ArticleJournal of computer-aided molecular design2020
Identification and neuroprotective evaluation of a potential c-Jun N-terminal kinase 3 inhibitor through structure-based virtual screening and in-vitro assay.
Article in Journal of computer-aided molecular design, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 12 citations in OpenAlex.
- Identification of Pharmacophore Responsible for the JNK3 Enzyme Inhibition using KPLS-based QSAR Analysis.Central nervous system agents in medicinal chemistry · 2026Article
- Identification of potential JNK3 inhibitors through virtual screening, molecular docking and molecular dynamics simulation as therapeutics for Alzheimer's disease.Molecular diversity · 2024Article
- Piceatannol improved cerebral blood flow and attenuated JNK3 and mitochondrial apoptotic pathway in a global ischemic model to produce neuroprotection.Naunyn-Schmiedeberg's archives of pharmacology · 2024Article
- Efonidipine Inhibits JNK and NF-κB Pathway to Attenuate Inflammation and Cell Migration Induced by Lipopolysaccharide in Microglial Cells.Biomolecules & therapeutics · 2022Article
- JNK3 as Therapeutic Target and Biomarker in Neurodegenerative and Neurodevelopmental Brain Diseases.Cells · 2020Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
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Abstract
The c-Jun N-terminal kinase 3 (JNK3) signaling cascade is activated during cerebral ischemia leading to neuronal damage. The present study was carried out to identify and evaluate novel JNK3 inhibitors using in-silico and in-vitro approach. A total of 380 JNK3 inhibitors belonging to different organic groups was collected from the previously reported literature. These molecules were used to generate a pharmacophore model. This model was used to screen a chemical database (SPECS) to identify newer molecules with similar chemical features. The top 1000 hits molecules were then docked against the JNK3 enzyme coordinate following GLIDE rigid receptor docking (RRD) protocol. Best posed molecules of RRD were used during induced-fit docking (IFD), allowing receptor flexibility. Other computational predictions such as binding free energy, electronic configuration and ADME/tox were also calculated. Inferences from the best pharmacophore model suggested that, in order to have specific JNK3 inhibitory activity, the molecules must possess one H-bond donor, two hydrophobic and two ring features. Docking studies suggested that the main interaction between lead molecules and JNK3 enzyme consisted of hydrogen bond interaction with methionine 149 of the hinge region. It was also observed that the molecule with better MM-GBSA dG binding free energy, had greater correlation with JNK3 inhibition. Lead molecule (AJ-292-42151532) with the highest binding free energy (dG = 106.8 Kcal/mol) showed better efficacy than the SP600125 (reference JNK3 inhibitor) during cell-free JNK3 kinase assay (IC50 = 58.17 nM) and cell-based neuroprotective assay (EC50 = 7.5 µM).
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