ReactPG: Rethinking Reaction-to-Procedure Generation via Process Graph Diffusion

Experimental procedure generation is a key task in automated chemical synthesis, requiring plausible operation sequences while maintaining global consistency across operation ordering, material-state dependencies, and parameter assignments. Conventional sequence-generation paradigms linearize these interdependent decisions into text, requiring the underlying relational structure to be recovered implicitly from token-level context. To address this limitation, we propose ReactPG, the first conditional discrete graph diffusion framework for chemical procedure generation. ReactPG reformulates procedure generation as conditional generation over typed process graphs and employs a categorical Markov diffusion process over a constrained state space, parameterized by a joint process graph Transformer. We further construct OpenExp-PG, an experimental process graph dataset derived from OpenExp, and evaluate ReactPG using both graph-level and text-level metrics. Experimental results show that ReactPG achieves strong performance across multiple metrics while attaining near-zero violation rates on the evaluated procedural consistency constraints, outperforming existing large language model-based approaches.