Computational prediction of regioselectivity in the metabolism of xenobiotics

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Strategies for optimising the metabolic properties of drugs, cosmetics and agrochemicals often rely on the knowledge of the regioselectivity of metabolising enzymes. Computational methods have an enormous potential for predicting metabolically labile atom positions (sites of metabolism; SoMs) but are at an early state of development and have far-reaching limitations with respect to applicability, accuracy, interpretability and availability. The aim of this project is the systematic research and development of new computational methods that overcome these limitations and allow the accurate prediction of SoMs for a large variety of compounds, enzymes and species. A new and comprehensive high-quality dataset of substrates of metabolising enzymes and their expertly assigned mechanistic SoMs will be explored for model development for the first time. A range of different machine learning classifiers will be trained on an elaborate set of physically meaningful atomic descriptors. This will lead to a substantial expansion of the applicability and scope of these models and methods, from cytochrome P450-mediated metabolism toward full coverage of phase 1 + 2 metabolism, and from drug-like molecules to a broad range of xenobiotics and their metabolites. Problematic false positive prediction rates observed for most of the existing SoM predictors will be countered by the integration and combination of different types of models and further strategies. New methods for estimating prediction errors and the applicability domain will be investigated. Importantly, models for the assignment of biotransformation types to SoMs and qualitative estimation of metabolite abundance will also be developed, hence providing valuable additional information on the chemical structure and relevance of likely metabolites. The models will be subject to rigorous evaluation including the prospective validation in hepatocyte assays by an independent laboratory.