In recent years, several reaction templates-based and template-free
approaches have been reported for single-step retrosynthesis prediction. Even
though many of these approaches perform well from traditional data-driven
metrics standpoint, there is a disconnect between model architectures used and
underlying chemistry principles governing retrosynthesis. Here, we propose a
novel chemistry-aware retrosynthesis prediction framework that combines
powerful data-driven models with chemistry knowledge. We report a
tree-to-sequence transformer architecture based on hierarchical SMILES grammar
trees as input containing underlying chemistry information that is otherwise
ignored by models based on purely SMILES-based representations. The proposed
framework, grammar-based molecular attention tree transformer (G-MATT),
achieves significant performance improvements compared to baseline
retrosynthesis models. G-MATT achieves a top-1 accuracy of 51% (top-10 accuracy
of 79.1%), invalid rate of 1.5%, and bioactive similarity rate of 74.8%.
Further analyses based on attention maps demonstrate G-MATT's ability to
preserve chemistry knowledge without having to use extremely complex model
architectures.

提出了一种基于树 - 序列转换器模型和 SMILES 语法树的复合模型 - G-MATT，该模型创新性地结合了数据驱动模型和化学知识，能够显著改善传统预测指标，并在保留了化学知识的同时避免了过于复杂的模型架构。