Representation-Guided Discrete Molecular Graph Retrosynthesis
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 th...
| Authors | Jiahai Huang, Anjie Qiao, Zhen Wang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 23 May 2026 |
| Last updated at source | 23 May 2026 |
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