Suppr超能文献

考虑到硝基芳香族化合物的分解机制,开发了一种用于预测其热稳定性的定量构效关系模型。

Development of a QSPR model for predicting thermal stabilities of nitroaromatic compounds taking into account their decomposition mechanisms.

机构信息

Institut National de l'Environnement Industriel et des Risques (INERIS), Parc Technologique Alata, BP2, 60550 Verneuil-en-Halatte, France.

出版信息

J Mol Model. 2011 Oct;17(10):2443-53. doi: 10.1007/s00894-010-0908-0. Epub 2010 Dec 21.

Abstract

The molecular structures of 77 nitroaromatic compounds have been correlated to their thermal stabilities by combining the quantitative structure-property relationship (QSPR) method with density functional theory (DFT). More than 300 descriptors (constitutional, topological, geometrical and quantum chemical) have been calculated, and multilinear regressions have been performed to find accurate quantitative relationships with experimental heats of decomposition (-ΔH). In particular, this work demonstrates the importance of accounting for chemical mechanisms during the selection of an adequate experimental data set. A reliable QSPR model that presents a strong correlation with experimental data for both the training and the validation molecular sets (R (2) = 0.90 and 0.84, respectively) was developed for non-ortho-substituted nitroaromatic compounds. Moreover, its applicability domain was determined, and the model's predictivity reached 0.86 within this applicability domain. To our knowledge, this work has produced the first QSPR model, developed according to the OECD principles of regulatory acceptability, for predicting the thermal stabilities of energetic compounds.

摘要

通过将定量构效关系(QSPR)方法与密度泛函理论(DFT)相结合,对 77 种硝基芳香族化合物的分子结构与其热稳定性进行了关联。计算了超过 300 个描述符(结构、拓扑、几何和量子化学),并进行了多元回归,以找到与实验分解热(-ΔH)的准确定量关系。特别是,这项工作证明了在选择合适的实验数据集时,考虑化学机制的重要性。为非邻位取代的硝基芳香族化合物开发了一个可靠的 QSPR 模型,该模型与训练和验证分子集的实验数据具有很强的相关性(分别为 R(2)= 0.90 和 0.84)。此外,还确定了该模型的适用域,并且在该适用域内,模型的预测性达到了 0.86。据我们所知,这是根据监管可接受性的 OECD 原则开发的第一个用于预测高能化合物热稳定性的 QSPR 模型。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验