Data strip mining for the virtual design of pharmaceuticals with neural networks

A novel neural network based technique, called “data strip mining” extracts predictive models from data sets which have a large number of potential inputs and comparatively few data points. This methodology uses neural network sensitivity analysis to determine which predictors are most significant in the problem. Neural network sensitivity analysis holds all but one input to a trained neural network constant while varying each input over its entire range to determine its effect on the output. Elimination of variables through neural network sensitivity analysis and predicting performance through model cross-validation allows the analyst to reduce the number of inputs and improve the model´s predictive ability at the same time. This paper demonstrates its effectiveness on a pair of problems from combinatorial chemistry with over 400 potential inputs each. For these data sets, model selection by neural sensitivity analysis outperformed other variable selection methods including the forward selection and genetic algorithm