Risk assessment of organic aromatic compounds to Tetrahymena pyriformis in environmental protection by a simple QSAR model

被引:20
作者
Keshavarz, Mohammad Hossein [1 ]
Shirazi, Zeinab [1 ]
Sheikhabadi, Parvin Kiani [1 ]
机构
[1] Malek Ashtar Univ Technol, Fac Appl Sci, Tehran, Iran
关键词
Toxicity; Tetrahymena pyriformis; Organic aromatic compound; Molecular structure; QSAR; STRUCTURE-TOXICITY RELATIONSHIPS; DIFFERENT VALIDATION CRITERIA; REAL EXTERNAL PREDICTIVITY; IN-SILICO PREDICTION; NITROAROMATIC COMPOUNDS; ACCURATE PREDICTION; LINEAR-REGRESSION; PHENOLS; METRICS; QSPR;
D O I
10.1016/j.psep.2021.04.011
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
A new Quantitative Structure-Activity Relationship model is introduced for reliable prediction of the toxicity of organic aromatic compounds based on the logarithm of 50 % growth inhibitory concentration of Tetrahymena pyriformis (log(IGC50-1)), which have extensive use in ecotoxicology and environmental safety applications. The largest experimental data set of log(IGC50-1) for 892 organic aromatic compounds is used to derive and test the new model. A core correlation based on additive variables is introduced by the number of nitro groups, carbon and halogen atoms as well as some specific polar groups and molecular weight. An improved correlation based on two non-additive correcting functions is developed for the increment of the reliability of the core correlation. The reliability of the improved correlation is tested and compared with two of the best available methods, which require complex descriptors. The predicted results for 661 and 231 of training and test sets have been confirmed by internal and external validations. The values of correlation coefficient (R2), mean error (ME), root mean squared error (RMSE), and maximum of errors (Max Error) for 661/231 of training/test aromatic compounds are 0.8442/0.7771, 0.0000/0.0149, 0.3166/0.3603, and 0.9732/0.9825, respectively, which are good results as compared to extra complex models with lower reported data. Various statistical parameters confirm the goodnessof-fit, high reliability, precision, and accuracy of the novel model. (c) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:137 / 147
页数:11
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