When Bonds Resist Rotation: Automated Classification and Representation of Atropisomers in the MolBar Identifier

ChemRxiv

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 rotatable bond in a three-dimensional molecular structure as either rapidly convertible or configurationally stable, 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, largescale 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 preliminary version of MolBar 1.2.0 is available on the Python Package Index (PyPI) and can be installed using the pip command: pip install-pre molbar.

Authors 2

1. Nils van Staalduinen Aachen Institute of Physical Chemistry

   RWTH Aachen University

   Affiliation as printed

   Institute of Physical Chemistry, RWTH Aachen University, Melatener Str. 20, Aachen, Germany, 52074

2. Christoph Bannwarth Aachen Institute of Physical Chemistry

   RWTH Aachen University

   Affiliation as printed

   Institute of Physical Chemistry, RWTH Aachen University, Melatener Str. 20, Aachen, Germany, 52074