<scp>QSPR</scp> models to predict quantum chemical properties of imidazole derivatives using genetic algorithm–multiple linear regression and back‐propagation–artificial neural network

Moshayedi, Shiva; Shafiei, Fatemeh; Isfahani, Tahereh Momeni

doi:10.1002/qua.27003

Cited by 5 publications

(5 citation statements)

References 66 publications

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“…Multiple linear regression, popular in QSPR, might not capture nonlinear relationships present in complex and multidimensional chemical space. 11 Linear models are popular and easy to build and operate on molecular descriptors. However, there are issues arising in the interpretation and selection of important features.…”

Section: Introductionmentioning

confidence: 99%

Graph Neural Networks and Structural Information on Ionic Liquids: A Cheminformatics Study on Molecular Physicochemical Property Prediction

Baran,

Kloskowski

2023

J. Phys. Chem. B

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Ionic liquids (ILs) provide a promising solution in many industrial applications, such as solvents, absorbents, electrolytes, catalysts, lubricants, and many others. However, due to the enormous variety of their structures, uncovering or designing those with optimal attributes requires expensive and exhaustive simulations and experiments. For these reasons, searching for an efficient theoretical tool for finding the relationship between the IL structure and properties has been the subject of many research studies. Recently, special attention has been paid to machine learning tools, especially multilayer perceptron and convolutional neural networks, among many other algorithms in the field of artificial neural networks. For the latter, graph neural networks (GNNs) seem to be a powerful cheminformatic tool yet not well enough studied for dual molecular systems such as ILs. In this work, the usage of GNNs in structure–property studies is critically evaluated for predicting the density, viscosity, and surface tension of ILs. The problem of data availability and integrity is discussed to show how well GNNs deal with mislabeled chemical data. Providing more training data is proven to be more important than ensuring that they are immaculate. Great attention is paid to how GNNs process different ions to give graph transformations and electrostatic information. Clues on how GNNs should be applied to predict the properties of ILs are provided. Differences, especially regarding handling mislabeled data, favoring the use of GNNs over classical quantitative structure–property models are discussed.

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Section: Introductionmentioning

confidence: 99%

Graph Neural Networks and Structural Information on Ionic Liquids: A Cheminformatics Study on Molecular Physicochemical Property Prediction

Baran,

Kloskowski

2023

J. Phys. Chem. B

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“…QSPR models have been developed based on molecular descriptors derived from chemical structures for the prediction of thermodynamic properties, such as heat capacity (Cv), entropy ( S ), and thermal energy ( E th ) of some organic compounds and drug derivatives [47–50].…”

Section: Introductionmentioning

confidence: 99%

“…Density functional theory (DFT), quantum chemical descriptors and statistical methods have been applied to build QSAR model to predict the anti-Human African Trypanosomiasis(anti-HAT) activity of 60 2-phenylimidazopyridines derivatives [46]. QSPR models have been developed based on molecular descriptors derived from chemical structures for the prediction of thermodynamic properties, such as heat capacity (Cv), entropy (S), and thermal energy (E th ) of some organic compounds and drug derivatives [47][48][49][50].…”

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confidence: 99%

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QSPR study to predict some of quantum chemical properties of anticancer imidazo[4,5‐b]pyridine derivatives using genetic algorithm multiple linear regression and molecular descriptors

Jafari,

Momeni Isfahani,

Shafiei

et al. 2023

Int J of Quantum Chemistry

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Pyridine and its derivatives have been applied clinically for the treatment of a wide range of diseases and in the synthesis of novel drugs. In the present work, imidazo[4,5‐b]pyridine derivatives as anticancer drugs were exhibited to select the important descriptor for quantum chemical properties. Two of the fundamental thermodynamic properties are heat capacity (Cv) and entropy (S), which are important in the field of chemical kinetics and are key in the understanding and design of chemical processes involving chemical reactions. A Quantitative Structure–Property Relationship (QSPR) study was used to predict the quantum chemical properties like Cv and S of 105 imidazole derivatives using molecular descriptors and the genetic algorithm–multiple linear regression (GA–MLR). The best QSPR models were selected using criteria coefficients such as R2, R2adj, RMSE and Fisher ratio. Different internal and external validation metrics were adopted to evaluate the stability, fit and predictive power of the QSPR models. The validation results and statistical analysis show that the models possess good prediction power and robustness, and the total size (TS) and Sanderson electronegativity(RDF060e) and total information content index(TIC1) of imidazo[4,5‐b]pyridine derivatives are increasingly related to the studied properties.

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“…[1] The molecular descriptors of a molecule are shown in the numeric equation and then these molecular descriptors are used to calculate the physicochemical properties of the molecule. [2] The molecular descriptors can be estimated physicochemical properties, and are divided into many classes such as constitutional descriptors (molecular weight, total number of atoms, functional group), topological descriptors (connectivity index, Wiener index, Balaban index), electrostatic descriptors (polarizability, dipole moment), geometrical descriptors (molecular volume, molecular surface area), thermodynamic descriptors (vibrational frequencies, translational frequencies, rotational frequencies), and quantum chemical descriptors (HOMO/LUMO energies, standard heat of formation). [3] The QSPR models can be developed by first collecting the data: the data is collected and used to create the 3D structures for calculating molecular descriptors.…”

Section: Quantitative Structure-property Relationshipmentioning

confidence: 99%

Prediction of soil sorption coefficient of chemicals by molecular modeling technique

Pithaktrakool

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Soil pollution is one of the pollutions caused by nature and human activities, which are mostly the use of insecticide and chemical fertilizer to make plants grow well and produce more products. However, these chemicals are absorbed by soil. In this research work, properties of chemical compound affecting soil sorption were analyzed, and model that can predict soil sorption coefficient of chemicals (KOC) was examined by using quantitative structure property relationship (QSPR) technique. Totally 1,327 compounds were selected, and their three-dimensional structures were constructed. Semi-empirical PM7 method was used for geometry optimization calculations. A total of 1,536 physicochemical properties were computed. Multiple linear regression was employed to analyze properties of compound affecting soil sorption and to find QSPR model. The obtained model has a good relationship and predictive ability with r2 = 0.762 and q2 = 0.761.

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QSPR models to predict quantum chemical properties of imidazole derivatives using genetic algorithm–multiple linear regression and back‐propagation–artificial neural network

Cited by 5 publications

References 66 publications

Graph Neural Networks and Structural Information on Ionic Liquids: A Cheminformatics Study on Molecular Physicochemical Property Prediction

Graph Neural Networks and Structural Information on Ionic Liquids: A Cheminformatics Study on Molecular Physicochemical Property Prediction

QSPR study to predict some of quantum chemical properties of anticancer imidazo[4,5‐b]pyridine derivatives using genetic algorithm multiple linear regression and molecular descriptors

Prediction of soil sorption coefficient of chemicals by molecular modeling technique

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