Molecular stereochemistry strongly alters (bio)chemical interactions yet is often neglected in molecular deep learning. Tetrahedral (point) chirality, a form of stereochemistry describing relative spatial arrangements of bonded neighbors around tetrahedral carbon centers, especially influences substrate-catalyst binding and is therefore critical for pharmaceutical drug design and asymmetric catalysis. However, because chirality influences the set of 3D conformers accessible to the molecule without changing its 2D graph connectivity, modern graph neural networks (GNNs) designed for molecular property prediction often ignore chirality in their learned molecular representations. Most 2D GNNs at best use atomic labels to naïvely treat chirality, while E(3)-invariant 3D GNNs are invariant to chirality altogether.<br/><br/>To enable representation learning on molecules with defined chirality, we design an SE(3)-invariant neural network 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 directly into the neural architecture, mitigating the need for multi-conformer data augmentation. We showcase 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 ChIRo achieves state of the art performance when learning chiral-sensitive functions from molecular structures. We envision that our model, alongside future work to explicitly learn E/Z isomerism and atropisomerism, will expand the capacity of deep learning to advance enantioselective reaction optimization, 3D drug design, and stereochemical property prediction.

Learning 3D representations of molecular chirality with invariance to bond rotations

ACS Spring 2022

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