  Chemical reaction prediction, involving forward synthesis and retrosynthesis
prediction, is a fundamental problem in organic synthesis. A popular
computational paradigm formulates synthesis prediction as a
sequence-to-sequence translation problem, where the typical SMILES is adopted
for molecule representations. However, the general-purpose SMILES neglects the
characteristics of chemical reactions, where the molecular graph topology is
largely unaltered from reactants to products, resulting in the suboptimal
performance of SMILES if straightforwardly applied. In this article, we propose
the root-aligned SMILES (R-SMILES), which specifies a tightly aligned
one-to-one mapping between the product and the reactant SMILES for more
efficient synthesis prediction. Due to the strict one-to-one mapping and
reduced edit distance, the computational model is largely relieved from
learning the complex syntax and dedicated to learning the chemical knowledge
for reactions. We compare the proposed R-SMILES with various state-of-the-art
baselines and show that it significantly outperforms them all, demonstrating
the superiority of the proposed method.
