Prediction of λmax of 1,4-naphthoquinone derivatives using ant colony optimization

Atabati, Morteza; Zarei, Kobra; Mohsennia, M.

doi:10.1016/j.aca.2010.01.024

Cited by 12 publications

(5 citation statements)

References 21 publications

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“…To facilitate the drug discovery process, in silico modeling approaches − as a productive and cost-effective technology in design of novel lead compounds should be used in combination with experimental practices. − In view of this, Fujita’s group has carried out excellent work to study the P2Y receptor using both the in silico and experimental methods. − In addition, our group also used two-dimensional quantitative structure–activity relationship (2D-QSAR) to predict series of P2Y 12 antagonists using a novel genetic algorithm-support vector machine coupled approach . As we know, in many cases, 2D-QSAR often focuses on the predictive models − in which gaining an intuitive interpretation of important features from this 2D-QSAR study is not always simple. And it is also difficult to present a comprehensive feature for the ligand–receptor interactions, such as the hydrophobic contact, polar interactions between the key amino residues and agents.…”

Section: Introductionmentioning

confidence: 99%

Combined 3D-QSAR, Molecular Docking, and Molecular Dynamics Study on Piperazinyl-Glutamate-Pyridines/Pyrimidines as Potent P2Y₁₂ Antagonists for Inhibition of Platelet Aggregation

Ming

Wang

et al. 2011

J. Chem. Inf. Model.

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An unusually large data set of 397 piperazinyl-glutamate-pyridines/pyrimidines as potent orally bioavailable P2Y(12) antagonists for inhibition of platelet aggregation was studied for the first time based on the combination of three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, and molecular dynamics (MD) methods. The comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) studies have been performed with a training set of 317 compounds, estimating three superimposition methods. The best CoMFA and CoMSIA models, derived from superimposition I, shows leave-one-out cross-validation correlation coefficients (Q(2)) of 0.571 and 0.592 as well as the conventional correlation coefficients (R(2)(ncv)) of 0.814 and 0.834, respectively. In addition, the satisfactory results, based on the bootstrapping analysis and 10-fold cross-validation, further indicate the highly statistical significance of the optimal models. The external predictive abilities of these models were evaluated using a prediction set of 80 compounds, producing the predicted correlation coefficients (R(2)(pred)) of 0.664 and 0.668, respectively. The key amino acid residues were identified by molecular docking, and the stability and rationality of the derived molecular conformations were also validated by MD simulation. The good concordance between the docking results and CoMFA/CoMSIA contour maps provides helpful clues about the rational modification of molecules in order to design more potent P2Y(12) antagonists. We hope the developed models could provide some instructions for further synthesis of highly potent P2Y(12) antagonists.

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Section: Introductionmentioning

confidence: 99%

Combined 3D-QSAR, Molecular Docking, and Molecular Dynamics Study on Piperazinyl-Glutamate-Pyridines/Pyrimidines as Potent P2Y₁₂ Antagonists for Inhibition of Platelet Aggregation

Ming

Wang

et al. 2011

J. Chem. Inf. Model.

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“…Some other application of various versions of ACO in descriptor selection of QSPR/QSAR can be found in [265][266][267][268][269][270].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Chemometrics tools in QSAR/QSPR studies: A historical perspective

Yousefinejad

Hemmateenejad

2015

Chemometrics and Intelligent Laboratory Systems

121

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“…Ant colony optimisation (ACO) , a new method for selecting descriptors, is proposed for feature selection in QSPR modelling. As a case study, this method was used to aid the prediction of the absorption maxima of 1,4‐naphthoquinone derivatives.…”

Section: Quantitative Structure‐activity/property Relationship Studiementioning

confidence: 99%

Review of quantitative structure‐activity/property relationship studies of dyes: recent advances and perspectives

Luan

Liu

et al. 2013

Coloration Technology

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Dyes have been applied and are playing an increasingly important role in many industries, including the textile, printing, medical and energy industries. Their wide applications imply that specific dyes possessing given properties need to be effectively designed. The present review aims to survey information related to activity/property research of dyes that has been published in the past two decades. Emphasis is laid particularly on studies based on quantitative structure–activity/property relationships that have contributed to the theoretical design and application of dyes. Finally, the perspectives of quantitative structure‐activity/property relationship studies are set out in order to show how this method may be used to design new dyes and to evaluate their different properties. The challenges facing these studies are also outlined.

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Prediction of λmax of 1,4-naphthoquinone derivatives using ant colony optimization

Cited by 12 publications

References 21 publications

Combined 3D-QSAR, Molecular Docking, and Molecular Dynamics Study on Piperazinyl-Glutamate-Pyridines/Pyrimidines as Potent P2Y₁₂ Antagonists for Inhibition of Platelet Aggregation

Combined 3D-QSAR, Molecular Docking, and Molecular Dynamics Study on Piperazinyl-Glutamate-Pyridines/Pyrimidines as Potent P2Y₁₂ Antagonists for Inhibition of Platelet Aggregation

Chemometrics tools in QSAR/QSPR studies: A historical perspective

Review of quantitative structure‐activity/property relationship studies of dyes: recent advances and perspectives

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Prediction of λmax of 1,4-naphthoquinone derivatives using ant colony optimization

Cited by 12 publications

References 21 publications

Combined 3D-QSAR, Molecular Docking, and Molecular Dynamics Study on Piperazinyl-Glutamate-Pyridines/Pyrimidines as Potent P2Y12 Antagonists for Inhibition of Platelet Aggregation

Combined 3D-QSAR, Molecular Docking, and Molecular Dynamics Study on Piperazinyl-Glutamate-Pyridines/Pyrimidines as Potent P2Y12 Antagonists for Inhibition of Platelet Aggregation

Chemometrics tools in QSAR/QSPR studies: A historical perspective

Review of quantitative structure‐activity/property relationship studies of dyes: recent advances and perspectives

Contact Info

Product

Resources

About

Combined 3D-QSAR, Molecular Docking, and Molecular Dynamics Study on Piperazinyl-Glutamate-Pyridines/Pyrimidines as Potent P2Y₁₂ Antagonists for Inhibition of Platelet Aggregation

Combined 3D-QSAR, Molecular Docking, and Molecular Dynamics Study on Piperazinyl-Glutamate-Pyridines/Pyrimidines as Potent P2Y₁₂ Antagonists for Inhibition of Platelet Aggregation