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定量构效关系模型的可再现性和适用性:以羟基自由基反应模型的速率常数应用于多溴二苯醚和(苯并)三唑为例。

QSAR model reproducibility and applicability: a case study of rate constants of hydroxyl radical reaction models applied to polybrominated diphenyl ethers and (benzo-)triazoles.

机构信息

Department of Structural and Functional Biology, University of Insubria, Varese, Italy.

出版信息

J Comput Chem. 2011 Aug;32(11):2386-96. doi: 10.1002/jcc.21820. Epub 2011 May 3.

Abstract

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.

摘要

OECD 定量构效关系 (QSAR) 模型验证的三个核心原则的至关重要性在一个关于挥发性有机化合物 (VOCs) 通过 OH 在对流层中降解的案例研究中得到了强调,该案例研究应用于 CADASTER 的两个化学类别(多溴二苯醚 (PBDEs) 和(苯并)三唑)。如果没有实验数据,任何 QSAR 模型在化学品中的应用在很大程度上取决于用户对模型的可重复性。明确算法的可重复性(OECD 原则 2)通过基于分子描述符计算的 DRAGON 软件更新版本以及一些免费提供的在线描述符重新开发 MLR 模型来保证。遗传算法已经证实了其能够独立于输入变量池选择最具信息量描述符的能力。GA 选择的描述符对未用于模型开发的化学品进行建模的能力通过三种不同的分割(随机响应、K-ANN 和 K-均值聚类)得到验证,从而确保了新模型的外部预测性,独立于训练/预测集组成(OECD 原则 5)。通过使用本文提出的新模型与 EPI Suite 获得的预测值比较,检查结构适用性域的相关性对于 CADASTER 化学品的预测变得非常明显。

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