Biomedical Engineering Reference
In-Depth Information
derivatives. The analysis of the influence of the descriptors highlights that the
descriptor related to two hydrogen donor atoms placed at a distance of 9
˚
has
the highest principal component coefficient. This descriptor is encoded by the
charged nitrogen atom. Also, the descriptors related to the presence of a hydrogen
bond acceptor and a hydrogen bond donor (6
˚
or 9.5
˚
apart), to the presence of
the hydrophobic moiety and a hydrogen bond donor (9
˚
or 16
˚
apart) and to the
hydrogen bond donor and one of the edges of the molecule (10
˚
or 17.5
˚
apart),
show a high correlation with biological activity. These data are in agreement with
the model published by Cianchetta et al. [
40
].
In a recent study, Kramer et al. [
36
] reported a predictive QSAR model able
to distinguish between specific and nonspecific binding. 113 compounds from
the literature were split into six groups of equal activity range. From the 113
compounds, 15 molecule were randomly selected as validation set. The remaining
98 compounds were divided into a training set of 75 molecules and a test set of 23
molecules. The uncharged forms of the compounds were used to calculate 1D-, 2D-,
and 3D-descriptors such as the molecular electrostatic potential (MEP), the local
ionization energy (IEL), the local electron affinity (EAL), the local polarizability
(POL), and the Shannon entropies (SHANI and SHANE). The first model was
obtained using a combination of
-SVR and multiple linear regression descriptor
selection. An
r
2
of 0.81 for the training set was obtained by using EALmax,
EALmin, POLmin, SHANIbar, Naryl, shapeQ2, and shapeQ4 descriptors. The
model shows a good performance also for the validation set with an
r
2
of 0.70,
while the
q
2
is 0.50. In the second model the Naryl, shape and EALmin descriptors
were selected. The statistical analysis of this model resulted in an
r
2
for the training,
test and validation set of 0.64, 0.61, and 0.62, respectively. The Naryl descriptor
encodes for the number of aromatic rings in the molecule, and its selection
highlights that the potency of the blockers is correlated with the number of aromatic
rings. The shape descriptors are a measure of the shape similarity with astemizole,
cisapride and sertindole. They have a positive sign, indicating that the more the
shape of the molecule is similar to those of one of the three compounds cited above,
the higher is the probability of the compound to be a potent blocker. The EALmin
descriptor has a positive value, which might suggest that the potency of the hERG
blockers is decreased if the minimum electron affinity decreases. Additional six
models were generated, all of them showing quite satisfactory performance. These
six models describe a specific or an unspecific binding type. In general, the models
suggest that the affinity of the blockers is correlated with the similarity of the
compounds with the three hERG inhibitors cited above, and with the number of
aromatic rings present. To decrease the hERG potency, it is necessary to introduce
electronegative moieties, such as carbonyl groups, as indicating by the EALmin
descriptor.The Positive coefficient of the ClogP indicates that decreasing the
lipophilicity of the compounds might also lead to a hERG affinity decrease. In
addition, the number of hydrogen-bond donors is negatively correlated with the
potency, probably due to the desolvation penalty that cannot be compensated by
the hydrogen-bond interactions with the amino acids facing the central cavity of the
hERG channel.
e
