Several packages addressing these problems have been introduced over the years. 9-14 For example, PREFER 11 automates the model and feature selection process for classical machine learning and deep learning. Datamol 15 and MolFeat 12 provide a light wrapper for RDKit and a wide range of classical and modern molecular representations. Scikit-chem 14 and Scikit-Mol 13 connect RDKit-functionality with scikit-learn. However, most frameworks for integrating molecular feature representations into machine learning models do not include or allow custom handling of regular cheminformatics tasks, like molecular standardization, data splits or combining individual features. Those that allow for both still need to address invalid inputs or molecules failing in the preprocessing steps in an automated way. Therefore, it is challenging to achieve end-to-end automation while allowing processing steps to change flexibly depending on the project's requirements.