In this study, four new silicon-containing poly(ether-azomethine)s with linear structures were prepared using original silicon and biphenyl moiety-containing monomers: two diamines and two dialdehydes.

Artificial neural network ensembles were used for modeling the cyclin-dependent kinase inhibition of 1H-pyrazolo[3,4-d]pyrimidine derivatives. The structural characteristics of these inhibitors were encoded in relevant 3D-spatial descriptors extracted by genetic algorithm feature selection. Bayesian-regularized multilayer neural networks, trained by the back-propagation algorithm, were developed using these variables as inputs. The predictive power of the model was tested by leave-one-out cross validation. In addition, for a more rigorous measure of the predictive capacity, multiple validation sets were randomly generated as members of neural network ensembles, which makes doing averaged predictions feasible. In this way, the predictive power was analyzed accounting for the averaged test set R values and test set mean-square errors. Otherwise, Kohonen self-organizing maps were used as an additional tool for the same modeling. The location of the inhibitors in a map facilitates the analysis of the connection between compounds and serves as a useful tool for qualitative predictions.

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The inhibitory activity towards farnesyl protein transferase enzyme (FPT) of 49 piperidine substituted trihalobenzocycloheptapyridine analogues (thBCHPs) has been successfully modelled using 2D spatial autocorrelation vectors. Predictive linear and non-linear models were obtained by forward stepwise multilinear regression analysis (MRA) and artificial neural network (ANN) approaches, respectively. A variable selection routine that selected relevant non-linear information from the data set was employed prior to networks training. The MRA model, using three descriptors, was able to explain about 68% data variance. The model showed a linear dependence between the inhibitory activities and autocorrelation coefficients weighted by van der Waals volumes and atomic polarizabilities on the inhibitors molecules. The non-linear approach preserve several characteristics described for the linear one. Three descriptors were selected encoding the same atomic properties, but the new ones were able to explain about 92% data variance. In addition, the ANN model had higher predictive power. Furthermore, inhibitors were well distributed regarding its activity levels in a Kohonen self-organizing map (SOM) built using the input variables of the best neural network.

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