Synthesizability-Guided Discrete Diffusion Model for Molecular Generation

Molecular generative models can propose candidates with favorable predicted properties, but are not guaranteed to generate synthesizable molecules. Candidates that are hard to synthesize cannot readily enter wet-lab experiments. Computer-aided synthesis planning (CASP) tools provide route-specific evidence towards synthesizability, but are too slow to integrate into generation. In this paper, we introduce *SynGuide*, a guidance mechanism that distills CASP outcomes into a noise-conditioned guidance signal that guides the generation of discrete diffusion models. We define a fixed-budget retrosynthetic-accessibility (RA) label indicating whether a CASP tool finds a route to purchasable building blocks within a fixed search budget. In order to build guided generative models, we then train a noise-conditioned predictor of the RA-label and use its gradient to guide a frozen discrete diffusion generator. Under RA-label guidance, our method increases the measured planner solve rate by 24%. Across three different protein targets, *SynGuide* achieves *state-of-the-art* performance improving synthesizability. This work provides a practical way to incorporate planner-derived synthesizability objectives into molecular generation without placing expensive retrosynthesis searches in the sampling loop.