Representation-Guided Discrete Molecular Graph Retrosynthesis

2026-05-23

AI Summary

Existing template-free, single-step retrosynthesis models suffer from slow convergence and limited generation quality and diversity due to the difficulty of explicitly modeling chemical semantics. To address this, this work proposes a Graph Representation Guidance (GRG) framework that integrates molecular representations from a pretrained encoder into a denoising diffusion Transformer. During generation, multi-granularity alignment strategies provide deep guidance, while a representation similarity–based reranking mechanism enhances both diversity and accuracy without requiring an additional verifier. Evaluated on USPTO-50k, the model achieves top-1/3/5/10 accuracies of 58.6/77.2/83.4/87.1, respectively, with diversity improved to 15.5, training epochs reduced by 35%, and inference time shortened by 30%.

Abstract

Stochastic process-based molecular graph generators have become the state of the art for template-free single-step retrosynthesis. However, these models are typically trained only on product-reactant pairs, thereby acquiring chemistry-relevant representations in an indirect and implicit manner. Meanwhile, recent advances in computer vision demonstrate that offering representation guidance to a generator can effectively distill semantics from pretrained encoders into DiTs, substantially improving both convergence and generation quality. Whether similar gains extend to the retrosynthesis task, and what graph-specific design choices can make them work, remains an open question. To address these questions, we conduct a systematic empirical study over a unified design space spanning teacher molecular representations, endpoint and granularity choices, injection depths in the denoiser, correspondence strategies and guidance scheme. Guided by these considerations, we develop Graph-oriented Representation Guidance (GRG), which achieves 58.6 / 77.2 / 83.4 / 87.1 top-1 / 3 / 5 / 10 accuracy on USPTO-50k, while increasing diversity to 15.5, both substantially outperforming the adopted base generator. Notably, GRG consistently improves all top-k metrics in out-of-distribution settings, suggesting that representation guidance facilitates the acquisition of intrinsic chemical semantics. Meanwhile, the introduced representation guidance reduces the number of epochs by 35% and the wall-clock time by 30% to reach comparable performance. In addition, we introduce a simple yet effective representation-similarity-based reranking mechanism, which further improves the top of the ranked list without training an additional verifier.

Problem

• retrosynthesis
• molecular graph generation
• representation guidance
• template-free synthesis
• chemical semantics

Innovation

• representation guidance
• molecular graph generation
• retrosynthesis
• diffusion models
• graph representation learning