Assessing potency of c-Jun N-terminal kinase 3 (JNK3) inhibitors using 2D molecular descriptors and binary QSAR methodology

Ijjaali, Ismaïl; Petitet, François; Dubus, Elodie; Barberan, Olivier; Michel, Aurélia

doi:10.1016/j.bmc.2007.03.062

Cited by 18 publications

(4 citation statements)

References 24 publications

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“…Molecular descriptors quantitatively represent structural and physicochemical features of molecules, and have been extensively used in deriving structure−activity relationships, quantitative structure−activity relationships and VS tools. ,, We used 98 1D and 2D descriptors (Table ) derived from our software, which include 18 descriptors in the class of simple molecular properties, 3 descriptors in the class of chemical properties, 35 descriptors in the class of molecular connectivity and shape, and 42 descriptors in the class of electrotopological state.…”

Section: Methodsmentioning

confidence: 99%

Virtual Screening of Selective Multitarget Kinase Inhibitors by Combinatorial Support Vector Machines

Wang

Tan

et al. 2010

Mol. Pharmaceutics

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Multitarget agents have been increasingly explored for enhancing efficacy and reducing countertarget activities and toxicities. Efficient virtual screening (VS) tools for searching selective multitarget agents are desired. Combinatorial support vector machines (C-SVM) were tested as VS tools for searching dual-inhibitors of 11 combinations of 9 anticancer kinase targets (EGFR, VEGFR, PDGFR, Src, FGFR, Lck, CDK1, CDK2, GSK3). C-SVM trained on 233-1,316 non-dual-inhibitors correctly identified 26.8%-57.3% (majority >36%) of the 56-230 intra-kinase-group dual-inhibitors (equivalent to the 50-70% yields of two independent individual target VS tools), and 12.2% of the 41 inter-kinase-group dual-inhibitors. C-SVM were fairly selective in misidentifying as dual-inhibitors 3.7%-48.1% (majority <20%) of the 233-1,316 non-dual-inhibitors of the same kinase pairs and 0.98%-4.77% of the 3,971-5,180 inhibitors of other kinases. C-SVM produced low false-hit rates in misidentifying as dual-inhibitors 1,746-4,817 (0.013%-0.036%) of the 13.56 M PubChem compounds, 12-175 (0.007%-0.104%) of the 168 K MDDR compounds, and 0-84 (0.0%-2.9%) of the 19,495-38,483 MDDR compounds similar to the known dual-inhibitors. C-SVM was compared to other VS methods Surflex-Dock, DOCK Blaster, kNN and PNN against the same sets of kinase inhibitors and the full set or subset of the 1.02 M Zinc clean-leads data set. C-SVM produced comparable dual-inhibitor yields, slightly better false-hit rates for kinase inhibitors, and significantly lower false-hit rates for the Zinc clean-leads data set. Combinatorial SVM showed promising potential for searching selective multitarget agents against intra-kinase-group kinases without explicit knowledge of multitarget agents.

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Section: Methodsmentioning

confidence: 99%

Virtual Screening of Selective Multitarget Kinase Inhibitors by Combinatorial Support Vector Machines

Wang

Tan

et al. 2010

Mol. Pharmaceutics

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“…It has been reported that compound XII inhibits PDGF with IC 50 of 8 µM and EGFR with IC 50 of 20 µM [51]. In addition, quinoline alkaloids with phenol ring such as compound XI (3-(4-Hydroxyphenyl)quinoline) has mild mitogen-activated protein kinases activity and this is due to its high similarity with CHEMBL248643 which can inhibit mitogen-activated protein kinase p38 with IC 50 of 20 µM and c-Jun N-terminal kinase 3 with IC 50 of 0.59 µM [52,53].…”

Section: Quinoline Alkaloidsmentioning

confidence: 99%

Chemoinformatic analysis of alkaloids isolated from Peganum genus

Bayazeid

Nasibova

2021

Mol Divers

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Peganum genus is rich with its high phytochemical and botanical variability. Peganum species have been used as sedative, antitumor, analgesics, antidepressant, and other medicinal purposes. The aim of this research is to study the molecular diversity of Peganum genus to shed more lights on the structureactivity relationship of the alkaloids isolated from Peganum genus. All Peganum alkaloids were grouped according to their structure properties. A chemoinformatic approach (Swiss Model) was used to determine the molecular targets of these alkaloids. To visualize the results, R programming language was used to generate hierarchical clustering heatmaps. The results of this study helps researcher to better understanding of the structural-activity relationship of Peganum alkaloids.

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“…Shaikh et al, 3 for example, presented a 3D-QSAR approach in combination with docking studies on 44 (benzothiazole-2-yl)acetonitrile derivatives as JNK3 inhibitors in their work yet without publishing the used data set. 3 A binary 2D-QSAR approach was conducted on the proprietary Aureus Kinase knowledge database by Ijjaali et al 4 Chung et al investigated a receptor-guided 3D-QSAR analysis of anilinobipyridines as JNK3 inhibitors. 5 They used the methodologies of CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity indices analysis) for their studies.…”

Section: ' Introductionmentioning

confidence: 99%

Modeling and Benchmark Data Set for the Inhibition of c-Jun N-terminal Kinase-3

Schattel

Hinselmann

Jahn

et al. 2011

J. Chem. Inf. Model.

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The goal of this paper is to present and describe a novel 2D- and 3D-QSAR (quantitative structure-activity relationship) binary classification data set for the inhibition of c-Jun N-terminal kinase-3 with previously unpublished activities for a diverse set of compounds. JNK3 is an important pharmaceutical target because it is involved in many neurological disorders. Accordingly, the development of JNK3 inhibitors has gained increasing interest. 2D and 3D versions of the data set were used, consisting of 313 (70 actives) and 249 (60 actives) compounds, respectively. All compounds, for which activity was only determined for the racemate, were removed from the 3D data set. We investigated the diversity of the data sets by an agglomerative clustering with feature trees and show that the data set contains several different scaffolds. Furthermore, we show that the benchmarks can be tackled with standard supervised learning algorithms with a convincing performance. For the 2D problem, a random decision forest classifier achieves a Matthew's correlation coefficient of 0.744, the 3D problem could be modeled with a Matthew's correlation coefficient of 0.524 with 3D pharmacophores and a support vector machine. The performance of both data sets was evaluated within a nested 10-fold cross-validation. We therefore suggest that the data set is a reasonable basis for generating QSAR models for JNK3 because of its diverse composition and the performance of the classifiers presented in this study.

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Assessing potency of c-Jun N-terminal kinase 3 (JNK3) inhibitors using 2D molecular descriptors and binary QSAR methodology

Cited by 18 publications

References 24 publications

Virtual Screening of Selective Multitarget Kinase Inhibitors by Combinatorial Support Vector Machines

Virtual Screening of Selective Multitarget Kinase Inhibitors by Combinatorial Support Vector Machines

Chemoinformatic analysis of alkaloids isolated from Peganum genus

Modeling and Benchmark Data Set for the Inhibition of c-Jun N-terminal Kinase-3

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