Elucidating the Space of Enzymatic Reaction: A Unified Benchmark and Pretrained Model

Existing reaction models primarily learn molecular transformations, whereas enzymatic reactions depend jointly on molecular structure and catalytic function. We formulate this problem as learning an enzymatic reaction space linking reactants, products, and Enzyme Commission (EC) annotations. To characterize this space, we introduce **VenusRX-Bench**, a unified benchmark for forward reaction prediction, single-step retrosynthesis, and EC-number prediction. **VenusRX-Bench** integrates reactions from multiple biochemical databases with standardized curation, leakage-controlled splits, and consistent evaluation. Benchmarking representative chemical and enzymatic models reveals a clear chemical-to-enzymatic domain gap, driven by limited domain data, catalytic-context dependency, and the difficulty of modeling large biomolecular structures. To bridge this gap, we develop **VenusRX**, a unified T5-style sequence-to-sequence model for enzymatic reactions. **VenusRX** jointly learns forward prediction, retrosynthesis, and reaction reconstruction, with two-stage training on millions of template-expanded reactions followed by real biochemical reactions. In addition, optional EC conditioning incorporates catalytic context, while Molecule Library-Constrained Decoding improves the generation of complex biomolecules. Across benchmark tasks and challenging generalization splits, **VenusRX** achieves the best or competitive performance in most evaluated settings over representative chemical and enzymatic baselines. Moreover, EC information consistently improves reaction prediction, while learned reaction representations support accurate EC prediction, revealing a bidirectional relationship between reaction structure and catalytic function. Together, **VenusRX-Bench** and **VenusRX** provide a unified framework for elucidating and modeling enzymatic reaction space.