Submit Manuscript  

Article Details


Predictive and descriptive CoMFA models: The effect of variable selection

Author(s):

Bakhtyar Sepehri, Nematollah Omidikia, Mohsen Kompany-Zareh and Raouf Ghavami*  

Abstract:


In this research, 8 variable selection approaches were used to investigate the effect of variable selection on the predictive power and stability of CoMFA models. Three data sets including 36 EPAC antagonists, 79 CD38 inhibitors and 57 ATAD2 bromodomain inhibitors were modelled by CoMFA. First of all, for all three data sets, CoMFA models with all CoMFA descriptors were created then by applying each variable selection method a new CoMFA model was developed so for each data set, 9 CoMFA model were built. Obtained results show noisy and uninformative variables affect CoMFA results. Based on created models, applying 5 variable selection approaches including FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS and SPA-jackknife increase the predictive power and stability of CoMFA models significantly. Among of them, SPA-jackknife removes the most of variables while FFD retains the most of them. FFD and IVE-PLS are time consuming process while SRD-FFD and SRD-UVE-PLS run need to few seconds. Also applying FFD, SRD-FFD, IVE-PLS, SRD-UVE-PLS protect CoMFA countor maps information for both fields.

Keywords:

CoMFA, variable selection, UVE-PLS, IVE-PLS, SPA-jackknife, FFD, SRD, D-optimal design.

Affiliation:

Department of Chemistry, Faculty of Science, University of Kurdistan, Sanandaj, Department of Chemistry, Faculty of Chemistry, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Department of Chemistry, Faculty of Chemistry, Institute for Advanced Studies in Basic Sciences (IASBS), Zanjan, Department of Chemistry, Faculty of Science, University of Kurdistan, Sanandaj



Full Text Inquiry