title: Predicting the DPP-IV inhibitory activity pIC₅₀ based on their physicochemical properties.
abstract: 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.
authors.affiliation: School of Materials Science and Engineering, Shanghai University, 149 Yan-Chang Road, Shanghai 200072, China.
authors.forename: Tianhong
authors.initials: T
authors.lastname: Gu
authors.forename: Xiaoyan
authors.initials: X
authors.lastname: Yang
authors.forename: Minjie
authors.initials: M
authors.lastname: Li
authors.forename: Milin
authors.initials: M
authors.lastname: Wu
authors.forename: Qiang
authors.initials: Q
authors.lastname: Su
authors.forename: Wencong
authors.initials: W
authors.lastname: Lu
authors.forename: Yuhui
authors.initials: Y
authors.lastname: Zhang
mesh_terms: D000465:Algorithms; D055598:Chemical Phenomena; D018819:Dipeptidyl Peptidase 4 / Q000378:metabolism*; D054873:Dipeptidyl-Peptidase IV Inhibitors / Q000737:chemistry* / Q000494:pharmacology*; D006801:Humans; D020128:Inhibitory Concentration 50; D008958:Models, Molecular
chemical_list: D054873:Dipeptidyl-Peptidase IV Inhibitors; D018819:Dipeptidyl Peptidase 4