CatalyState: Enzyme-Conditioned Refinement of Generic Transition-State Priors

Recent transition-state generators have shown that reaction geometry can be learned as a transferable prior from reactant and product structures. Enzymatic reactions, however, pose a different modeling problem: the relevant transition region is embedded in a specific active site, whereas enzyme-specific transition-state structures are rarely available as supervision. We present CatalyState, which treats this setting as inference over a frozen reaction prior rather than as a new coordinate-generation problem. Given a generic transition-region proposal, CatalyState learns an endpoint-grounded enzyme–geometry preference and applies Catalytic Posterior Transport (CPT) to infer a constrained distribution over nearby reaction geometries. This formulation preserves transferable reaction chemistry in the prior while using scarce enzyme-specific information for local conditioning and enforcing mapped bond-edit, steric, and geometric validity explicitly. Across enzyme- and reaction-disjoint evaluations, CatalyState distinguishes native catalytic environments from corrupted pocket and catalytic-context controls, improves local proposals across multiple independently trained reaction priors, and shows transfer of the learned preference to an independent semiempirical reaction-coordinate evaluator. Strong local-search controls further show that enzyme compatibility is useful for within-system refinement without being equivalent to physical energy or transition-state accuracy. These results support adapting, rather than relearning, transferable reaction priors for enzyme-specific reaction geometry.