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Raouf Ghavami

Raouf Ghavami

Academic rank: Professor
ORCID:
Education: PhD.
ScopusId: 55408187000
Faculty: Faculty of Science
Address:
Phone: 08713393265

Research

Title
Highly correlating distance-connectivity-based topological indices. 4: Stepwise factor selection-based PCR models For QSPR study Of 14 properties of monoalkenes
Type
JournalPaper
Keywords
quantitative structure-property relationships, topological index Sh, alkane properties
Year
2007
Journal POLISH JOURNAL OF CHEMISTRY
DOI
Researchers Mojtaba Shamsipur ، Raouf Ghavami ، Bahram Hemmateenejad ، Hashem Sharghi

Abstract

The potential usefulness of some newly proposed topological indices (Sh indices) has been examined by their application to predict 14 different properties of a large number of alkenes (C4–C9, a total of 162 molecules). Ten different indices (Sh1–Sh10) and a novel one (Sh index) were calculated for each molecule by different combination of the connectivity and distance sum vectors. The alkenes’ properties studied here were boiling point (BP), melting point (MP), density (D), molar refraction (MR), molar volume (MV), refraction index (no), critical temperature (Tc), critical pressure (Pc), heat of combustion (HC), molar heat of vaporization (HV), heat of atomization (HA), viscosity (VISC), flashpoint (FLASHK) and second virial coefficient (VIRC2). First, the novel Sh index and the Randic connectivity index were used to simply correlate them to different monoalkenes’properties. For all properties, except Pc, the Sh index produced high correlation coefficient. Besides, in almost all cases, the Sh index resulted in higher correlation than the Randic index. In order to predict the properties of alkenes more accurately, PCR analysis was employed to drive multiparametric equations between the Sh indices and alkenes’ properties. It was found that the stepwise selection procedure for factor selection, which was in accordance with the correlation ranking procedure, produced more convenient models in comparison with the eigen-value ranking procedure. The advantages of the resulting QSPR models obtained by using Sh indices, relative to some other proposed models, include lower number of variables and higher prediction power.