{"title":"转化毒理学中的定量结构-毒性关系","authors":"Svetoslav Slavov, Richard D. Beger","doi":"10.1016/j.cotox.2020.04.002","DOIUrl":null,"url":null,"abstract":"<div><p>During the past decade, quantitative structure–activity relationship (QSAR) enjoyed an ever-increasing application in various fields including translational sciences. This review summarizes the progress in data preprocessing, processing, and validation techniques as well as the standardization in reporting of QSARs and the legislative framework promoting the use of computational approaches as viable tools for reducing animal testing. Software products focused on prediction of translational end-points and recently published individual models are discussed briefly. Particular attention is given to challenges springing from the immense complexity of translational QSARs.</p></div>","PeriodicalId":93968,"journal":{"name":"Current opinion in toxicology","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.cotox.2020.04.002","citationCount":"4","resultStr":"{\"title\":\"Quantitative structure–toxicity relationships in translational toxicology\",\"authors\":\"Svetoslav Slavov, Richard D. Beger\",\"doi\":\"10.1016/j.cotox.2020.04.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>During the past decade, quantitative structure–activity relationship (QSAR) enjoyed an ever-increasing application in various fields including translational sciences. This review summarizes the progress in data preprocessing, processing, and validation techniques as well as the standardization in reporting of QSARs and the legislative framework promoting the use of computational approaches as viable tools for reducing animal testing. Software products focused on prediction of translational end-points and recently published individual models are discussed briefly. Particular attention is given to challenges springing from the immense complexity of translational QSARs.</p></div>\",\"PeriodicalId\":93968,\"journal\":{\"name\":\"Current opinion in toxicology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.6000,\"publicationDate\":\"2020-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.1016/j.cotox.2020.04.002\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current opinion in toxicology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2468202020300322\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current opinion in toxicology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2468202020300322","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Quantitative structure–toxicity relationships in translational toxicology
During the past decade, quantitative structure–activity relationship (QSAR) enjoyed an ever-increasing application in various fields including translational sciences. This review summarizes the progress in data preprocessing, processing, and validation techniques as well as the standardization in reporting of QSARs and the legislative framework promoting the use of computational approaches as viable tools for reducing animal testing. Software products focused on prediction of translational end-points and recently published individual models are discussed briefly. Particular attention is given to challenges springing from the immense complexity of translational QSARs.