Figure 8 Regression plots for the estimated and experimental
pKa values for 71 data points
The developed ANN model did an excellent job in the prediction of
dissociation constants, however, the method id limited by the large
number of properties to be used as input parameters limitation to be
found in the literature or measured experimentally. The ANN model can be
more useful and flexible if it was possible to use fewer input data. In
this study, the ANN model was also tested by reducing the number of
input data. The molecular weight, critical temperature and pressure were
maintained as inputs in the new model as they identified the studied
compound, in addition to the temperature as the dissociation constants
values were temperature-dependent. As for the four physical parameters
left, the ANN model was tested by removing them one by one, then two and
three at the time. The performance of the ANN models based on
Roverall, MSEtrain,
MSEval and MSEtest were summarized in
Table 13. It was found that the performance of the model deteriorated
slightly by removing some input information, compared to the original
model. However, the ANN model while leaving only the surface tension and
refractive index data had a very good performance and is now called the
new ANN model. Although the new ANN model had a slightly lower
performance than the full model, the estimated
pKa values were still in good agreement with the
experimental values but the model allowed for more flexibility in its
application.
As observed in Figure 9, the best validation performance was reached at
epoch 76 and became unchanged with the increase in the number of epochs.
Figure 9 shows the regression between the estimated and experimental
values of the new ANN model. Regression plots for the estimated and
experimental pKa values for 71 data points for
the new ANN model are presented in Figure 10.
Figure 9 The performance improvement of the new ANN model