  Molecule representation learning (MRL) methods aim to embed molecules into a
real vector space. However, existing SMILES-based (Simplified Molecular-Input
Line-Entry System) or GNN-based (Graph Neural Networks) MRL methods either take
SMILES strings as input that have difficulty in encoding molecule structure
information, or over-emphasize the importance of GNN architectures but neglect
their generalization ability. Here we propose using chemical reactions to
assist learning molecule representation. The key idea of our approach is to
preserve the equivalence of molecules with respect to chemical reactions in the
embedding space, i.e., forcing the sum of reactant embeddings and the sum of
product embeddings to be equal for each chemical equation. This constraint is
proven effective to 1) keep the embedding space well-organized and 2) improve
the generalization ability of molecule embeddings. Moreover, our model can use
any GNN as the molecule encoder and is thus agnostic to GNN architectures.
Experimental results demonstrate that our method achieves state-of-the-art
performance in a variety of downstream tasks, e.g., 17.4% absolute Hit@1 gain
in chemical reaction prediction, 2.3% absolute AUC gain in molecule property
prediction, and 18.5% relative RMSE gain in graph-edit-distance prediction,
respectively, over the best baseline method. The code is available at
https://github.com/hwwang55/MolR.
