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.

翻译：图神经网络（GNNs）在众多深度学习问题中发挥着基础作用，尤其在化学信息学领域。然而，典型的GNN无法捕捉手性概念，这意味着它们无法区分化合物的三维结构与其镜像（对映体）。区分对映体的能力在药物发现中尤为重要，因为对映体可能具有截然不同的生化性质。本文提出了一种理论上合理的信息传递机制，使GNN能够感知节点邻居的顺序。我们将这一通用概念应用于分子手性领域，构建了手性边神经网络（ChiENN）层，该层可附加至任何GNN模型以实现手性感知。实验表明，在GNN中添加ChiENN层后，在手性敏感的分子性质预测任务中，其性能优于当前最先进的方法。