Unified model and benchmark connect enzymes to reactions and molecules
Elucidating the Space of Enzymatic Reaction: A Unified Benchmark and Pretrained Model
Artificial Intelligence
Summary
Predicting chemical reactions involving enzymes is hard because enzymes change molecules depending on their specific function, not just their structures. The authors created a benchmark called VenusRX-Bench that combines data from several biochemical sources to test how well models predict enzymatic reactions, the starting materials needed, and enzyme types. They also made a new computer model called VenusRX that learns from millions of reactions and includes information about enzyme function to improve accuracy. Their work helps clarify how enzyme types and reactions relate, making it easier to study and predict complex biochemical processes.
What this means in practice
- •For pharmaceutical chemists: Identify chemical pathways and enzymes needed to synthesize complex drug molecules more accurately using the VenusRX model.
- •For biotechnology developers: Design enzyme-catalyzed reactions by predicting feasible reactants, products, and enzyme classifications to engineer novel biochemical processes.
Authors
Yutong Hu, Tianming Huang, Yanbo Zhao, Qiongyu Zhang, Shixiang Tang, Lei Bai, Ziyi Zhou, Liang Hong, Pan Tan
Abstract
Existing reaction models primarily learn molecular transformations, whereas enzy- matic 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, ret- rosynthesis, 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 on most evaluated settings over representative chem- ical 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