Aurora-A kinase inhibitor scaffolds and binding modes

Yan, Aixia; Wang, Li-Yu; Xu, Shanshan; Xu, Jun

doi:10.1016/j.drudis.2010.12.003

Cited by 49 publications

(58 citation statements)

References 36 publications

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“…These atom groups were key groups which can produce the hydrogen binding for the interaction with Aurora-A kinase. (We cannot confirm that if these interaction patterns exist between each individual inhibitor and Aurora-A kinase, but we really found these important interactions between the inhibitor and the residues Glu211, Ala213 and Lys162 of Aurora-A kinase from most of the known cocomplex crystal structures [60] ). For surface descriptors, it is generally accepted that receptor and substrate molecules recognize each other at their molecular surfaces.…”

Section: Analysis Of the Two Classification Models And The Selected Dmentioning

confidence: 71%

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Classification of Aurora‐A Kinase Inhibitors Using Self‐Organizing Map (SOM) and Support Vector Machine (SVM)

Wang

Yan

et al. 2011

Molecular Informatics

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Two classification models of 148 Aurora-A kinase inhibitors were developed to separate active and weakly potent active inhibitors of Aurora-A kinase. Each molecule was represented by 12 selected molecular descriptors calculated by the ADRIANA.Code. Then the classification models were built using a Kohonen's Self-Organizing Map (SOM) and a Support Vector Machine (SVM) method, respectively, which could be used for virtual screening an existing database to find possible new lead compounds with higher activity. The prediction accuracy of the models for the training and test sets are 96.6 % and 90.0 % for SOM, 93.2 % and 93.3 % for SVM.

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Section: Analysis Of the Two Classification Models And The Selected Dmentioning

confidence: 71%

“…The scaffolds were shown in Scheme 1. We divided the scaffolds of the compounds according to the literature [4,[10][11][12][13][15][16][17]60,61] . Scaffold 1 [10] contains a core of thienopyrimidine, the compounds in subset 1 contain scaffold 1, which includes 24 inhibitors.…”

Section: Validation the Models Built By The Subset Of Datasetmentioning

confidence: 99%

Classification of Aurora‐A Kinase Inhibitors Using Self‐Organizing Map (SOM) and Support Vector Machine (SVM)

Wang

Yan

et al. 2011

Molecular Informatics

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“…The sulphonamide group was located entirely within a hydrophobic region at the front of the ATP binding pocket and extended toward the solvent accessible front [23]. The hydrophobic aromatic rings such as pyrazole, isothiazole and imidazo [1, 2-a] pyrazine, had a quite positive interaction with the hydrophobic ATP binding pocket [24].…”

Section: Structure-based Ligand Docking Analysismentioning

confidence: 99%

“…However, some pharmacophore models with good statistics were not able to reflect the key interactions between the inhibitor and Aurora A kinase. Only one of the six CPHs, CPH5, represents the important structural requirements for the Aurora A kinase inhibitors, especially the key hydrogen bonds in the hinge region, which are important for inhibitory activity [24]. The CPH5 contains one hydrogen bond acceptor, two hydrogen bond donors and two aromatic rings.…”

Section: Generation and Selection Of The Common Pharmacophore Hypothesismentioning

confidence: 99%

QSAR studies on imidazopyrazine derivatives as Aurora A kinase inhibitors

Liu

Lü

Yuan

et al. 2012

SAR and QSAR in Environmental Research

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Aurora kinases have emerged as attractive targets for the development of novel anti-cancer agents. A combined study of molecular docking, pharmacophore modelling and 3D-QSAR was performed on a series of imidazo [1, 2-a] pyrazines as novel Aurora kinase inhibitors to gain insights into the structural determinants and their structure-activity relationship. An ensemble of conformations based on molecular docking was used for PHASE pharmacophore studies. The developed best-fitted pharmacophore model was validated by diverse chemotypes of Aurora A kinase inhibitors and was consistent with the structural requirements for the docked binding mechanism. Subsequently, the pharmacophore-based alignment was used to develop PHASE and comparative molecular similarity indices analysis (CoMSIA) 3D-QSAR models. The best CoMSIA model showed good statistics (q (2 )= 0.567, r (2 )= 0.992), and the predictive ability of the model was validated using an external test set of 13 compounds giving a satisfactory prediction ([Formula: see text]). The 3D contour maps provided insight into the binding mechanism and highlighted key structural features that are essential to the inhibitory activity. Based on the PHASE and CoMSIA 3D-QSAR results, a set of novel Aurora A inhibitors were designed that showed excellent potencies.

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“…Thus, the inhibition of any sub-types of the Aurora kinases causes cancer cell death by apoptosis and mitotic catastrophe [9]. To date, many aurora kinase inhibitors have been reported [10]. Among them, VX680 [11] was the first to enter clinical trials, but it was later withdrawn due to cardiac toxicity [12].…”

Section: Introductionmentioning

confidence: 99%

Benzo[e]pyrimido[5,4-b][1,4]diazepin-6(11H)-one derivatives as Aurora A kinase inhibitors: LQTA-QSAR analysis and detailed systematic validation of the developed model

et al. 2015

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Aurora kinases are sub-divided into Aurora A, Aurora B, and Aurora C kinases that are considered as prospective targets for a new class of anticancer drugs. In this work, a 4-D-QSAR model using an LQTA-QSAR approach with previously reported 31 derivatives of benzo[e]pyrimido[5,4 -b][1,4]diazepin -6(11H)-one as potent Aurora kinase A inhibitors has been created. Instead of single conformation, the conformational ensemble profile generated for each ligand by using trajectories and topology information retrieved from molecular dynamics simulations from GROMACS package were aligned and used for the calculation of intermolecular interaction energies at each grid point. The descriptors generated on the basis of these Coulomb and Lennard-Jones potentials as independent variables were used to perform a PLS analysis using biological activity as dependent variable. A good predictive model was generated with nine field descriptors and five latent variables. The model showed [Formula: see text]; [Formula: see text] and [Formula: see text]. This model was further validated systematically by using different validation parameters. This 4D-QSAR model gave valuable information to recognize features essential to adapt and develop novel potential Aurora kinase inhibitors.

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Aurora-A kinase inhibitor scaffolds and binding modes

Cited by 49 publications

References 36 publications

Classification of Aurora‐A Kinase Inhibitors Using Self‐Organizing Map (SOM) and Support Vector Machine (SVM)

Classification of Aurora‐A Kinase Inhibitors Using Self‐Organizing Map (SOM) and Support Vector Machine (SVM)

QSAR studies on imidazopyrazine derivatives as Aurora A kinase inhibitors

Benzo[e]pyrimido[5,4-b][1,4]diazepin-6(11H)-one derivatives as Aurora A kinase inhibitors: LQTA-QSAR analysis and detailed systematic validation of the developed model

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