School of Materials Science and Engineering, Shanghai University, 149 Yan-Chang Road, Shanghai 200072, China.
Biomed Res Int. 2013;2013:798743. doi: 10.1155/2013/798743. Epub 2013 Jun 20.
The second development program developed in this work was introduced to obtain physicochemical properties of DPP-IV inhibitors. Based on the computation of molecular descriptors, a two-stage feature selection method called mRMR-BFS (minimum redundancy maximum relevance-backward feature selection) was adopted. Then, the support vector regression (SVR) was used in the establishment of the model to map DPP-IV inhibitors to their corresponding inhibitory activity possible. The squared correlation coefficient for the training set of LOOCV and the test set are 0.815 and 0.884, respectively. An online server for predicting inhibitory activity pIC50 of the DPP-IV inhibitors as described in this paper has been given in the introduction.
本工作开发的第二个开发程序旨在获得 DPP-IV 抑制剂的物理化学性质。基于分子描述符的计算，采用了一种称为 mRMR-BFS（最小冗余最大相关性-后向特征选择）的两阶段特征选择方法。然后，支持向量回归（SVR）用于建立模型，将 DPP-IV 抑制剂映射到它们相应的抑制活性上。LOOCV 和测试集的训练集的平方相关系数分别为 0.815 和 0.884。本文中描述的 DPP-IV 抑制剂抑制活性 pIC50 的在线预测服务器已在引言中给出。
