{"title":"Global classification model for acute toxicity of organic compounds towards Tetrahymena pyriformis","authors":"Xinliang Yu , Zekai Zhang , Hanlu Wang","doi":"10.1016/j.psep.2024.10.108","DOIUrl":null,"url":null,"abstract":"<div><div>Predicting the toxic categories for organics towards <em>Tetrahymena pyriformis</em> is a challenging task. A subset of molecular descriptors reflecting structural characteristics such as molecular size, hydrophilicity, polarity, hydrogen bonds, activated multiple halogen substituents and activated carbon–carbon double bonds, were used for developing global quantitative structure–toxicity relationship models for 1792 toxic chemicals (pIGC<sub>50</sub>) in <em>Tetrahymena pyriformis.</em> By utilizing the random forest algorithm, a nine–descriptor classification model was established, which yielded accuracy, sensitivity and specificity higher than 91 % for chemicals with low toxicity classified as Class 0 (pIGC<sub>50</sub> < 3.4) and with high toxicity classified as Class 1 (pIGC<sub>50</sub> ≥ 3.4) in the test set. This classification model based on a larger applicability domain provides a potential tool for predicting the toxic categories for organic compounds towards <em>Tetrahymena pyriformis</em>.</div></div>","PeriodicalId":20743,"journal":{"name":"Process Safety and Environmental Protection","volume":"192 ","pages":"Pages 1221-1227"},"PeriodicalIF":6.9000,"publicationDate":"2024-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Process Safety and Environmental Protection","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S095758202401396X","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Predicting the toxic categories for organics towards Tetrahymena pyriformis is a challenging task. A subset of molecular descriptors reflecting structural characteristics such as molecular size, hydrophilicity, polarity, hydrogen bonds, activated multiple halogen substituents and activated carbon–carbon double bonds, were used for developing global quantitative structure–toxicity relationship models for 1792 toxic chemicals (pIGC50) in Tetrahymena pyriformis. By utilizing the random forest algorithm, a nine–descriptor classification model was established, which yielded accuracy, sensitivity and specificity higher than 91 % for chemicals with low toxicity classified as Class 0 (pIGC50 < 3.4) and with high toxicity classified as Class 1 (pIGC50 ≥ 3.4) in the test set. This classification model based on a larger applicability domain provides a potential tool for predicting the toxic categories for organic compounds towards Tetrahymena pyriformis.
期刊介绍:
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