Journal of Chemistry

Application of Variable Selection and Modeling in QSPR/QSAR Studies

Publishing date
20 Feb 2015
Submission deadline
03 Oct 2014

1Islamic Azad University, Tehran, Iran

2University of Tehran, Tehran, Iran

3University of North Texas, Denton, USA

4Oral Roberts University, Tulsa, USA

This issue is now closed for submissions.

Application of Variable Selection and Modeling in QSPR/QSAR Studies

This issue is now closed for submissions.


Quantitative structure property/activity relationship (QSPR/QSAR) studies have been recognized to be an efficient computational tool in understanding the correlation between the structure of molecules and their properties/activities. QSPR/QSAR is a mathematical technique that relates the properties/activities of interested molecule to its structural features. The advantage of this approach over other methods lies in the fact that the descriptors used can be calculated from structure only and are not reliant on any experiment properties. Once the structure of a compound is known, any descriptor can be calculated. The built model performance and accuracy of the results are powerfully reliant on the way that descriptors were selected.

We invite researchers to contribute original research articles as well as review articles that explore all fields of theoretical chemistry, computational chemistry, and modeling. Specific topics include the quantitative structure property relationship (QSPR), quantitative structure activity relationship (QSAR), feature selection, pattern recognition, biomolecular structure prediction, molecular design, and bioinformatics.

Potential topics include, but are not limited to:

  • Development of new statistical, mathematical, and theoretical methods for chemistry and related fields
  • Development of new computational methods or effective algorithms for chemical software
  • Well-characterized data sets to check performance for the new methods and software
  • Quantitative structure property-activity relationship estimations
  • Variable selection methods such as partial least squares (PLS), genetic algorithm (GA), and replacement method (RM)
  • Modeling techniques such as artificial neural network and support vector machine
Journal of Chemistry
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