QSPR models to predict quantum chemical properties of imidazole derivatives using genetic algorithm–multiple linear regression and back‐propagation–artificial neural network. Issue 24 (7th September 2022)

• Record Type: Journal Article
• Authors: Moshayedi, Shiva; Shafiei, Fatemeh; Momeni Isfahani, Tahereh
• Is Part Of: International journal of quantum chemistry. Volume 122:Issue 24(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-09-07
• Subjects: BP–ANN -- enthalpy of formation -- entropy -- external validation -- GA–MLR -- imidazole derivatives; Quantum chemistry -- Periodicals; 541.28
• DOI: 10.1002/qua.27003
• Languages: English
• ISSNs: 0020-7608

Abstract

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 neural network (BP–ANN) were applied to design QSPR models to predict the quantum chemical properties like the entropy ( S ) and enthalpy of formation (∆ H f ) 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 ∆ H f, showed that there is good linear correlation between the selected descriptors and ∆ H f 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. Abstract : In this study, the genetic algorithm multiple linear regression (GA–MLR), and backpropagation–artificial neural network (BP–ANN) were applied to design QSPR models for predicting the entropy ( S ) and enthalpy of formation (∆ H f ) 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.