Abstract：
Pharmacophore modelling, 3 D QSAR modelling, virtual screening, and molecular dynamics study, all-in-one combination were employed successfully design and develop an alpha-glucosidase inhibitor. To explain the structural prerequisites of biologically active components, 3 D-QSAR models were generated using the selected best hypothesis (AARRR) for compounds 55 included in the model C. The selection of 3 D-QSAR models showed that the Gaussian steric characteristic is crucial to alpha glucosidase\'s inhibitory potential. The alpha-glucosidase inhibitory potency of the compound is enhanced by other components, including Gaussian hydrophobic groups, Gaussian hydrogen bond acceptor or donor groups, Gaussian electrostatic characteristics, and a Gaussian steric feature. An identification of structure-activity relationships can be obtained from the developed 3 D-QSAR, C model, with R2 = 0.77 and SD = 0.02 for training set, and Q2 = 0.66, RMSE 0.02, and Pearson R = 0.81 for testing set, corresponding to elevated predictive ability. Additionally, docking and MM/GBSA experiments on 1146023 showed that it interacts with critical amino acids in the binding site when coupled with acarbose. Further, five compounds that display a high affinity for alpha-glucosidase were found, and these compounds may serve as potent leads for alpha-glucosidase inhibitor development. Biological activity will be tested for these compounds in the future.Communicated by Ramaswamy H. Sarma.
摘要：
药效团建模，三维QSAR建模，虚拟筛选,和分子动力学研究，多合一组合被成功地设计和开发了一种α-葡萄糖苷酶抑制剂.为了解释生物活性成分的结构先决条件，使用对于模型C中包括的化合物55的选择的最佳假设(AARRR)产生3D-QSAR模型。3D-QSAR模型的选择显示高斯空间特征对于α葡糖苷酶的抑制潜力是至关重要的。该化合物的α-葡萄糖苷酶抑制效力被其他成分增强，包括高斯疏水基团，高斯氢键受体或供体基团，高斯静电特性，和高斯空间特征。可以从开发的3D-QSAR中获得结构-活性关系的鉴定，C型号,对于训练集，R2=0.77，SD=0.02，对于测试集，Q2=0.66，RMSE0.02，PearsonR=0.81，对应于预测能力的提高。此外,在1146023上的对接和MM/GBSA实验表明，当与阿卡波糖偶联时，它与结合位点的关键氨基酸相互作用。Further,发现了五种对α-葡萄糖苷酶具有高亲和力的化合物，这些化合物可以作为α-葡糖苷酶抑制剂开发的有效线索。将来将对这些化合物的生物活性进行测试。由RamaswamyH.Sarma沟通。
