Literature DB >> 15272826

Structural interpretation of the topological index. 2. The molecular connectivity index, the Kappa index, and the atom-type E-State index.

Qian-Nan Hu1, Yi-Zeng Liang, Hong Yin, Xiao-Ling Peng, Kai-Tai Fang.   

Abstract

The structural interpretation is extended to the topological indices describing cyclic structures. Three representatives of the topological index, such as the molecular connectivity index, the Kappa index, and the atom-type E-State index, are interpreted by mining out, through projection pursuit combining with a number theory method generating uniformly distributed directions on unit sphere, the structural features hidden in the spaces spanned by the three series of indices individually. Some interesting results, which can hardly be found by individual index, are obtained from the multidimensional spaces by several topological indices. The results support quantitatively the former studies on the topological indices, and some new insights are obtained during the analysis. The combinations of several molecular connectivity indices describe mainly three general categories of molecular structure information, which include degree of branching, size, and degree of cyclicity. The cyclicity can also be coded by the combination of chi cluster and path/cluster indices. The Kappa shape indices encode, in combination, significant information on size, the degree of cyclicity, and the degree of centralization/separation in branching. The size, branch number, and cyclicity information has also been mined out to interpret atom-type E-State indices. The structural feature such as the number of quaternary atoms is searched out to be an important factor. The results indicate that the collinearity might be a serious problem in the applications of the topological indices.

Year:  2004        PMID: 15272826     DOI: 10.1021/ci049973z

Source DB:  PubMed          Journal:  J Chem Inf Comput Sci        ISSN: 0095-2338


  1 in total

1.  QSPR model for Caco-2 cell permeability prediction using a combination of HQPSO and dual-RBF neural network.

Authors:  Yukun Wang; Xuebo Chen
Journal:  RSC Adv       Date:  2020-11-26       Impact factor: 4.036

  1 in total

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