Using DFT methodology for more reliable predictive models: Design of inhibitors of Golgi α-Mannosidase II
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- 作者：Adela Bobovská ; a ; b ; chembobo@savba.sk" class="auth_mail" title="E-mail the corresponding author ; Igor Tvaro&scaron ; kaa ; chemitsa@savba.sk" class="auth_mail" title="E-mail the corresponding author ; Juraj Kó ; ňaa ; chemkona@savba.sk" class="auth_mail" title="E-mail the corresponding author
- 关键词：GMII ; Golgi α-mannosidase II ; LMan ; lysosomal α-mannosidase ; QM-DFT ; quantum mechanical density functional theory calculations ; GH ; glycoside hydrolase ; E.C. ; enzyme commission number ; I-E ; inhibitor-enzyme complex ; I-BS ; inhibitor-binding site complex ; I-BSF ; inhibitor-binding site fragment ; ED ; empirical descriptor ; MMD ; molecular mechanics descriptor ; LIE ; linear interaction energy method
- 刊名：Journal of Molecular Graphics and Modelling
- 出版年：2016
- 出版时间：May 2016
- 年：2016
- 卷：66
- 期：Complete
- 页码：47-57
- 全文大小：1810 K
文摘
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QSAR models for targeted and side-effects receptors were built.
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A required prediction accuracy was reached using QM interaction energy descriptors.
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Robustness of structurally fragmented QM descriptors was demonstrated.
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Hybrid empirical-QM QSAR models are applicable for lead optimization.
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The computational workflow is feasible on a personal computer.