文献类型：期刊文献

英文题名：In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts

作者：Yang, Hongbin[1];Sun, Lixia[1];Li, Weihua[1];Liu, Guixia[1];Tang, Yun[1]

机构：[1]East China Univ Sci & Technol, Sch Pharm, Shanghai Key Lab New Drug Design, Shanghai, Peoples R China

年份：2018

卷号：6

外文期刊名：FRONTIERS IN CHEMISTRY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000425505200001)】；

基金：This work was supported by the National Key Research and Development Program of China (Grant 2016YFA0502304), the National Natural Science Foundation of China (Grants 81373329 and 81673356) and the 863 Project (Grant 2012AA020308).

语种：英文

外文关键词：drug safety; chemical toxicity; drug design; machine learning; structural alerts

摘要：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.