It is fundamental for science and technology to be able to predict chemical
reactions and their properties. To achieve such skills, it is important to
develop good representations of chemical reactions, or good deep learning
architectures that can learn such representations automatically from the data.
There is currently no universal and widely adopted method for robustly
representing chemical reactions. Most existing methods suffer from one or more
drawbacks, such as: (1) lacking universality; (2) lacking robustness; (3)
lacking interpretability; or (4) requiring excessive manual pre-processing.
Here we exploit graph-based representations of molecular structures to develop
and test a hypergraph attention neural network approach to solve at once the
reaction representation and property-prediction problems, alleviating the
aforementioned drawbacks. We evaluate this hypergraph representation in three
experiments using three independent data sets of chemical reactions. In all
experiments, the hypergraph-based approach matches or outperforms other
representations and their corresponding models of chemical reactions while
yielding interpretable multi-level representations.

采用基于图的分子结构表示的超图注意力神经网络方法解决化学反应表示与性质预测问题，既能增强在通用性、鲁棒性、解释性等方面的考虑，又能达到比其他方法更好的性能与可解释性。