In silico toxicity studies of traditional Chinese herbal medicine: A mini review

Abstract

With the availability of public databases that store compound-target/compound-toxicity information, and Traditional Chinese medicine (TCM) databases, in silico approaches are used in toxicity studies of TCM herbal medicine. Here, three in silico approaches for toxicity studies were reviewed, which include machine learning, network toxicology and molecular docking. For each method, its application and implementation e.g., single classifier vs. multiple classifier, single compound vs. multiple compounds, validation vs. screening, were explored. While these methods provide data-driven toxicity prediction that is validated in vitro and/or in vivo, it is still limited to single compound analysis. In addition, these methods are limited to several types of toxicity, with hepatotoxicity being the most dominant. Future studies involving the testing of combination of compounds on the front end i.e., to generate data for in silico modeling, and back end i.e., validate findings from prediction models will advance the in silico toxicity modeling of TCM compounds.

Keywords: Herbal medicine; In silico toxicity studies; Machine learning; Molecular docking; Network toxicology; Traditional Chinese medicine.