Aims & Scope: 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.
Materials & Methods: 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 models 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-UVEPLS
and SPA-jackknife increases the predictive power and stability of CoMFA models significantly.
Result & Conclusion: Among them, SPA-jackknife removes most of the variables while FFD retains
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.