首页> 外文期刊>Journal of Computational Chemistry: Organic, Inorganic, Physical, Biological >QSAR model reproducibility and applicability: A case study of rate constants of hydroxyl radical reaction models applied to polybrominated diphenyl ethers and (benzo-)triazoles
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QSAR model reproducibility and applicability: A case study of rate constants of hydroxyl radical reaction models applied to polybrominated diphenyl ethers and (benzo-)triazoles

机译:QSAR模型的可重复性和适用性:应用于多溴联苯醚和(苯并-)三唑的羟基自由基反应模型速率常数的案例研究

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摘要

The crucial importance of the three central OECD principles for quantitative structure-activity relationship (QSAR) model validation is highlighted in a case study of tropospheric degradation of volatile organic compounds (VOCs) by OH, applied to two CADASTER chemical classes (PBDEs and (benzo-)triazoles). The application of any QSAR model to chemicals without experimental data largely depends on model reproducibility by the user. The reproducibility of an unambiguous algorithm (OECD Principle 2) is guaranteed by redeveloping MLR models based on both updated version of DRAGON software for molecular descriptors calculation and some freely available online descriptors. The Genetic Algorithm has confirmed its ability to always select the most informative descriptors independently on the input pool of variables. The ability of the GA-selected descriptors to model chemicals not used in model development is verified by three different splittings (random by response, K-ANN and K-means clustering), thus ensuring the external predictivity of the new models, independently of the training/prediction set composition (OECD Principle 5). The relevance of checking the structural applicability domain becomes very evident on comparing the predictions for CADASTER chemicals, using the new models proposed herein, with those obtained by EPI Suite.
机译:在对流层对OH对挥发性有机化合物(VOC)进行降解的案例研究中,强调了三项主要OECD原理对于定量构效关系(QSAR)模型验证的至关重要性,并将其应用于两种CADASTER化学类别(PBDEs和(苯并) -)三唑)。没有实验数据的任何QSAR模型在化学品上的应用很大程度上取决于用户的模型可重复性。通过基于用于分子描述符计算的DRAGON软件的更新版本和一些免费的在线描述符,重新开发MLR模型,可以确保明确算法(OECD原理2)的可重复性。遗传算法已证实其始终能够独立于变量输入池选择信息量最大的描述符的能力。由GA选择的描述符对模型开发中未使用的化学物质进行建模的能力已通过三种不同的分裂方法进行了验证(按响应随机,K-ANN和K-均值聚类),从而确保了新模型的外部可预测性,而与培训/预测集组成(经合组织原则5)。通过比较使用本文提出的新模型对CADASTER化学品的预测与EPI Suite获得的预测,检查结构适用性领域的相关性变得非常明显。

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