Faculty of Pharmacy, Universiti Teknologi MARA Selangor, Puncak Alam Campus, 42300 Bandar Puncak Alam, Selangor, Malaysia.
Faculty of Pharmacy, Universiti Teknologi MARA Selangor, Puncak Alam Campus, 42300 Bandar Puncak Alam, Selangor, Malaysia; Collaborative Drug Discovery Research, Faculty of Pharmacy, Universiti Teknologi MARA Selangor, Puncak Alam Campus, 42300 Bandar Puncak Alam, Selangor, Malaysia.
Curr Opin Struct Biol. 2023 Jun;80:102588. doi: 10.1016/j.sbi.2023.102588. Epub 2023 Apr 5.
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.
随着存储化合物-靶标/化合物-毒性信息的公共数据库以及中药（TCM）数据库的出现，计算方法被用于 TCM 草药的毒性研究。本文综述了三种用于毒性研究的计算方法，包括机器学习、网络毒理学和分子对接。对于每种方法，都探讨了其应用和实施，例如，单分类器与多分类器、单化合物与多化合物、验证与筛选。虽然这些方法提供了经过体外和/或体内验证的基于数据的毒性预测，但仍仅限于单个化合物分析。此外，这些方法仅限于几种类型的毒性，其中肝毒性最为突出。未来的研究涉及在前端测试化合物组合，即生成用于计算建模的数据，以及在后端验证预测模型的结果，这将推进 TCM 化合物的计算毒性建模。
