ArticleBioMed research international2013
Pharmacophore modeling and docking studies on some nonpeptide-based caspase-3 inhibitors.
Article in BioMed research international, 2013. 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.
- Structure-based machine learning model for discovering pregnane X receptor (PXR) agonists and biological activity validation.Archives of toxicology · 2026Article
- Rational in silico design of PPARγ agonists for type 2 diabetes: an integrated study using pharmacophore modeling, 3D-QSAR, molecular docking, MD simulations, DFT, and toxicity prediction.Molecular diversity · 2026Article
- Current and emerging therapeutic targets of alzheimer's disease for the design of multi-target directed ligands.MedChemComm · 2019Review
- Screening of caspase-3 inhibitors from natural molecule database using e-pharmacophore and docking studies.Bioinformation · 2019Article
- Machine Learning From Molecular Dynamics Trajectories to Predict Caspase-8 Inhibitors Against Alzheimer's Disease.Frontiers in pharmacology · 2019Article
- Implication of Caspase-3 as a Common Therapeutic Target for Multineurodegenerative Disorders and Its Inhibition Using Nonpeptidyl Natural Compounds.BioMed research international · 2015Article
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3 authors.
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Abstract
Neurodegenerative disorders are major consequences of excessive apoptosis caused by a proteolytic enzyme known as caspase-3. Therefore, caspase-3 inhibition has become a validated therapeutic approach for neurodegenerative disorders. We performed pharmacophore modeling on some synthetic derivatives of caspase-3 inhibitors (pyrrolo[3,4-c]quinoline-1,3-diones) using PHASE 3.0. This resulted in the common pharmacophore hypothesis AAHRR.6 which might be responsible for the biological activity: two aromatic rings (R) mainly in the quinoline nucleus, one hydrophobic (H) group (CH₃), and two acceptor (A) groups (-C=O). After identifying a valid hypothesis, we also developed an atom-based 3D-QSAR model applying the PLS algorithm. The developed model was statistically robust (q² = 0.53; pred_r² = 0.80). Additionally, we have performed molecular docking studies, cross-validated our results, and gained a deeper insight into its molecular recognition process. Our developed model may serve as a query tool for future virtual screening and drug designing for this particular target.
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