Toward a principled methodology for neural network design and performance evaluation in QSAR. Application to the prediction of LogP

The prediction of properties of molecules from their structure (QSAR) is basically a nonlinear regression problem. Neural networks are proven to be parsimonious universal approximators of nonlinear functions; therefore, they are excellent candidates for performing the nonlinear regression tasks involved in QSAR. However, their full potential can be exploited only in the framework of a rigorous approach. In the present paper, we describe a principled methodology for designing neural networks for QSAR and estimating their performances, and we apply this approach to the prediction of logP. We compare our results to those obtained on the same molecules by other methods.