Despite being identified as the most potent receptor related to vasoconstriction, human
urotensin-II receptor (hUT) has not been fully explored as a target for the treatment of cardiovascular
diseases. In view of this and with an aim to identify precise structural requirements for binding of hUT
antagonists, we endeavoured to develop, for the first time, multivariate QSAR models using
chemometric methods like partial least squares (PLS) and feed-forward neural network (FFNN). A set
of 48 pyrrolidine derivatives having hUT binding affinity was used for multivariate model
development. The accuracy and predictability of the developed models was evaluated using crossvalidation.
The PLS model showed good correlation between selected descriptors and Ki values (r2 =0.745 and r2 (CV)
=0.773). However, the predictive performance of FFNN was better than the PLS technique with r2 =0.810. The study
clearly suggests the role of lipophilic and steric descriptors in the ligand-hUT interactions. The QSAR models generated
can be successfully extended to predict the binding affinities and for the effective design of novel hUT antagonists.
Keywords: QSAR, urotensin-II, PLS, FFNN, descriptors, TSAR.
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