pEC3=-0.425(±0.124)*clogP – 0.152(±0.0412)*ETS2 + 6.17
(±1.22) Equation 2
n=21, r2=0.71, r2adj
=0.46, p=0.002
As a final exercise, the dataset of 22 compounds was partitioned into a
training (N=14) and test set (N=8). As with the work of Roberts et
al. , we generated the QSAR model on a set consisting primarily of
nonfunctional aldehydes and ketones to avoid confounding effects. A
small number of additional exemplars that help cover the full range in
pEC3 were also included (Table 2). All compounds were
predicted using equation 3. Compounds 2 , 4 and
18 were also predicted using our previously reported
model for SNAr
domain.[54] Again, a two-parameter
model was fitted using the training data resulting in equation 3. The
training set explained variance is somewhat lower than observed with the
larger combined set (r2=0.40), however the descriptor
coefficients are qualitatively similar. Prediction on the test set of
compounds show the compounds are quite well ranked
(r2=0.49). A noticeable outlier in figure 3 is
compound 17 which on further analysis of the structure
can also potentially function via the acyl reaction
domain.[5] This could account for its
low predicted activity from this Schiff-base derived model. When
compounds 20 (SNAr domain) and
17 (Acyl domain) are excluded from the test set,
r2 of 0.62 is observed.
pEC3 = -0.388(±0.150)*clogP – 0.172(±0.061)*ETS2 +
6.671 (±1.841) Equation 3
Training set (n=14, r2=0.49,
r2adj=0.40, p=0.02),
Test set (n=8, r2=0.49), Test set (n=6 (ex
17 & 20 ), r2=0.62)