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首页> 外文期刊>Chemistry: A European journal >Neural networks as a tool to classify compounds according to aromaticity criteria
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Neural networks as a tool to classify compounds according to aromaticity criteria

机译：神经网络作为根据芳香性标准对化合物进行分类的工具

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Aromaticity is a fundamental concept in chemistry, with many theoretical and practical implications. Although most organic compounds can be categorized as aromatic, non-aromatic, or antiaromatic, it is often difficult to classify borderline compounds as well as to quantify this property. Many aromaticity criteria have been proposed, although none of them gives an entirely satisfactory solution. The inability to fully arrange organic compounds according to a single criterion arises from the fact that aromaticity is a multidimensional phenomenon. Neural networks are computational techniques that allow one to treat a large amount of data, thereby reducing the dimensionality of the input set to a bidimensional output. We present the successful applications of Kohonen's self-organizing maps to classify organic compounds according to aromaticity criteria, showing a good correlation between the aromaticity of a compound and its placement in a particular neuron. Although the input data for the training of the network were different aromaticity criteria (stabilization energy, diamagnetic susceptibility, NICS, NICS(1), and HOMA) for five-membered heterocycles, the method can be extended to other organic compounds. Some useful features of this method are: 1) it is very fast, requiring less than one minute of computational time to place a new compound in the map; 2) the placement of the different compounds in the map is conveniently visualized; 3) the position of a compound in the map depends on its aromatic character, thus allowing us to establish a quantitative scale of aromaticity, based on Euclidean distances between neurons, 4) it has predictive power. Overall, the results reported herein constitute a significant contribution to the longstanding debate on the quantitative treatment of aromaticity.
机译：芳香性是化学中的基本概念，具有许多理论和实践意义。尽管大多数有机化合物可分为芳香族，非芳香族或抗芳香族化合物，但通常很难对临界化合物进行分类并对其性质进行定量。尽管没有一个给出完全令人满意的解决方案，但是已经提出了许多芳香性标准。由于芳香性是多维现象，因此无法根据单一标准完全排列有机化合物。神经网络是一种计算技术，可让人们处理大量数据，从而降低了输入集到二维输出的维数。我们介绍了Kohonen的自组织图的成功应用，可根据芳香性标准对有机化合物进行分类，显示出化合物的芳香性与其在特定神经元中的位置之间的良好相关性。尽管用于网络训练的输入数据是五元杂环的不同芳香性标准（稳定能，抗磁化率，NICS，NICS（1）和HOMA），但该方法可以扩展到其他有机化合物。此方法的一些有用功能是：1）非常快，只需不到一分钟的计算时间即可在地图上放置新化合物。 2）方便地查看地图中不同化合物的位置; 3）化合物在图中的位置取决于其芳香特性，因此使我们能够基于神经元之间的欧几里得距离建立芳香的定量尺度，4）它具有预测能力。总体而言，本文报道的结果为有关芳香性定量处理的长期辩论做出了重要贡献。

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