SyGMa: Combining Expert Knowledge and Empirical Scoring in the Prediction of Metabolites

Predictions of potential metabolites based on chemical structure are becoming increasingly important in drug discovery to guide medicinal chemistry efforts that address metabolic issues and to support experimental metabolite screening and identification. Herein we present a novel rule‐based method, SyGMa (Systematic Generation of potential Metabolites), to predict the potential metabolites of a given parent structure. A set of reaction rules covering a broad range of phase 1 and phase 2 metabolism has been derived from metabolic reactions reported in the Metabolite Database to occur in humans. An empirical probability score is assigned to each rule representing the fraction of correctly predicted metabolites in the training database. This score is used to refine the rules and to rank predicted metabolites. The current rule set of SyGMa covers approximately 70 % of biotransformation reactions observed in humans. Evaluation of the rule‐based predictions demonstrated a significant enrichment of true metabolites in the top of the ranking list: while in total, 68 % of all observed metabolites in an independent test set were reproduced by SyGMa, a large part, 30 % of the observed metabolites, were identified among the top three predictions. From a subset of cytochrome P450 specific metabolites, 84 % were reproduced overall, with 66 % in the top three predicted phase 1 metabolites. A similarity analysis of the reactions present in the database was performed to obtain an overview of the metabolic reactions predicted by SyGMa and to support ongoing efforts to extend the rules. Specific examples demonstrate the use of SyGMa in experimental metabolite identification and the application of SyGMa to suggest chemical modifications that improve the metabolic stability of compounds. A dataset of 6187 metabolic reactions reported to occur in man has been used to develop a rule‐based method that systematically predicts and ranks potential metabolites of a given parent compound. The graphic shows a projection of the training set with various types of correctly predicted metabolic reactions represented by different colors.