Selected article for: "cross validation and training set"

Author: Xu, Jun; Huang, Sichao; Luo, Haibin; Li, Guoji; Bao, Jiaolin; Cai, Shaohui; Wang, Yuqiang
Title: QSAR Studies on Andrographolide Derivatives as α-Glucosidase Inhibitors
  • Cord-id: s0ps9rd0
  • Document date: 2010_3_2
  • ID: s0ps9rd0
    Snippet: Andrographolide derivatives were shown to inhibit α-glucosidase. To investigate the relationship between activities and structures of andrographolide derivatives, a training set was chosen from 25 andrographolide derivatives by the principal component analysis (PCA) method, and a quantitative structure-activity relationship (QSAR) was established by 2D and 3D QSAR methods. The cross-validation r(2) (0.731) and standard error (0.225) illustrated that the 2D-QSAR model was able to identify the im
    Document: Andrographolide derivatives were shown to inhibit α-glucosidase. To investigate the relationship between activities and structures of andrographolide derivatives, a training set was chosen from 25 andrographolide derivatives by the principal component analysis (PCA) method, and a quantitative structure-activity relationship (QSAR) was established by 2D and 3D QSAR methods. The cross-validation r(2) (0.731) and standard error (0.225) illustrated that the 2D-QSAR model was able to identify the important molecular fragments and the cross-validation r(2) (0.794) and standard error (0.127) demonstrated that the 3D-QSAR model was capable of exploring the spatial distribution of important fragments. The obtained results suggested that proposed combination of 2D and 3D QSAR models could be useful in predicting the α-glucosidase inhibiting activity of andrographolide derivatives.

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