The conformational features of ten known amnesia reversal compounds were analyzed by the molecular mechanics calculations with the aim of identifying a common spatial disposition of the polar functional groups present in all the molecules (a N-C=O amidic group and a X-C=O group, with X = 0, N). Principal component analysis (PCA) led to the identification of three interatomic distances able to provide all the information necessary to describe the relative spatial disposition of the two functional groups. Cluster analysis was then performed to group the minimum energy conformations according to the values of those distances. Clusters were analyzed to single out those containing conformations of the maximum number of active compounds. This procedure permitted the finding of several acceptable pharmacophore models. The influence on results by the dissociated/undissociated forms of carboxylic compounds presented in the data set is also discussed. Two potent prolyl endopeptidase (PEP) inhibitors showing strong anti-amnesic effect were also included in the study. The results suggest that the common biological activity between these classes of compounds could be intemreted on the basis of the common spatial disposition of the investigated funckm 30.400, Pornezia (Roma). tional groups.
A structure-activity relationship study has been done on 8 compounds with the activity known as 'Ca2+ channel blockers'. Conformational analysis was carried out using a molecular mechanics method. The 3D-QSAR approach was used and the most polar functional groups present in all the molecules were considered. Eight interatomic distances are necessary to define the relative spatial disposition of these relevant molecular fragments. The structure-activity relationship between interatomic distances and biological activity was performed using statistic and chemometric methods. In particular, with Principal Component Analysis, it was possible to reduce the number of interatomic distances: only six of the eight distances are sufficient to describe the system in a useful way. A classification method was iteratively used to select the most probable conformations linked to the biological activity and to build a model able to classify conformations according to their biological behaviour. Cluster analysis on the active selected conformations subsequently allowed the identification of two different geometrical patterns for the active compounds. Finally the validity of the model was verified by correctly predicting the activity of other molecules not used in the construction of the model but possessing known activity.
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