酚类物质毒性作用方式的多变量判别

A. Aptula, T. Netzeva, I. Valkova, M. Cronin, T. Schultz, R. Kühne, G. Schüürmann
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引用次数: 101

摘要

采用逐步线性判别分析(LDA)对已获得的221种酚类化合物对梨形四膜虫(Tetrahymena pyriformis)的毒性机制进行分类。根据结构规律,将化合物先验地分为极性麻醉剂、弱酸呼吸解偶剂、亲电试剂和软亲电试剂四种机制。疏水性(带或不带电离校正)(log K o w, log D o w),酸度常数(pK a),前沿轨道能(E L u M o, E H o M o)和氢键供体和受体计数作为分子描述符。采用3-6个变量的LDA模型对四种机制的分类总体正确率达到86-89%,对呼吸解偶联剂和亲电试剂的分类结果差异较大。对于后者,开发了一个单独的模型,可以非常准确地区分代谢激活的化合物和具有不同机制的化合物。通过互补子集构建的LDA模型对模拟的外部预测进行评估,从而对模型进行验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multivariate Discrimination between Modes of Toxic Action of Phenols
A set of 221 phenols, for which toxicity data to the ciliate Tetrahymena pyriformis were available, was subjected to stepwise linear discriminant analysis (LDA) in order to classify their toxic mechanisms of action. The compounds were a priori grouped into the following four mechanisms according to structural rules: polar narcotics, weak acid respiratory uncouplers, pro-electrophiles and soft electrophiles. Hydrophobicity with and without correction for ionisation (log K o w , log D o w u), acidity constant (pK a ), frontier orbital energies (E L U M O , E H O M O ) and hydrogenbond donor and acceptor counts were used as molecular descriptors. LDA models employing 3-6 variables achieved 86-89% overall correct classification of the four mechanisms, with more varied performance for respiratory uncouplers and pro-electrophiles. For the latter, a separate model was developed that discriminated compounds undergoing metabolic activation from compounds with different mechanisms very accurately. Model validation was performed by evaluating the simulated external prediction through LDA models built from complementary subsets.
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