Metabolite Identification Data in Drug Discovery, Part 2: Site-of-Metabolism Annotation, Analysis, and Exploration for Machine Learning.

Abstract

The ability to pinpoint and predict sites of metabolism (SoMs) is essential for designing and optimizing effective and safe bioactive small molecules. However, the number of molecules with annotated SoMs is limited, hindering the advancement of data-driven methods such as machine learning for metabolism prediction. Here, we provide a comprehensive characterization of SoM data obtained from the readouts of a human hepatocyte assay conducted at AstraZeneca Gothenburg. We explore a new strategy for SoM annotation that accounts for uncertainty in the experimental data, and we relate our findings to the most comprehensive SoM data collection available to date. Our study includes entropy analysis of SoM annotations, accompanied by representative examples that highlight the complexities of interpreting and working with metabolism data. Furthermore, we demonstrate the impact and value of the new metabolism data on SoM prediction. Importantly, a substantial portion of the data generated and analyzed as part of this work is made publicly available.