ). Despite its pharmaceutical potential, the genetic diversity of C. radicans remains relatively unexplored.…”