ArticleJournal of molecular modeling2017
Docking-assisted 3D-QSAR studies on xanthones as α-glucosidase inhibitors.
Article in Journal of molecular modeling, 2017. 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.
- Screening of Dietary Flavonoids for Synergistic α-Glucosidase Inhibition with 1-Deoxynojirimycin and Elucidation of the Underlying Molecular Mechanism.Molecules (Basel, Switzerland) · 2026Article
- Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors.Computational and structural biotechnology journal · 2024Review
- 3D-QSAR, Scaffold Hopping, Virtual Screening, and Molecular Dynamics Simulations of Pyridin-2-one as mIDH1 Inhibitors.International journal of molecular sciences · 2024Article
- In silico discovery of potential PPI inhibitors for anti-lung cancer activity by targeting the CCND1-CDK4 complex via the P21 inhibition mechanism.Frontiers in chemistry · 2024Article
- Synthesis and Molecular Docking Studies of Alkoxy- and Imidazole-Substituted Xanthones as α-Amylase and α-Glucosidase Inhibitors.Molecules (Basel, Switzerland) · 2023Article
- Characterization of α-Glucosidase Inhibitors fromMolecules (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, a series of xanthone analogues has been identified as α-glucosidase inhibitors. To provide deeper insight into the three-dimensional (3D) structural requirements for the activities of these molecules, CoMFA and CoMSIA approaches were employed on 54 xanthones to construct 3D-QSAR models. Their bioactive conformations were first investigated by docking studies and optimized by subsequent molecular dynamics (MD) simulations using the homology modeled structure of the target protein. Based on the docking/MD-determined conformers, 3D-QSAR studies generated several significant models in terms of 47 molecules as the training set. The best model (CoMSIA-SHA) yielded q
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