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\begin{document}

\title{QSPR models to Predict the quantum chemical properties of imidazole
derivatives using genetic algorithm multiple linear regression and
back-propagation-artificial neural network}



\author[1]{shiva Moshayedi}%
\author[1]{fatemeh shafiei}%
\author[2]{Tahereh Momeni Isfahani}%
\affil[1]{Islamic Azad University Arak Branch}%
\affil[2]{Islamic Azad University of Arak}%


\vspace{-1em}



  
  \date{May 21, 2024}


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\begin{abstract}
Imidazole derivatives are the foundation of different types of drugs
with a wide range of biological activities. In this study, the genetic
algorithm multiple linear regression (GA- MLR), and
backpropagation-artificial artificial neural network (BP-ANN) were
applied to design QSPR models to predict the quantum chemical properties
like the entropy(S) and enthalpy of formation([?]Hf) of imidazole
derivatives. In order to draw molecular structure of 84 derivative
compounds Gauss View 05 program was used. These structures were
optimized at DFT-B3LYP / 6-311G* level with Gaussian09W. The Dragon
software was used to calculate a set of different molecular descriptors,
and the genetic algorithm procedure and backward stepwise regression
were applied for the selection of descriptors. The resulting
quantitative GA-MLR model of [?]Hf, showed that there is good linear
correlation between the selected descriptors and [?]Hf of compounds. Also
the results show that the BP-ANN model appeared to be superior to GA-MLR
model for prediction of entropy. Different internal and external
validation metrics were adopted to verify the predictive performance of
QSPR models. The predictive powers of the models were found to be
acceptable. Thus, these QSPR models may be useful for designing new
series of imidazole derivatives and prediction of their properties.%
\end{abstract}%



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\textbf{Hosted file}

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