Automated Classification and Representation of Sp2–sp2 Atropisomers in the MolBar Identifier

Abstract Atropisomerism, a form of chirality arising from restricted rotation around single bonds, holds significant pharmacological importance. Yet, the automated identification and representation of atropisomerism in molecular databases and cheminformatics tools remain limited. Identifying bonds that lead to stable atropisomers typically involves labor-intensive experiments or costly quantum chemical scans. In this work, we present an extension of the molecule identifier MolBar that automatically classifies each sp2–sp2 bond in a three-dimensional molecular structure as either rapidly convertible or configurationally stable (rotational barrier ≥ 20 kcal/mol), specifying the absolute configuration for the latter. Our methodology uses two distinct classifiers: a physically motivated classifier for steric hindrance and a message-passing graph neural network for electronic effects. Integrated into MolBar, this module enables unsupervised, large-scale annotation of atropisomeric bonds along with their absolute configurations. This advancement facilitates the representation of atropisomerism in databases and supports atropisomerism-aware machine learning in drug discovery. A new version of MolBar (1.2.0) is available on the Python Package Index (PyPI) and can be installed using the pip command: pip install molbar.