UAlign: Pushing the Limit of Template-free Retrosynthesis Prediction
  with Unsupervised SMILES Alignment
Kaipeng Zeng Xin Zhao Yu Zhang Fan Nie Xiaokang Yang Yaohui Jin Yanyan Xu
Retrosynthesis planning poses a formidable challenge in the organic chemical
industry, particularly in pharmaceuticals. Single-step retrosynthesis
prediction, a crucial step in the planning process, has witnessed a surge in
interest in recent years due to advancements in AI for science. Various deep
learning-based methods have been proposed for this task in recent years,
incorporating diverse levels of additional chemical knowledge dependency. This
paper introduces UAlign, a template-free graph-to-sequence pipeline for
retrosynthesis prediction. By combining graph neural networks and Transformers,
our method can more effectively leverage the inherent graph structure of
molecules. Based on the fact that the majority of molecule structures remain
unchanged during a chemical reaction, we propose a simple yet effective SMILES
alignment technique to facilitate the reuse of unchanged structures for
reactant generation. Extensive experiments show that our method substantially
outperforms state-of-the-art template-free and semi-template-based approaches.
Importantly, Our template-free method achieves effectiveness comparable to, or
even surpasses, established powerful template-based methods. Scientific
contribution: We present a novel graph-to-sequence template-free retrosynthesis
prediction pipeline that overcomes the limitations of Transformer-based methods
in molecular representation learning and insufficient utilization of chemical
information. We propose an unsupervised learning mechanism for establishing
product-atom correspondence with reactant SMILES tokens, achieving even better
results than supervised SMILES alignment methods. Extensive experiments
demonstrate that UAlign significantly outperforms state-of-the-art
template-free methods and rivals or surpasses template-based approaches, with
up to 5\% (top-5) and 5.4\% (top-10) increased accuracy over the strongest
baseline.