For theory about neural networks you are referred to your local library. In principle the network should be able to replace the classical QSAR modules, but in practice I think it will only be useful to use QSAR and the network in parallel. Neural networks are normally used to do pattern recognition. They are often useful to detect hidden correlations between data. In the special case of QSAR problems a neural network should in principle be capable to find correlations between the parameters for the variable active groups.
Most neural networks accept bit patterns as input.
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.