With exception of metabolic simulations performed using TIMES (version 2.31.2.82), all work was performed using Python (version 3.10.4) run with IPython (version 8.4.0) in JupyterLab (version 3.3.2). The Toolbox API (OECD Toolbox version 4.5 with Service Pack 1 update, API version 6), and BioTransformer (Wishart Lab, version 3.0, executable Java Archive, June 15, 2022 release) were used for automated metabolic simulations. Efficient batch execution of metabolism simulations was handled via parallel processing multiple individual calls to either BioTransformer or the Toolbox API via the “multiprocess” package. The command line interface (CLI) calls needed to interact with BioTransformer were executed via the “subprocess” package, and the Toolbox API was queried via its Swagger user interface hosted on a locally running Windows Desktop instance of the Toolbox Server. The data generated from the MetSim hierarchical schema were translated into JavaScript Object Notation (JSON) format using Python. The resulting data were inserted into a Mongo Database (MongoDB) using the “pymongo” package for efficient storage and retrieval. The code repository including all Jupyter Notebooks documenting the analysis performed and the MetSim framework are available at https://github.com/patlewig/metsim. Data files needed to reproduce the analysis are provided at https://doi.org/10.23645/epacomptox.25463926 and as Supporting Information.

This dataset is associated with the following publication: Groff, L., A. Williams, I. Shah, and G. Patlewicz. MetSim: Integrated Programmatic Access and Pathway Management for Xenobiotic Metabolism Simulators. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 37(5): 685-697, (2024).