Use of robust classification techniques for the prediction of human cytochrome P450 2D6 inhibition.

A new in silico model is developed to predict cytochrome P450 2D6 inhibition from 2D chemical structure. Using a diverse training set of 100 compounds with published inhibition constants, an ensemble approach to recursive partitioning is applied to create a large number of classification trees, each of which yields a yes/no prediction about inhibition for a given compound. These binary classifications are combined to provide an overall prediction, which answers the yes/no question about inhibition and provides a measure of confidence about that prediction. Compared to single-tree models, the ensemble approach is less sensitive to noise in the experimental data as well as to changes in the training set. Internal validation tests indicated an overall classification accuracy of 75%, whereas predictions applied to an external set of 51 compounds yielded 80% accuracy, with all inhibitors correctly identified. The speed and 2D nature of this model make it appropriate for high-throughput processing of large chemical libraries, and the confidence level provides a continuous scale on which to prioritize compounds.