Review and comparision of methods to study the contribution of variables in artificial neural network models for QSAR study

Abstract:

Although Artificial Neural Network(ANN) shows superior predictive power in the study of quantitative structure-activity relationship(QSAR),it has been labeled as a "black box" because it provides little explanatory insight into the relative importance of the independent variables.In this paper,as an example of toxicity of 35 nitro-aromatics on fathead minnow,six methods which could give the relative contribution and/or the contribution profile of input factors were reviewed and compared.The Partial Derivative method was found to be the most useful as it gave the most complete results,followed by the Profile method that gave the contribution profile of input variables.The Perturb method allowed a good classification of input parameters as well as the Weights method that had been simplified,but these two methods lacked stability.Finally,the classical stepwise methods gave the poorest results.