MGSEKformer: Multi-Granularity Graph-Sequence Enhanced Knowledge Transformer for Single-Step Retrosynthesis Prediction

Single-step retrosynthesis prediction serves as a fundamental building block for multi-step synthesis route planning in modern drug discovery and organic material synthesis. To improve both efficiency and accuracy, researchers have explored a variety of deep learning frameworks, most of which focus on molecular feature extraction at a single granularity—either the atomic or functional group level. However, these approaches have not sufficiently addressed the differences and interactions between these two distinct granularities in terms of their impact on chemical reactions, which limits further improvements in predictive performance. To this end, we propose the Multi-Granularity Graph-Sequence Enhanced Knowledge Transformer framework (MGSEKformer) for single-step retrosynthesis. Specifically, we employ two PAGformers to extract local structural priors and long-range dependencies at the atomic and functional group levels, respectively, and use cross-attention and gating units to model their interactions and aggregate complementary information adaptively. Furthermore, building upon the standard feedforward transformation, we design a knowledge-attention branch to replace the fixed-weight fusion based on simple concatenation in a conventional Kformer, enabling adaptive injection of variable-length chemical prior knowledge and overcoming the reliance on predefined reaction templates. Comparative experiments against 13 baseline models show that the MGSEKformer achieves optimal multi-candidate inference performance. Ablation studies further demonstrate that functional-group feature encoding and cross-granularity interactive fusion substantially improve the ranking accuracy of high-confidence candidates, effectively supporting practical retrosynthetic route design.