This looks very useful for anyone having to process multiple molecules, I particularly like the error processing!

The open-source package scikit-learn provides various machine learning algorithms and data processing tools, including the Pipeline class, which allows users to prepend custom data transformation steps to the machine learning model. We introduce the MolPipeline package, which extends this concept to chemoinformatics by wrapping default functionalities of RDKit, such as reading and writing SMILES strings or calculating molecular descriptors from a molecule object. We aimed to build an easy-to-use Python package to create completely automated end-to-end pipelines that scale to large data sets. Particular emphasis was put on handling erroneous instances, where resolution would require manual intervention in default pipelines. In addition, we included common cheminformatics tasks, like scaffold splits and molecular standardization, natively in the pipeline framework and adaptable for the needs of various projects.

The source code is on GitHub

It can be installed using

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