Predicting the DPP-IV Inhibitory Activity pIC50 Based on Their Physicochemical Properties.

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) wa...

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Detalles Bibliográficos
Publicado en:	BioMed Research International Vol. 2013; pp. 798743 - 798744
Autores principales:	Gu, Tianhong, Yang, Xiaoyan, Li, Minjie, Wu, Milin, Su, Qiang, Lu, Wencong, Zhang, Yuhui
Formato:	Journal Article
Publicado:	Wiley-Blackwell 2013
Acceso en línea:	Ver este registro en EBSCOhost

Descripción
Sumario:	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.