ArticleJournal of computer-aided molecular design2013
Elaborate ligand-based modeling coupled with QSAR analysis and in silico screening reveal new potent acetylcholinesterase inhibitors.
Article in Journal of computer-aided molecular design, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- An In Silico Study Based on QSAR and Molecular Docking and Molecular Dynamics Simulation for the Discovery of Novel Potent Inhibitor against AChE.Pharmaceuticals (Basel, Switzerland) · 2024Article
- Exploiting activity cliffs for building pharmacophore models and comparison with other pharmacophore generation methods: sphingosine kinase 1 as case study.Journal of computer-aided molecular design · 2022Article
- A Comprehensive Review of Cholinesterase Modeling and Simulation.Biomolecules · 2021Review
- Design of Curcumin and Flavonoid Derivatives with Acetylcholinesterase and Beta-Secretase Inhibitory Activities Using in Silico Approaches.Molecules (Basel, Switzerland) · 2020Article
- Computational modeling of the bat HKU4 coronavirus 3CLJournal of molecular recognition : JMR · 2017Article
- Combining molecular dynamics simulation and ligand-receptor contacts analysis as a new approach for pharmacophore modeling: beta-secretase 1 and check point kinase 1 as case studies.Journal of computer-aided molecular design · 2016Article
- Identification of novel inhibitors for Pim-1 kinase using pharmacophore modeling based on a novel method for selecting pharmacophore generation subsets.Journal of computer-aided molecular design · 2016Article
- Combining docking-based comparative intermolecular contacts analysis and k-nearest neighbor correlation for the discovery of new check point kinase 1 inhibitors.Journal of computer-aided molecular design · 2015Article
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3 authors.
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Abstract
Inhibition of the enzyme acetylcholinesterase (AChE) has been shown to alleviate neurodegenerative diseases prompting several attempts to discover and optimize new AChE inhibitors. In this direction, we explored the pharmacophoric space of 85 AChE inhibitors to identify high quality pharmacophores. Subsequently, we implemented genetic algorithm-based quantitative structure-activity relationship (QSAR) modeling to select optimal combination of pharmacophoric models and 2D physicochemical descriptors capable of explaining bioactivity variation among training compounds (r2(68)=0.94, F-statistic=125.8, r2 LOO=0.92, r2 PRESS against 17 external test inhibitors = 0.84). Two orthogonal pharmacophores emerged in the QSAR equation suggesting the existence of at least two binding modes accessible to ligands within AChE binding pocket. The successful pharmacophores were comparable with crystallographically resolved AChE binding pocket. We employed the pharmacophoric models and associated QSAR equation to screen the national cancer institute list of compounds. Twenty-four low micromolar AChE inhibitors were identified. The most potent gave IC50 value of 1.0 μM.
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