Development of binary classification models for grouping hydroxylated polychlorinated biphenyls into active and inactive thyroid hormone receptor agonists.

IF 2.3 3区 环境科学与生态学 Q3 CHEMISTRY, MULTIDISCIPLINARY SAR and QSAR in Environmental Research Pub Date : 2023-04-01 DOI:10.1080/1062936X.2023.2207039
L K Akinola, A Uzairu, G A Shallangwa, S E Abechi
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Abstract

Some adverse effects of hydroxylated polychlorinated biphenyls (OH-PCBs) in humans are presumed to be initiated via thyroid hormone receptor (TR) binding. Due to the trial-and-error approach adopted for OH-PCB selection in previous studies, experiments designed to test the TR binding hypothesis mostly utilized inactive OH-PCBs, leading to considerable waste of time, effort and other material resources. In this paper, linear discriminant analysis (LDA) and binary logistic regression (LR) were used to develop classification models to group OH-PCBs into active and inactive TR agonists using radial distribution function (RDF) descriptors as predictor variables. The classifications made by both LDA and LR models on the training set compounds resulted in an accuracy of 84.3%, sensitivity of 72.2% and specificity of 90.9%. The areas under the ROC curves, constructed with the training set data, were found to be 0.872 and 0.880 for LDA and LR models, respectively. External validation of the models revealed that 76.5% of the test set compounds were correctly classified by both LDA and LR models. These findings suggest that the two models reported in this paper are good and reliable for classifying OH-PCB congeners into active and inactive TR agonists.

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羟基化多氯联苯为活性和非活性甲状腺激素受体激动剂的二元分类模型的建立。
羟基化多氯联苯(OH-PCBs)对人体的一些不良影响被认为是通过甲状腺激素受体(TR)结合而引发的。由于以往的研究采用试错法选择OH-PCB,验证TR结合假说的实验大多采用非活性OH-PCB,导致大量时间、精力和其他物质资源的浪费。本文以径向分布函数(RDF)描述符为预测变量,采用线性判别分析(LDA)和二元逻辑回归(LR)建立分类模型,将oh - pcb分为活性和非活性TR激动剂。LDA和LR模型对训练集化合物的分类准确率为84.3%,灵敏度为72.2%,特异性为90.9%。用训练集数据构建的ROC曲线下面积,LDA模型为0.872,LR模型为0.880。模型的外部验证表明,76.5%的测试集化合物被LDA和LR模型正确分类。这些结果表明,本文报道的两种模型对于将OH-PCB同系物划分为活性和非活性TR激动剂是良好和可靠的。
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来源期刊
CiteScore
5.20
自引率
20.00%
发文量
78
审稿时长
>24 weeks
期刊介绍: SAR and QSAR in Environmental Research is an international journal welcoming papers on the fundamental and practical aspects of the structure-activity and structure-property relationships in the fields of environmental science, agrochemistry, toxicology, pharmacology and applied chemistry. A unique aspect of the journal is the focus on emerging techniques for the building of SAR and QSAR models in these widely varying fields. The scope of the journal includes, but is not limited to, the topics of topological and physicochemical descriptors, mathematical, statistical and graphical methods for data analysis, computer methods and programs, original applications and comparative studies. In addition to primary scientific papers, the journal contains reviews of books and software and news of conferences. Special issues on topics of current and widespread interest to the SAR and QSAR community will be published from time to time.
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