Structural Interpretation of the Topological Index. 2. The Molecular Connectivity Index, the Kappa Index, and the Atom-type E-State Index

2004 ◽  
Vol 44 (4) ◽  
pp. 1193-1201 ◽  
Author(s):  
Qian-Nan Hu ◽  
Yi-Zeng Liang ◽  
Hong Yin ◽  
Xiao-Ling Peng ◽  
Kai-Tai Fang
Keyword(s):  
Topological Index ◽  
Connectivity Index ◽  
Molecular Connectivity ◽  
Kappa Index ◽  
Atom Type ◽  
Molecular Connectivity Index ◽  
Structural Interpretation ◽  
State Index ◽  
Index 2

TOPOLOGICAL MODELS FOR THE PREDICTION OF NEUTRAL ENDOPEPTIDASE AND ANGIOTENSIN-CONVERTING ENZYME INHIBITORY ACTIVITY OF MERCAPTOACYLDIPEPTIDES

2006 ◽  
Vol 05 (03) ◽  
pp. 565-577 ◽  
Author(s):  
VINEY LATHER ◽  
A. K. MADAN
Keyword(s):  
Inhibitory Activity ◽  
Topological Index ◽  
Wiener Index ◽  
Neutral Endopeptidase ◽  
Connectivity Index ◽  
Molecular Connectivity ◽  
Ace Inhibitory Activity ◽  
Eccentric Connectivity Index ◽  
Data Set ◽  
Molecular Connectivity Index

The relationship between the topological indices and the Neutral Endopeptidase (NEP) inhibitory activity and Angiotensin-Converting Enzyme (ACE) inhibitory activity of mercaptoacyldipeptides has been investigated. Three topological indices — the Wiener index (a distance-based topological index), the molecular connectivity index (an adjacency-based topological index), and the eccentric connectivity index (an adjacency-cum-distance-based topological index), were presently used for investigation. A data set comprising 39 differently substituted mercaptoacyldipeptides was selected for the present study. The values of the Wiener index, molecular connectivity index, and eccentric connectivity index for each of the 39 compounds comprising the data set were computed using an in-house computer program. Resultant data were analyzed and suitable models were developed after identification of the active ranges. Subsequently, a biological activity was assigned to each compound using these models, and the biological activity was then compared with the reported NEP and ACE inhibitory activity of each compound. Accuracy of prediction up to a maximum of ~91% was obtained using these models.

Research on Calculation of Molecular Connectivity Index

2013 ◽  
Vol 303-306 ◽  
pp. 2671-2674 ◽  
Author(s):  
Wei Ye Tao ◽  
Lai You Wang ◽  
Guo Quan Huang ◽  
Hua Ying Zhou ◽  
Man Luo
Keyword(s):  
Adjacency Matrix ◽  
Molecular Descriptor ◽  
Connectivity Index ◽  
Molecular Connectivity ◽  
Molecular Connectivity Index ◽  
Ascii File ◽  
Structural Selectivity ◽  
2D And 3D ◽  
3D Topology ◽  
Mol2 Format

Compared to other topological indices, molecular connectivity index has good structural selectivity and correlation.According to the molecule’s 2D and 3D topology file, we conducted research on the mol2 format file to consider how to convert the ASCII file into the adjacency matrix. Based on the adjacency matrix, we analyzed the relevant algorithm to calculate the molecules’ molecular connectivity index through adjacency matrix. As a molecular descriptor, the molecular connectivity index can be used in QSAR.

Correlating the solubility of CO<sub>2</sub> in absorbents with molecular connectivity index

2012 ◽  
Vol 42 (3) ◽  
pp. 297-305
Author(s):  
TieZhi LI ◽  
Hui HU ◽  
ZhiGang TANG ◽  
WeiYang FEI
Keyword(s):  
Connectivity Index ◽  
Molecular Connectivity ◽  
Molecular Connectivity Index
Sign in / Sign up

Export Citation Format

Share Document