scholarly journals Investigation of NMDA Receptor Channel Blockers in a Series of Methylene Blue Conjugates Using QSAR and Molecular Modeling

2019 ◽  
Vol 2 (2) ◽  
pp. e00091
Author(s):  
V.Y. Grigorev ◽  
K.A. Shcherbakov ◽  
D.E. Polianczyk ◽  
А.N. Razdolsky ◽  
A.V. Veselovsky ◽  
...  
Keyword(s):  
Methylene Blue ◽  
Nmda Receptor ◽  
Support Vector ◽  
Channel Blockers ◽  
Receptor Binding Site ◽  
Chemical Structures ◽  
Qsar Models ◽  
Derivatives Of ◽  
Nmda Receptor Channel

29 conjugates of methylene blue and four chemical structures, including derivatives of carbazole, tetrahydrocarbazole, substituted indoles and γ-carboline, combined with a 1-oxopropylene spacer have been studied as channel blockers of the NMDA receptor (binding site of MK-801) by using four QSAR methods (multiple linear regression, random forest, support vector machine, Gaussian process) and molecular docking. QSAR models have satisfactory characteristics. The analysis of regression models at the statistical level revealed an important role of the hydrogen bond in the complex formation. This was also confirmed by the study of modeled by docking complexes. It was found that the increase in the inhibitory activity of the part of compounds could be attributed to appearance of additional H bonds between the ligands and the receptor.

scholarly journals Essential Role of NMDA Receptor Channel ε4 Subunit (GluN2D) in the Effects of Phencyclidine, but Not Methamphetamine

PLoS ONE ◽  
2010 ◽  
Vol 5 (10) ◽  
pp. e13722 ◽  
Author(s):  
Yoko Hagino ◽  
Shinya Kasai ◽  
Wenhua Han ◽  
Hideko Yamamoto ◽  
Toshitaka Nabeshima ◽  
...  
Keyword(s):  
Nmda Receptor ◽  
Essential Role ◽  
Nmda Receptor Channel ◽  
Receptor Channel

scholarly journals Cheminformatics-aided pharmacovigilance: application to Stevens-Johnson Syndrome

2015 ◽  
Vol 23 (5) ◽  
pp. 968-978 ◽  
Author(s):  
Yen S Low ◽  
Ola Caster ◽  
Tomas Bergvall ◽  
Denis Fourches ◽  
Xiaoling Zang ◽  
...  
Keyword(s):  
Area Under The Curve ◽  
Individual Case ◽  
Knowledge Bases ◽  
Molecular Structures ◽  
Stevens Johnson Syndrome ◽  
Support Vector ◽  
Chemical Structures ◽  
Johnson Syndrome ◽  
Qsar Models ◽  
Chemical Descriptors

Abstract Objective Quantitative Structure-Activity Relationship (QSAR) models can predict adverse drug reactions (ADRs), and thus provide early warnings of potential hazards. Timely identification of potential safety concerns could protect patients and aid early diagnosis of ADRs among the exposed. Our objective was to determine whether global spontaneous reporting patterns might allow chemical substructures associated with Stevens-Johnson Syndrome (SJS) to be identified and utilized for ADR prediction by QSAR models. Materials and Methods Using a reference set of 364 drugs having positive or negative reporting correlations with SJS in the VigiBase global repository of individual case safety reports (Uppsala Monitoring Center, Uppsala, Sweden), chemical descriptors were computed from drug molecular structures. Random Forest and Support Vector Machines methods were used to develop QSAR models, which were validated by external 5-fold cross validation. Models were employed for virtual screening of DrugBank to predict SJS actives and inactives, which were corroborated using knowledge bases like VigiBase, ChemoText, and MicroMedex (Truven Health Analytics Inc, Ann Arbor, Michigan). Results We developed QSAR models that could accurately predict if drugs were associated with SJS (area under the curve of 75%–81%). Our 10 most active and inactive predictions were substantiated by SJS reports (or lack thereof) in the literature. Discussion Interpretation of QSAR models in terms of significant chemical descriptors suggested novel SJS structural alerts. Conclusions We have demonstrated that QSAR models can accurately identify SJS active and inactive drugs. Requiring chemical structures only, QSAR models provide effective computational means to flag potentially harmful drugs for subsequent targeted surveillance and pharmacoepidemiologic investigations.

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