Several open-source applications are available that support researchers in QSPR modelling and help in solving some of the aforementioned challenges. A popular framework is KNIME , which utilizes a GUI with visual workflows and has many pre-implemented components. However, designing custom components in KNIME can be challenging and the integration of Python extensions in the Java-based API is not always straightforward . With a focus on deep-learning-based models, DeepChem , was one of the pioneering Python packages for molecular modelling. It offers a wide array of different featurizers and models and a flexible and easy to understand API that is modular and extensible. However, not all DeepChem models address the aforementioned reproducibility and deployment issues out-of-the-box. For example, the offered SklearnModel class does serialize the model itself, but reproducing the preparation workflow and creating the feature matrix is left to the users themselves, which can be inconvenient or, worse, might create reproducibility and deployment problems.