Analysis of uncertainty of neural fingerprint-based models.

Machine learning has gained popularity for predicting molecular properties based on molecular structure. This study explores the uncertainty estimates of neural fingerprint-based models by comparing pure graph neural networks (GNN) to classical machine learning algorithms combined with neural fingerprints. We investigate the advantage of extracting the neural fingerprint from the GNN and integrating it into a method known for producing better-calibrated probability estimates.

MolPipeline: A Python Package for Processing Molecules with RDKit in Scikit-learn.

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.

Can Cysteine Protease Cross-Class Inhibitors Achieve Selectivity?

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.