ZymPro: Transition-Aware Generative Modeling of Enzymatic Reaction Processes

Enzymatic reactions are fundamental to biological processes and underpin a wide range of biotechnological applications. During these reactions, transition states represent key molecular configurations that reflect enzyme-specific catalytic environments, which lower the activation barriers to product formation. However, despite extensive research on enzymatic reaction prediction, transition states remain underexplored in generative modeling, leaving unclear how they can support reaction prediction. Thus, we introduce ZymPro for generative modeling of enzymatic reaction processes through explicit transition-state generation. ZymPro employs energy-based process supervision from reaction paths and introduces a novel process-corrected diffusion that refines denoising predictions toward the transition state. The learned process knowledge is then transferred to product generation and enzyme compatibility ranking. Extensive experiments demonstrate that ZymPro achieves strong performance in enzyme compatibility ranking for unseen reaction rules and high accuracy in product generation for unseen products. The code is available at https://anonymous.4open.science/r/ZymPro-26E4/.