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首页> 外文期刊>Combinatorial Chemistry _High Throughput Screening >The Use of Artificial Neural Networks for the Selection of the Most Appropriate Thermal Parameters and for the Classification of a Set of Phenylcarbamic Acid Derivates
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The Use of Artificial Neural Networks for the Selection of the Most Appropriate Thermal Parameters and for the Classification of a Set of Phenylcarbamic Acid Derivates

机译：人工神经网络用于选择最合适的热参数以及对一组苯基氨基甲酸衍生物的分类

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摘要

The objective of this work was to apply artificial neural networks (ANNs) to the classification group of 43 derivatives of phenylcarbamic acid. To find the appropriate clusters Kohonen topological maps were employed. As input data, thermal parameters obtained during DSC and TG analysis were used. Input feature selection (IFS) algorithms were used in order to give an estimate of the relative importance of various input variables. Additionally, sensitivity analysis was carried out to eliminate less important thermal variables. As a result, one classification model was obtained, which can assign our compounds to an appropriate class. Because the classes contain groups of molecules structurally related, it is possible to predict the structure of the compounds (for example the position of the substitution alkoxy group in the phenyl ring) on the basis of obtained parameters.
机译：这项工作的目的是将人工神经网络（ANN）应用于43种苯基氨基甲酸衍生物的分类。为了找到合适的集群，采用了Kohonen拓扑图。作为输入数据，使用在DSC和TG分析过程中获得的热参数。使用输入特征选择（IFS）算法来估计各种输入变量的相对重要性。另外，进行了灵敏度分析以消除不太重要的热变量。结果，获得了一个分类模型，可以将我们的化合物分配到适当的类别。由于这些类别包含在结构上相关的分子组，因此可以根据获得的参数预测化合物的结构（例如，苯环中取代烷氧基的位置）。

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