We propose MolPipeline, a package that aims to automate the model-building process while allowing it to flexibly adjust to the needs of different projects. By combining RDKit and scikit-learn, their functionality is unified. With the MolPipeline package, we aim to make this combination accessible to practitioners for their daily cheminformatic tasks, model building, and handling the ever-increasing data size while avoiding unnecessary re-implementation. By leveraging the pipeline concept from scikit-learn and adding instance-based processing and consistent error handling, we hope to provide a robust implementation suited for various molecular machine learning tasks. We plan to maintain and add further functionality to the MolPipeline in the future, including clustering algorithms and modern deep learning estimators. MolPipeline is available at .