![Author ORCID: We display the ORCID iD icon alongside authors names on our website to acknowledge that the ORCiD has been authenticated when entered by the user. To view the users ORCiD record click the icon. [opens in a new tab]](https://chemrxiv.org/engage/assets/public/chemrxiv/images/logos/orcid.png)
Abstract
The ability to pinpoint and predict sites of metabolism (SoMs) is essential for designing and optimizing effective and safe bioactive small molecules, such as drugs. However, the number of molecules with annotated SoMs in the public domain and pharmaceutical industry is limited, hindering the advancement of data-driven methods like 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 to support further research.
Supplementary materials
Title
Supporting information
Description
Murcko scaffolds in the AZ Compound Set that are also represented in the MetaQSAR data set or the Approved Drugs set (Table S1); UMAP plot of the atoms represented in the AZ SoM_extended and AZ SoM_exact data sets, distribution of atom environment similarities between the atoms included in the AZ SoM_exact set and their nearest neighbors in the MetaQSAR data set, and distribution of atom environment similarities between the atoms included in the MetaQSAR test set and AZ SoM_exact set and their nearest neighbors in the MetaQSAR training set (Figure S1); distribution of atom environment similarities between the atoms included in the AZ SoM_exact test sets and their nearest neighbors in the MetaQSAR training set and AZ SoM_exact training sets, respectively, for both random-split and time-split AZ SoM_exact training set and test set (Figure S2).
Actions
