G-MATT: Single-step Retrosynthesis Prediction using Molecular Grammar Tree Transformer
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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 similarity rate of 74.8 G-MATT attention maps demonstrate the ability to retain chemistry knowledge without relying on excessively complex model architectures.
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