Yang Hongbin, Sun Lixia, Li Weihua, Liu Guixia, Tang Yun
Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, China.
Front Chem. 2018 Feb 20;6:30. doi: 10.3389/fchem.2018.00030. eCollection 2018.
During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.
在药物研发过程中，安全性始终是最重要的问题，包括各种毒性和药物不良反应，这些都应在临床前和临床试验阶段进行评估。这篇综述文章首先简要介绍了用于药物设计中化学毒性预测的计算方法，包括机器学习方法和结构警示。机器学习方法已广泛应用于定性分类和定量回归研究，而结构警示可被视为先导优化的补充工具。本文重点介绍了针对各种毒性建立的预测模型的最新进展。还提供了可用的数据库和网络服务器。尽管这些方法和模型对药物设计非常有帮助，但未来药物安全性评估仍存在一些挑战和局限性有待改进。
