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• W3102297515 endingPage "107797" @default.
• W3102297515 startingPage "107797" @default.
• W3102297515 abstract "The synthetic glucose receptors help to develop glucose sensors and alternative insulin therapies. Designing a glucose recognition molecule in an aqueous system remains a considerable challenge. Therefore, In-silico molecular screening hypothesis is proposed to overcome the difficulties found during the modeling of a molecule. The small organic compounds from compound databases are screened for glucose receptor modeling. Thereafter, the different computational models are designed that mimic natural glucose receptors based on screened compounds. The orientation and binding of glucose molecules within the developed receptor are predicted through the molecular interaction approach. The modeled receptors and receptor-glucose complex structures are used for geometry optimization and molecular dynamics computation. The docking results reveal that ZINC82047919, ZINC238094340, and ZINC238519600 compounds-based models provide better interactions with glucose and its orientation within the receptor cavity. The molecular dynamics simulation results showed that the receptor designed using compound ZINC238094340 is unable to hold the glucose and undergo significant conformation changes during simulation process. The receptor designed from ZINC238094340 and ZINC238519600 compounds is utilized as a reference glucose binding receptor in this study. The proposed computational approach is able to develop a novel glucose receptor and other glucose relative sugar molecules." @default.
• W3102297515 created "2020-11-23" @default.
• W3102297515 creator A5000929601 @default.
• W3102297515 creator A5005700461 @default.
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• W3102297515 date "2021-03-01" @default.
• W3102297515 modified "2023-09-30" @default.
• W3102297515 title "Design a synthetic glucose receptor using computational intelligence approach" @default.
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• W3102297515 doi "https://doi.org/10.1016/j.jmgm.2020.107797" @default.
• W3102297515 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33246193" @default.
• W3102297515 hasPublicationYear "2021" @default.
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