Literature DB >> 10955521

Overall connectivities/topological complexities: a new powerful tool for QSPR/QSAR

.   

Abstract

Earlier attempts to assess the complexity of molecules are analyzed and summarized in a number of definitions of general and topological complexity. A concept which specifies topological complexity as overall connectivity, and generalizes the idea of molecular connectivities of Randic, Kier, and Hall, is presented. Two overall connectivity indices, TC and TC1, are defined as the connectivity (the sum of the vertex degrees) of all connected subgraphs in the molecular graph. The contributions to TC and TC1, which originate from all subgraphs having the same number of edges e, form two sets of eth-order overall connectivities, eTC and eTC1. The total number of subgraphs K is also analyzed as a complexity measure, and the vector of its eth-order components, eK, is examined as well. The TC, TC1, and K indices match very well the increase in molecular complexity with the increase in the number of atoms and, at a constant number of atoms, with the increased degree of branching and cyclicity of the molecular skeleton, as well as with the multiplicity of bonds and the presence of heteroatoms. The potential of the three sets of eth-order complexities for applications to QSPR was tested by the modeling of 10 alkane properties (boiling point, critical temperature, critical pressure, critical volume, molar volume, molecular refraction, heat of formation, heat of vaporization, heat of atomization, and surface tension), in parallel with Kier and Hall's molecular connectivity indices (k)chi. The topological complexity indices were shown to outperform molecular connectivity indices in 44 out of the 50 pairs of models compared, including all models with four and five parameters.

Entities:  

Year:  2000        PMID: 10955521     DOI: 10.1021/ci990120u

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


  6 in total

1.  Study of peptide fingerprints of parasite proteins and drug-DNA interactions with Markov-Mean-Energy invariants of biopolymer molecular-dynamic lattice networks.

Authors:  Lázaro Guillermo Pérez-Montoto; María Auxiliadora Dea-Ayuela; Francisco J Prado-Prado; Francisco Bolas-Fernández; Florencio M Ubeira; Humberto González-Díaz
Journal:  Polymer (Guildf)       Date:  2009-06-03       Impact factor: 4.430

2.  Novel topological descriptors for analyzing biological networks.

Authors:  Matthias M Dehmer; Nicola N Barbarini; Kurt K Varmuza; Armin A Graber
Journal:  BMC Struct Biol       Date:  2010-06-17

3.  Structural differentiation of graphs using Hosoya-based indices.

Authors:  Matthias Dehmer; Abbe Mowshowitz; Yongtang Shi
Journal:  PLoS One       Date:  2014-07-14       Impact factor: 3.240

4.  A large scale analysis of information-theoretic network complexity measures using chemical structures.

Authors:  Matthias Dehmer; Nicola Barbarini; Kurt Varmuza; Armin Graber
Journal:  PLoS One       Date:  2009-12-15       Impact factor: 3.240

5.  Entropy bounds for hierarchical molecular networks.

Authors:  Matthias Dehmer; Stephan Borgert; Frank Emmert-Streib
Journal:  PLoS One       Date:  2008-08-28       Impact factor: 3.240

6.  Scoring function for DNA-drug docking of anticancer and antiparasitic compounds based on spectral moments of 2D lattice graphs for molecular dynamics trajectories.

Authors:  Lázaro G Pérez-Montoto; Lourdes Santana; Humberto González-Díaz
Journal:  Eur J Med Chem       Date:  2009-06-17       Impact factor: 6.514

  6 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.