G-MATT: Single-step Retrosynthesis Prediction using Molecular Grammar Tree Transformer
This work addresses the challenge of incorporating chemistry principles into retrosynthesis prediction for drug discovery, representing an incremental improvement over existing methods.
The paper tackled the problem of single-step retrosynthesis prediction by proposing a chemistry-aware framework that integrates domain knowledge with data-driven models, achieving a top-1 accuracy of 51% and a top-10 accuracy of 79.1% on the USPTO-50K dataset.
Various template-based and template-free approaches have been proposed for single-step retrosynthesis prediction in recent years. While these approaches demonstrate strong performance from a data-driven metrics standpoint, many model architectures do not incorporate underlying chemistry principles. Here, we propose a novel chemistry-aware retrosynthesis prediction framework that combines powerful data-driven models with prior domain knowledge. We present a tree-to-sequence transformer architecture that utilizes hierarchical SMILES grammar-based trees, incorporating crucial chemistry information that is often overlooked by SMILES text-based representations, such as local structures and functional groups. The proposed framework, grammar-based molecular attention tree transformer (G-MATT), achieves significant performance improvements compared to baseline retrosynthesis models. G-MATT achieves a promising top-1 accuracy of 51% (top-10 accuracy of 79.1%), invalid rate of 1.5%, and bioactive similarity rate of 74.8% on the USPTO- 50K dataset. Additional analyses of G-MATT attention maps demonstrate the ability to retain chemistry knowledge without relying on excessively complex model architectures.