• Corpus ID: 245668667

Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation

@article{Tavakoli2022RxnHA,
  title={Rxn Hypergraph: a Hypergraph Attention Model for Chemical Reaction Representation},
  author={Mohammadamin Tavakoli and Alexander Shmakov and Francesco Ceccarelli and Pierre Baldi},
  journal={ArXiv},
  year={2022},
  volume={abs/2201.01196}
}
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… 

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