Literature DB >> 18007502

QSPR calculation of normal boiling points of organic molecules based on the use of correlation weighting of atomic orbitals with extended connectivity of zero- and first-order graphs of atomic orbitals.

Maykel Pérez González1, Andrey A Toropov, Pablo R Duchowicz, Eduardo A Castro.   

Abstract

We report the results of a calculation of the normal boiling points of a representative set of 200 organic molecules through the application of QSPR theory. For this purpose we have used a particular set of flexible molecular descriptors, the so called Correlation Weighting of Atomic Orbitals with Extended Connectivity of Zero- and First-Order Graphs of Atomic Orbitals. Although in general the results show suitable behavior to predict this physical chemistry property, the existence of some deviant behaviors points to a need to complement this index with some other sort of molecular descriptors. Some possible extensions of this study are discussed.

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Year:  2004        PMID: 18007502      PMCID: PMC6147301          DOI: 10.3390/91201019

Source DB:  PubMed          Journal:  Molecules        ISSN: 1420-3049            Impact factor:   4.411


  4 in total

1.  Boiling points of halogenated aliphatic compounds: a quantitative structure-property relationship for prediction and validation.

Authors:  Tomas Oberg
Journal:  J Chem Inf Comput Sci       Date:  2004 Jan-Feb

2.  TOPS-MODE based QSARs derived from heterogeneous series of compounds. Applications to the design of new herbicides.

Authors:  Maykel Pérez González; Humberto Gonzalez Díaz; Reinaldo Molina Ruiz; Miguel A Cabrera; Ronal Ramos de Armas
Journal:  J Chem Inf Comput Sci       Date:  2003 Jul-Aug

3.  Structural determination of paraffin boiling points.

Authors:  H WIENER
Journal:  J Am Chem Soc       Date:  1947-01       Impact factor: 15.419

4.  Predicting mutagenicity of chemicals using topological and quantum chemical parameters: a similarity based study.

Authors:  S C Basak; G D Grunwald
Journal:  Chemosphere       Date:  1995-07       Impact factor: 7.086

  4 in total
  1 in total

1.  Electron configuration-based neural network model to predict physicochemical properties of inorganic compounds.

Authors:  Hyun Kil Shin
Journal:  RSC Adv       Date:  2020-09-08       Impact factor: 4.036

  1 in total

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