Systematic Approaches for the Encoding of Chemical Groups: A Case study.

Herein are the data files that support a simple python package to facilitate profiling of chemicals through the ECHA ARN groupings using the Random Forest model that was developed in Karamertzanis, P, Patlewicz G, Sannicola M, Paul-Friedman K, Shah (2024) Systematic Approaches for the Encoding of Chemical Groups: A Case study. Chem. Res. Toxicol. 37, 600-619. https://doi.org/10.1021/acs.chemrestox.3c00411 The original repository supporting this publication can be found here https://github.com/pkaramertzanis/regulatory_grouping/tree/master. In https://github.com/patlewig/arn_cats/ we provide a simpler means of applying the best random forest model developed in the original study to make grouping assignments for new chemicals. Although the arn_cats package and associated repository provide notebooks to recreate the random forest models and necessary input files, these are provided for convenience. The dataset of REACH chemicals that was supplied in the SI of the original publication is provided here as it is useful set to verify the RF model predictions.