Structure-Constrained Molecular Design with Discrete Graph Bridge Diffusion

Structure-constrained molecular design, such as decorating a scaffold, extending a motif, or linking fragments, requires generating around a fixed substructure while leaving it untouched. Diffusion models are a natural fit for this: unlike autoregressive language models, they can enforce such constraints directly by construction, simply by clamping the prescribed subgraph during sampling. However, much of the diffusion literature has focused instead on de novo generation or property optimization via guidance, with structural constraints more often imposed at inference time than trained for directly. We revisit this setting along two complementary directions. The first focuses on the role of preprocessing, examining how much of the apparent gap between graph diffusion and chemical language models can be attributed to preprocessing rather than architecture. This alone yields a substantially stronger constrained-generation baseline than prior graph diffusion work. The second, Graph Bridge Diffusion (), goes further by training the model to see the constraint throughout the generative process rather than only at inference, closing a train–inference mismatch that every prior clamping-based approach leaves open. The result is a single model that serves a variety of structure-based generation tasks without task-specific retraining, and that better matches the reference chemical series in composition, not just in satisfying the constraint. Trained on 1.6M molecules with 16M parameters, GBD performs competitively with chemical language models with $5\times$ more parameters and over 1.1B molecular strings for pretraining, while outperforming graph diffusion baselines at matched budgets.