Comparative molecular field analysis (CoMFA), topomer CoMFA, and hologram QSAR studies on a series of novel HIV‐1 protease inhibitors

Heidari, Afsane; Fatemi, M.

doi:10.1111/cbdd.12917

Cited by 14 publications

(7 citation statements)

References 29 publications

(36 reference statements)

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“…The activities of MAO-B inhibitors are related to the integrity of the pharmacophore [ 36 ]. In the Topomer CoMFA model, the pharmacophore is related to cutting style, which is important for the model’s predictive performance [ 17 , 37 , 38 ]. In the Topomer CoMFA analysis, the training set is split into two fragments.…”

Section: Discussionmentioning

confidence: 99%

Discovery of MAO-B Inhibitor with Machine Learning, Topomer CoMFA, Molecular Docking and Multi-Spectroscopy Approaches

Zheng

Qin²,

Wang³

et al. 2022

Biomolecules

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Alzheimer’s disease (AD) is the most common type of dementia and is a serious disruption to normal life. Monoamine oxidase-B (MAO-B) is an important target for the treatment of AD. In this study, machine learning approaches were applied to investigate the identification model of MAO-B inhibitors. The results showed that the identification model for MAO-B inhibitors with　K-nearest neighbor(KNN) algorithm had a prediction accuracy of 94.1% and 88.0% for the 10-fold cross-validation test and the independent test set, respectively. Secondly, a quantitative activity prediction model for MAO-B was investigated with the Topomer CoMFA model. Two separate cutting mode approaches were used to predict the activity of MAO-B inhibitors. The results showed that the cut model with q2 = 0.612 (cross-validated correlation coefficient) and r2 = 0.824 (non-cross-validated correlation coefficient) were determined for the training and test sets, respectively. In addition, molecular docking was employed to analyze the interaction between MAO-B and inhibitors. Finally, based on our proposed prediction model, 1-(4-hydroxyphenyl)-3-(2,4,6-trimethoxyphenyl)propan-1-one (LB) was predicted as a potential MAO-B inhibitor and was validated by a multi-spectroscopic approach including fluorescence spectra and ultraviolet spectrophotometry.

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Section: Discussionmentioning

confidence: 99%

Discovery of MAO-B Inhibitor with Machine Learning, Topomer CoMFA, Molecular Docking and Multi-Spectroscopy Approaches

Zheng

Qin²,

Wang³

et al. 2022

Biomolecules

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“…Topomer CoMFA is a rapid fragment-based three-dimensional quantitative structure-activity relationship (3D-QSAR) method [ 39 , 40 ]. Unlike traditional CoMFA, the Topomer CoMFA does not require subjective alignment of 3D ligand conformers and uses automatic alignment rules, so analysis is faster.…”

Section: Methodsmentioning

confidence: 99%

2D-SAR, Topomer CoMFA and molecular docking studies on avian influenza neuraminidase inhibitors

Niu

et al. 2019

Computational and Structural Biotechnology Journal

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Avian influenza is a serious zoonotic infectious disease with huge negative impacts on local poultry farming, human health and social stability. Therefore, the design of new compounds against avian influenza has been the focus in this field. In this study, computational methods were applied to investigate the compounds with neuraminidase inhibitory activity. First, 2D-SAR model was built to recognize neuraminidase inhibitors (NAIs). As a result, the accuracy of 10 cross-validation and independent tests is 96.84% and 98.97%, respectively. Then, the Topomer CoMFA model was constructed to predict the inhibitory activity and analyses molecular fields. Two models were obtained by changing the cutting methods. The second model is employed to predict the activity (q2 = 0.784 and r2 = 0.982). Molecular docking was also used to further analyze the binding sites between NAIs and neuraminidase from human and avian virus. As a result, it is found that same binding Total Score has some differences, but the binding sites are basically the same. At last, some potential NAIs were screened and some optimal opinions were taken. It is expected that our study can assist to study and develop new types of NAIs.

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“…Being different from the traditional CoMFA, topomerCoMFA is a No-Alignment CoMFA [21]. In topomerCoMFA, electrostatic and steric fields were calculated for the fragments from 3D structural cleavage.…”

Section: Methodsmentioning

confidence: 99%

Computational Analysis of Artimisinin Derivatives on the Antitumor Activities

Liu

Zhang

2017

Nat. Prod. Bioprospect.

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The study on antitumor activities of artemisinin and its derivatives has been closely focused on in recent years. Herein, 2D and 3D QSAR analysis was performed on the basis of a series of artemisinin derivatives with known bioactivities against the non-small-cell lung adenocarcinoma A549 cells. Four QSAR models were successfully established by CoMSIA, CoMFA, topomer CoMFA and HQSAR approaches with respective characteristic values q2 = 0.567, R2 = 0.968, ONC = 5; q2 = 0.547, R2 = 0.980, ONC = 7; q2 = 0.559, R2 = 0.921, ONC = 7 and q2 = 0.527, R2 = 0.921, ONC = 6. The predictive ability of CoMSIA with r2 = 0.991 is the best one compared with the other three approaches, such as CoMFA (r2 = 0.787), topomer CoMFA (r2 = 0.819) and HQSAR (r2 = 0.743). The final QSAR models can provide guidance in structural modification of artemisinin derivatives to improve their anticancer activities.

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Comparative molecular field analysis (CoMFA), topomer CoMFA, and hologram QSAR studies on a series of novel HIV‐1 protease inhibitors

Cited by 14 publications

References 29 publications

Discovery of MAO-B Inhibitor with Machine Learning, Topomer CoMFA, Molecular Docking and Multi-Spectroscopy Approaches

Discovery of MAO-B Inhibitor with Machine Learning, Topomer CoMFA, Molecular Docking and Multi-Spectroscopy Approaches

2D-SAR, Topomer CoMFA and molecular docking studies on avian influenza neuraminidase inhibitors

Computational Analysis of Artimisinin Derivatives on the Antitumor Activities

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