  Graph Neural Networks (GNNs) play a fundamental role in many deep learning
problems, in particular in cheminformatics. However, typical GNNs cannot
capture the concept of chirality, which means they do not distinguish between
the 3D graph of a chemical compound and its mirror image (enantiomer). The
ability to distinguish between enantiomers is important especially in drug
discovery because enantiomers can have very distinct biochemical properties. In
this paper, we propose a theoretically justified message-passing scheme, which
makes GNNs sensitive to the order of node neighbors. We apply that general
concept in the context of molecular chirality to construct Chiral Edge Neural
Network (ChiENN) layer which can be appended to any GNN model to enable
chirality-awareness. Our experiments show that adding ChiENN layers to a GNN
outperforms current state-of-the-art methods in chiral-sensitive molecular
property prediction tasks.
