Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation
This work addresses the lack of universal, robust, and interpretable reaction representations in chemistry, offering a domain-specific solution for predicting chemical properties.
The authors tackled the problem of representing chemical reactions for property prediction by developing a hypergraph attention neural network, which matched or outperformed existing methods across three datasets while providing interpretable multi-level representations.
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.