Machine learning was performed similarly to the previously described HASTEN software, using chemprop . Briefly, the chemprop model was trained using default settings to predict the Glide docking_score based on the SMILES-input. Additional work was done to speed up the predictions using parallel computing via slurm. The structure database was built using RegistrationHash-functionality in RDKit and Sqlite3. A graphical user interface was written using the tkinter package in Python.