  Motivation: Retrosynthesis planning poses a formidable challenge in the
organic chemical industry. 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.
  Results: 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.
