MolPipeline extends the pipeline concept from scikit-learn 8, to processing and transforming molecular input data using RDKit. These pipelines allow to perform a sequence of user defined transformations on the input data and finalize the procedure with an optional machine learning model for predictions. As the individual transformations remain accessible, corresponding parameters can be optimized with typical tools for hyperparameter optimization, like scikit-learn's GridSearchCV. In MolPipeline, we provide new pipeline elements implementing standard cheminformatics tasks using RDKit while complying with scikit-learn's pipeline API. These pipeline elements are building blocks with well-defined input and output. Therefore, they can be flexibly arranged to create new pipelines for specific needs, like custom molecular standardization. The pipeline elements in MolPipeline are grouped into the modules any2mol, mol2mol, mol2any and custom estimators.