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3D-QSAR (CoMFA and CoMSIA) and pharmacophore (GALAHAD) studies on the differential inhibition of aldose reductase by flavonoid compounds

机译:3D-QSAR(CoMFA和CoMSIA)和药效团(GALAHAD)研究类黄酮化合物对醛糖还原酶的不同抑制作用

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Inhibitory activities of flavonoid derivatives against aldose reductase (AR) enzyme were modelled by using CoMFA, CoMSIA and GALAHAD methods. CoMFA and CoMSIA methods were used for deriving quantitative structure-activity relationship (QSAR) models. All QSAR models were trained with 55 compounds, after which they were evaluated for predictive ability with additional 14 compounds. The best CoMFA model included both steric and electrostatic fields, meanwhile, the best CoMSIA model included steric, hydrophobic and H-bond acceptor fields. These models had a good predictive quality according to both internal and external validation criteria. On the other hand, GALAHAD was used for deriving a 3D pharmacophore model. Twelve active compounds were used for deriving this model. The obtained model included hydrophobe, hydrogen bond acceptor and hydrogen bond donor features; it was able to identify the active AR inhibitors from the remaining compounds. These in silico tools might be useful in the rational design of new AR inhibitors.
机译:使用CoMFA,CoMSIA和GALAHAD方法模拟了类黄酮衍生物对醛糖还原酶(AR)酶的抑制活性。 CoMFA和CoMSIA方法用于推导定量构效关系(QSAR)模型。所有QSAR模型都用55种化合物训练,然后用另外14种化合物对它们的预测能力进行了评估。最好的CoMFA模型包括空间和静电场,同时,最好的CoMSIA模型包括空间,疏水和H键受体场。根据内部和外部验证标准,这些模型具有良好的预测质量。另一方面,GALAHAD用于推导3D药效团模型。使用十二种活性化合物推导该模型。获得的模型包括疏水物,​​氢键受体和氢键供体特征。它能够从其余化合物中鉴定出活性AR抑制剂。这些计算机软件工具可能在合理设计新的AR抑制剂中有用。

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