Analysis of linear and nonlinear QSAR data using neural networks

The use of feed forward back propagation neural networks to perform the equivalent of multiple linear regression has been examined using artificial structured data sets and real literature data. Their predictive ability has been assessed using leave-one-out cross-validation and training/test set protocols. While networks have been shown to fit data sets well, they appear to suffer from a number of disadvantages. In particular, they have performed poorly in prediction for the QSAR data examined here, they are susceptible to chance effects, and the relationships developed by the networks are difficult to interpret. This investigation reports results for one particular form of artificial neural network; other architectures and applications, however, may be more suitable.