RouteDiff: Molecular Design via Discrete Diffusion over Synthetic Routes

Molecular generative models can propose molecules with desirable properties, but the resulting molecules are not necessarily synthesizable. Synthesis-aware molecular generation addresses this gap by choosing both a molecule and an executable route that constructs it. Existing sequence-based route generators typically commit route decisions autoregressively or refine completed pathways in separate stages. We introduce RouteDiff, a masked discrete diffusion model over complete postfix routes. A bidirectional Transformer predicts unresolved building-block and reaction decisions from the currently visible route context, while diffusion progressively determines when those decisions are revealed and committed. To scale to a 223,243-entry building-block catalog, we combine frozen molecular representations with learned low-rank identity components, and compose supported property conditions at inference time without task-specific retraining. On Enamine, RouteDiff combines a 92.8 reconstruction rate and 0.987 similarity with the highest building-block diversity (0.783) among the compared methods. Under a 10,000-oracle-call budget, it achieves the highest synthesis-constrained AUC Top-10 on eight of ten PMO tasks, with gains of up to 13.2 points. Denoising-trajectory analysis further finds pre-commit changes in unresolved building-block or reaction predictions in 36/48 analyzed successful trajectories.