Characterization of Mixtures Part 1: Prediction of Infinite-Dilution Activity Coefficients Using Neural Network-Based QSPR Models
Characterization of Mixtures Part 1: Prediction of Infinite-Dilution Activity Coefficients Using Neural Network-Based QSPR Models
复制标题
DOI：
10.1002/qsar.200860022
复制
发表时间：
2008-12-01
期刊：
影响因子：
--
通讯作者：
Livingstone, David J.
中科院分区：
文献类型：
--
作者：
Ajmani, Subhash;Rogers, Stephen C.;Livingstone, David J.
建立混合物的QSAR/QSPR模型的主要问题在于它们的表征。结果表明，用简单的摩尔分数加权物理化学描述符即可建立二元液体混合物密度的QSPR模型。然而，从解释结果模型的角度来看，这些参数并不令人满意。在这篇论文中，已经研究了一种基于替代机理的混合物表征方法。已经表明，虽然不可能使用这些描述符来建立显着的线性模型。已有可能构建令人满意的人工神经网络模型。讨论了这些模型的性能和单个描述符的重要性。
The major problem in building QSAR/QSPR models for mixtures lies in their characterization. It has been shown that it is possible to construct QSPR models for the density of binary liquid mixtures using simple mole fraction weighted physicochemical descriptors. Such parameters are unsatisfactory;, however, from the point of view of interpretation of the resultant models. In this paper, an alternative mechanism-based approach to the characterization of mixtures has been investigated. It has been shown that while it is not possible to build significant linear models using these descriptors. it has been possible to construct satisfactory artificial neural network models. The performance of these models and the importance of the individual descriptors are discussed.