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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 cheminformatics by wrapping standard RDKit functionality, 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. MolPipeline provides the building blocks to enable seamless integration of common cheminformatics tasks within scikit-learn's pipeline framework, such as scaffold splits and molecular standardization, making pipeline building easily adaptable to diverse project requirements.

Cysteine proteases are important targets for the discovery of novel therapeutics for many human diseases. From parasitic diseases to cancer, cysteine proteases follow a common mechanism, the formation of an encounter complex with subsequent nucleophilic reactivity of the catalytic cysteine thiol group toward the carbonyl carbon of a peptide bond or an electrophilic group of an inhibitor. Modulation of target enzymes occurs preferably by covalent modification, which imposes challenges in balancing cross-reactivity and selectivity. Given the resurgence of irreversible covalent inhibitors, can they impair off-target effects or are reversible covalent inhibitors a better route to selectivity? This Perspective addresses how small molecule inhibitors may achieve selectivity for different cathepsins, cruzain, rhodesain, and falcipain-2. We discuss target- and ligand-based designs emphasizing repurposing inhibitors from one cysteine protease to others.