Title:Modeling Physico-Chemical Properties of Quinolone Derivatives Using GA-MLR as a Computational Study
VOLUME: 16 ISSUE: 6
Author(s):Meysam Shirmohammadi, Esmat Mohammadinasab* and Zakiyeh Bayat
Affiliation:Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Department of Chemistry, Arak Branch, Islamic Azad University, Arak, Department of Chemistry, Quchan Branch, Islamic Azad University, Quchan
Keywords:QSPR, Quinolones, refractive index, polarizability, HOMO-LUMO energy gap, GA-MLR method.
Abstract:
Background: In this study, we used a hierarchical approach to develop quantitative
structure-activity relationship (QSAR) models for modeling physico-chemical properties of quinolone
derivatives.
Objective: The relationship between some of the molecular descriptors with physic-chemical
properties such as refractive index (n), polarizability (α) and HOMO-LUMO energy gap (ΔEH-L)
was represented.
Materials and Methods: Quantum mechanical calculations using abinitio method at the #HF/6-
31++G** level were carried out to obtain the optimized geometry and then, the comprehensive set
of molecular descriptors was computed by using the Dragon software. Genetic algorithm using
multiple linear regression (GA-MLR) with backward method by SPSS software were utilized to
construct QSAR models.
Results: The analytical powers of the established theoretical models were discussed using leaveone-
out (LOO) cross-validation technique. A multi-parametric equation containing maximum
three descriptors with suitable statistical qualities was obtained for predicting the studied properties.
Conclusion: The QSPR analysis for the prediction of the refractive index, the polarizability and
the HOMO-LUMO energy gap of 40 quinolone derivatives using GA-MLR method was performed.
The achieved results showed that the best model for predicting the refractive index, the
polarizability and the HOMO-LUMO energy gap contains maximum three descriptors. MLR
analysis, using genetic algorithms as suitable descriptors selection method showed that the three
selected descriptors play a vital role in the prediction of physicochemical properties of quinolone
derivatives. It can be noted that the best descriptors in the final obtained models can be used to design
and screen new drugs.
