ArticleFrontiers in molecular biosciences2024
Revealing innovative JAK1 and JAK3 inhibitors: a comprehensive study utilizing QSAR, 3D-Pharmacophore screening, molecular docking, molecular dynamics, and MM/GBSA analyses.
Article in Frontiers in molecular biosciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 14 citations in OpenAlex.
- Next-generation Janus kinase inhibitors: Integrating synthetic innovation, structural biology, and computational design for precision drug discovery.Pharmaceutical science advances · 2026Review
- Antibody Screening and Binding Prediction Analysis Targeting Stx2.Antibodies (Basel, Switzerland) · 2026Article
- Synthesis and biological evaluation of thiourea-tethered benzodiazepinones as anti-proliferative agents targeting JAK-3 kinase.Naunyn-Schmiedeberg's archives of pharmacology · 2025Article
- Molecular docking and dynamics in protein serine/threonine kinase drug discovery: advances, challenges, and future perspectives.Frontiers in pharmacology · 2025Review
- Computational insights into rational design and virtual screening of pyrazolopyrimidine derivatives targeting Janus kinase 3 (JAK3).Frontiers in chemistry · 2024Article
- Development of novel CDK9 and CYP3A4 inhibitors for cancer therapy through field and computational approaches.Frontiers in chemistry · 2024Article
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Authors and funding
8 authors at 5 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The heterocycle compounds, with their diverse functionalities, are particularly effective in inhibiting Janus kinases (JAKs). Therefore, it is crucial to identify the correlation between their complex structures and biological activities for the development of new drugs for the treatment of rheumatoid arthritis (RA) and cancer. In this study, a diverse set of 28 heterocyclic compounds selective for JAK1 and JAK3 was employed to construct quantitative structure-activity relationship (QSAR) models using multiple linear regression (MLR). Artificial neural network (ANN) models were employed in the development of QSAR models. The robustness and stability of the models were assessed through internal and external methodologies, including the domain of applicability (DoA). The molecular descriptors incorporated into the model exhibited a satisfactory correlation with the receptor-ligand complex structures of JAKs observed in X-ray crystallography, making the model interpretable and predictive. Furthermore, pharmacophore models ADRRR and ADHRR were designed for each JAK1 and JAK3, proving effective in discriminating between active compounds and decoys. Both models demonstrated good performance in identifying new compounds, with an ROC of 0.83 for the ADRRR model and an ROC of 0.75 for the ADHRR model. Using a pharmacophore model, the most promising compounds were selected based on their strong affinity compared to the most active compounds in the studied series each JAK1 and JAK3. Notably, the pharmacokinetic, physicochemical properties, and biological activities of the selected compounds (As compounds ZINC79189223 and ZINC66252348) were found to be consistent with their therapeutic effects in RA, owing to their non-toxic, cholinergic nature, absence of P-glycoprotein, high gastrointestinal absorption, and ability to penetrate the blood-brain barrier. Furthermore, ADMET properties were assessed, and molecular dynamics and MM/GBSA analysis revealed stability in these molecules.
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