QSAR and Molecular Docking Studies on Non-Imidazole-Based Histamine H3 Receptor Antagonists

Hamzeh‐Mivehroud, Maryam; Khoshravan-Azar, Zoha; Dastmalchi, Siavoush

doi:10.34172/ps.2019.64

Cited by 6 publications

(3 citation statements)

References 52 publications

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“…The verification and validity [67][68][69] of the QSPR models were assessed by different fitting criteria; internal cross-validation using leave-one out (LOO) method and the external validation parameters [70][71][72][73][74][75].…”

Section: Validationmentioning

confidence: 99%

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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Section: Validationmentioning

confidence: 99%

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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“…In another study a quantitative structure-activity relationship (QSAR) model, employing a genetic algorithm coupled with partial least square and stepwise multiple linear regression methods, was used with two major descriptors: connectivity information and mean absolute charge in predicting H 3 R antagonistic activity. The additional molecular docking revealed the crucial role of residues Y 3.33 , Y189 ECL2 , F193 ECL2 , L 5.39 , E 5.46 , W 6.48 , Y 6.51 , M 6.55 , Y 7.35 and F 7.39 in the interaction with H 3 R targeted ligands [ 91 ]. Schaller et al [ 52 ] used a ligand-guided homology modeling strategy that resulted in identification of two H 3 R ligands with nanomolar affinity, including Compound 8 (Z27743747) in Figure 2 .…”

Section: Hr-targeted Ligands and Receptor Binding Site Of Inactivementioning

confidence: 99%

Molecular Modeling of Histamine Receptors—Recent Advances in Drug Discovery

Mehta

Filipek

2021

Molecules

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The recent developments of fast reliable docking, virtual screening and other algorithms gave rise to discovery of many novel ligands of histamine receptors that could be used for treatment of allergic inflammatory disorders, central nervous system pathologies, pain, cancer and obesity. Furthermore, the pharmacological profiles of ligands clearly indicate that these receptors may be considered as targets not only for selective but also for multi-target drugs that could be used for treatment of complex disorders such as Alzheimer’s disease. Therefore, analysis of protein-ligand recognition in the binding site of histamine receptors and also other molecular targets has become a valuable tool in drug design toolkit. This review covers the period 2014–2020 in the field of theoretical investigations of histamine receptors mostly based on molecular modeling as well as the experimental characterization of novel ligands of these receptors.

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“…Five criteria proposed in authentic journals were selected in this study and details have been described in method section. They are highly cited and several researchers were used them to evaluate validity of QSAR models [15][16][17][18]. Designers of each criterion have been shown advantages of them in comparison with others for external validation of QSAR models [5,6,[19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Comparison of various methods for validity evaluation of QSAR models

Shayanfar

2022

BMC Chemistry

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Background Quantitative structure–activity relationship (QSAR) modeling is one of the most important computational tools employed in drug discovery and development. The external validation of QSAR models is the main point to check the reliability of developed models for the prediction activity of not yet synthesized compounds. It was performed by different criteria in the literature. Methods In this study, 44 reported QSAR models for biologically active compounds reported in scientific papers were collected. Various statistical parameters of external validation of a QSAR model were calculated, and the results were discussed. Results The findings revealed that employing the coefficient of determination (r2) alone could not indicate the validity of a QSAR model. The established criteria for external validation have some advantages and disadvantages which should be considered in QSAR studies. Conclusion This study showed that these methods alone are not only enough to indicate the validity/invalidity of a QSAR model.

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QSAR and Molecular Docking Studies on Non-Imidazole-Based Histamine H3 Receptor Antagonists

Cited by 6 publications

References 52 publications

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

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

Molecular Modeling of Histamine Receptors—Recent Advances in Drug Discovery

Comparison of various methods for validity evaluation of QSAR models

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