A recent paper "The Catch-22 of Predicting hERG Blockade Using Publicly Accessible Bioactivity Data" DOI described a classification model for HERG activity. I was delighted to see that all the datasets used in the study, including the training and external datasets, and the models generated using these datasets were provided as individual data files (CSV) and Python Jupyter notebooks, respectively, on GitHub https://github.com/AGPreissner/Publications).
The models were downloaded and the Random Forest Jupyter Notebooks (using RDKit) modified to save the generated model using pickle to store the predictive model, and then another Jupyter notebook was created to access the model without the need to rebuild the model each time. This notebook was exported as a python script to allow command line access, and Vortex scripts created that allow the user to run the model within Vortex and import the results and view the most significant features.
All models and scripts are available for download.