文献类型：期刊文献

英文题名：Computational Approaches to Identify Structural Alerts and Their Applications in Environmental Toxicology and Drug Discovery

作者：Yang, Hongbin[1];Lou, Chaofeng[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 200237, Peoples R China

年份：2020

卷号：33

期号：6

起止页码：1312

外文期刊名：CHEMICAL RESEARCH IN TOXICOLOGY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000541877700005)】；

基金：We sincerely thank Dr. Philip W. Lee from DuPont and Peter Wright from the Bender group at the University of Cambridge for their valuable comments that significantly improved the quality of the manuscript. This work was supported by the National Key Research and Development Program (Grant 2016YFA0502304), the National Natural Science Foundation of China (Grants 81872800 and 81673356), and the 111 Project (Grant BP0719034).

语种：英文

摘要：Structural alerts are a simple and easy way to identify toxic compounds being widely used in environmental toxicology research and drug discovery. With the emergence of big data techniques in recent years and their applications in chemistry and toxicology, computational approaches have become a promising method to identify structural alerts. In this Review, we describe the recent progress in computational methods for identification of structural alerts and their applications in toxicology. Two major computational approaches, namely frequency analysis and interpretable machine learning models, are reviewed. Recent studies have shown that both approaches are superior to expert systems with respect to predictive capability. Methodologies for defining the applicability domain of such approaches are also reviewed, with their importance stemming from their ability to not only improve the predictive performance of structural alert models but also ensure the confidence of a prediction. In addition to toxicity prediction, structural alerts could be also used to explain quantitative structure-activity relationship models and guide lead optimization in drug discovery. Nevertheless, there are still some challenges to be solved, such as how to address the co-existence of several structural alerts in one molecule, how to directly compare computationally derived structural alerts with expert systems, and how to explore new mechanisms of toxicity.