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基于Choquet积分的模糊分子表征:当根据原子/键贡献之间的依赖性(局部重叠价指数/局部重叠电子指数)计算全局定义时。

Choquet integral-based fuzzy molecular characterizations: when global definitions are computed from the dependency among atom/bond contributions (LOVIs/LOEIs).

作者信息

García-Jacas César R, Cabrera-Leyva Lisset, Marrero-Ponce Yovani, Suárez-Lezcano José, Cortés-Guzmán Fernando, Pupo-Meriño Mario, Vivas-Reyes Ricardo

机构信息

Instituto de Química, Universidad Nacional Autónoma de México (UNAM), Ciudad de México, México.

Grupo de Investigación de Inteligencia Artificial (AIRES), Facultad de Informática, Universidad de Camagüey, Camagüey, Cuba.

出版信息

J Cheminform. 2018 Oct 25;10(1):51. doi: 10.1186/s13321-018-0306-7.

Abstract

BACKGROUND

Several topological (2D) and geometric (3D) molecular descriptors (MDs) are calculated from local vertex/edge invariants (LOVIs/LOEIs) by performing an aggregation process. To this end, norm-, mean- and statistic-based (non-fuzzy) operators are used, under the assumption that LOVIs/LOEIs are independent (orthogonal) values of one another. These operators are based on additive and/or linear measures and, consequently, they cannot be used to encode information from interrelated criteria. Thus, as LOVIs/LOEIs are not orthogonal values, then non-additive (fuzzy) measures can be used to encode the interrelation among them.

RESULTS

General approaches to compute fuzzy 2D/3D-MDs from the contribution of each atom (LOVIs) or covalent bond (LOEIs) within a molecule are proposed, by using the Choquet integral as fuzzy aggregation operator. The Choquet integral-based operator is rather different from the other operators often used for the 2D/3D-MDs calculation. It performs a reordering step to fuse the LOVIs/LOEIs according to their magnitudes and, in addition, it considers the interrelation among them through a fuzzy measure. With this operator, fuzzy definitions can be derived from traditional or recent MDs; for instance, fuzzy Randic-like connectivity indices, fuzzy Balaban-like indices, fuzzy Kier-Hall connectivity indices, among others. To demonstrate the feasibility of using this operator, the QuBiLS-MIDAS 3D-MDs were used as study case and, as a result, a module was built into the corresponding software to compute them ( http://tomocomd.com/qubils-midas ). Thus, it is the only software reported in the literature that can be employed to determine Choquet integral-based fuzzy MDs. Moreover, regression models were created on eight chemical datasets. In this way, a comparison between the results achieved by the models based on the non-fuzzy QuBiLS-MIDAS 3D-MDs with regard to the ones achieved by the models based on the fuzzy QuBiLS-MIDAS 3D-MDs was made. As a result, the models built with the fuzzy QuBiLS-MIDAS 3D-MDs achieved the best performance, which was statistically corroborated through the Wilcoxon signed-rank test.

CONCLUSIONS

All in all, it can be concluded that the Choquet integral constitutes a prominent alternative to compute fuzzy 2D/3D-MDs from LOVIs/LOEIs. In this way, better characterizations of the compounds can be obtained, which will be ultimately useful in enhancing the modelling ability of existing traditional 2D/3D-MDs.

摘要

背景

通过执行聚合过程,从局部顶点/边不变量(局部顶点不变量/局部边不变量)计算出几种拓扑(二维)和几何(三维)分子描述符(MDs)。为此,在假设局部顶点不变量/局部边不变量彼此独立(正交)的情况下,使用基于范数、均值和统计的(非模糊)算子。这些算子基于加法和/或线性度量,因此,它们不能用于编码来自相关标准的信息。因此,由于局部顶点不变量/局部边不变量不是正交值,那么可以使用非加法(模糊)度量来编码它们之间的相互关系。

结果

提出了通过使用Choquet积分作为模糊聚合算子,从分子内每个原子(局部顶点不变量)或共价键(局部边不变量)的贡献计算模糊二维/三维分子描述符的一般方法。基于Choquet积分的算子与常用于二维/三维分子描述符计算的其他算子有很大不同。它执行一个重新排序步骤,根据局部顶点不变量/局部边不变量的大小融合它们,此外,它通过模糊度量考虑它们之间的相互关系。使用这个算子,可以从传统或最近的分子描述符中导出模糊定义;例如,模糊类Randic连接性指数、模糊类Balaban指数、模糊Kier-Hall连接性指数等。为了证明使用这个算子的可行性,将QuBiLS-MIDAS三维分子描述符用作研究案例,结果在相应软件中构建了一个模块来计算它们(http://tomocomd.com/qubils-midas)。因此,它是文献中报道的唯一可用于确定基于Choquet积分的模糊分子描述符的软件。此外,在八个化学数据集上创建了回归模型。通过这种方式,对基于非模糊QuBiLS-MIDAS三维分子描述符的模型与基于模糊QuBiLS-MIDAS三维分子描述符的模型所取得的结果进行了比较。结果表明,基于模糊QuBiLS-MIDAS三维分子描述符构建的模型具有最佳性能,通过Wilcoxon符号秩检验在统计上得到了证实。

结论

总而言之,可以得出结论,Choquet积分是从局部顶点不变量/局部边不变量计算模糊二维/三维分子描述符的一个突出选择。通过这种方式,可以获得对化合物更好的表征,这最终将有助于提高现有传统二维/三维分子描述符的建模能力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9748/6755596/1bbec0827ebb/13321_2018_306_Sch1_HTML.jpg

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