BONAFIDE: A Python Framework for the Calculation of Local Features for Atoms and Bonds in Molecules

Data-driven chemistry relies on robust, efficient, and reproducible pipelines for generating digital molecular representations. While comprehensive software for global molecular descriptors is widely available, the calculation of local atom and bond features remains fragmented across disparate tools with inconsistent interfaces. Here, we introduce the BOnd aNd Atom FeaturIzer and Descriptor Extractor (BONAFIDE), a Python package designed to standardize the generation of local descriptors. BONAFIDE provides a unified application programming interface that integrates 11 external computational chemistry and cheminformatics packages, including RDKit, MORFEUS, and Multiwfn as well as quantum chemical engines like xtb and Psi4 for single-point energy computations. The software currently supports the calculation of 590 distinct atom and bond features derived from either 2D or 3D molecular representations and electronic structure data. Key functionalities include flexible input format processing as well as output customization, seamless atom and bond feature calculation across all implemented descriptors, transparent hyperparameter tracking, comprehensive logging, and the possibility to implement custom featurizers within the BONAFIDE ecosystem. We also include runtime statistics for featurization and a case study that demonstrates how the tool is used for the featurization of amines through a multistep featurization pipeline. Current limitations and future development directions are discussed. BONAFIDE is available free of charge as open-source software under the Apache 2.0 license and can be obtained from GitHub (https://github.com/MolecularAI/atom-bond-featurizer).