  Molecular chirality, a form of stereochemistry most often describing relative
spatial arrangements of bonded neighbors around tetrahedral carbon centers,
influences the set of 3D conformers accessible to the molecule without changing
its 2D graph connectivity. Chirality can strongly alter (bio)chemical
interactions, particularly protein-drug binding. Most 2D graph neural networks
(GNNs) designed for molecular property prediction at best use atomic labels to
na\"ively treat chirality, while E(3)-invariant 3D GNNs are invariant to
chirality altogether. To enable representation learning on molecules with
defined stereochemistry, we design an SE(3)-invariant model that processes
torsion angles of a 3D molecular conformer. We explicitly model conformational
flexibility by integrating a novel type of invariance to rotations about
internal molecular bonds into the architecture, mitigating the need for
multi-conformer data augmentation. We test our model on four benchmarks:
contrastive learning to distinguish conformers of different stereoisomers in a
learned latent space, classification of chiral centers as R/S, prediction of
how enantiomers rotate circularly polarized light, and ranking enantiomers by
their docking scores in an enantiosensitive protein pocket. We compare our
model, Chiral InterRoto-Invariant Neural Network (ChIRo), with 2D and 3D GNNs
to demonstrate that our model achieves state of the art performance when
learning chiral-sensitive functions from molecular structures.
