A k-Nearest Neighbours Approach Using Metabolism-related Fingerprints to Improve In Silico Metabolite Ranking

The application of biotransformation dictionaries derived by expert evaluation of known metabolic pathways represents one approach to the prediction of both phase I and phase II xenobiotic metabolites. The ranking of metabolites generated by such dictionaries has previously been achieved through the use of qualitative reasoning rules and quantitative probability values. Using the biotransformation dictionary available in the Meteor expert system, we show that metabolite over-prediction by both of these methods can be reduced by the adoption of a k-nearest neighbours methodology in which the likelihood of a predicted biotransformation is determined based on comparison of a query chemical with structurally-similar substrates with known experimental metabolic data which activate the same biotransformation. Optimal performance was achieved when similarity was defined in terms of a combination of two fingerprints, one describing the overall profile of biotransformations a structure can potentially undergo and the other describing the local environment around the predicted site of metabolism for the particular biotransformation under consideration.