The WHAT IF neural network however, expects real numbers as input. It is of course easy to see that this greatly enhances the flexibility of this network. In practice, the neural network will find a two till three fold smaller deviation between the observed and calculated binding constants than classical QSAR methods for 90 till 95 percent of all data points. However, for the other 5 till 10 percent of the data points the correlation is five till ten times worse. Much experiments still have to be performed, but it looks to me that this is a good way of detecting outliers in the data set.
Most neural networks suffer badly from the multiple minimum problem.
IF neural network uses a optimization scheme that uses random neuron alterations. This ensures that given sufficient CPU time, also the global minimum can be found. In QSAR applications X's are the volume, charge, etc of groups in the molecule, and Y is the binding constant. WHAT IF will at regular intervals give some information about the present status of the fitting procedure. The training phase can be very CPU intensive, the testing phase however, is very fast.