TIGER: Text-Informed Generalized Enzyme-Reaction Retrieval
For computational biologists, TIGER provides a robust and transferable framework for enzyme-reaction retrieval, overcoming limitations of existing methods in generalization and direction asymmetry.
TIGER addresses poor generalization and asymmetry in bidirectional enzyme-reaction retrieval by using protein-to-text generation models and a dynamic gating network to fuse textual semantics with sequence features, achieving significant improvements over state-of-the-art baselines across diverse distributions.
Enzyme-reaction retrieval is a fundamental problem in computational biology, underpinning enzyme characterization, reaction mechanism elucidation, and the rational design of metabolic pathways and biocatalysts. As a bidirectional task, it entails both enzyme-to-reaction and reaction-to-enzyme mapping. However, existing approaches suffer from poor generalization across tasks and distributions, with performance highly sensitive to dataset splits and substantial asymmetry between retrieval directions. To address these challenges, we present TIGER, a Text-Informed Generalized Enzyme-Reaction Retrieval framework that leverages protein-to-text generation models to distill textual semantic knowledge from enzyme sequences, providing a generalized representation that bridges enzymes and biochemical reactions. To ensure the quality and reliability of textual semantics, we design a Dynamic Gating Network that adaptively fuses text-derived knowledge with sequence features, enabling more consistent and informative enzyme representations, while a Structure-Shared Feature Projector aligns enzyme and reaction representations within a unified latent space. Extensive experiments demonstrate that, under bidirectional retrieval supervision, TIGER significantly outperforms state-of-the-art baselines across diverse distributions and exhibits strong robustness and transferability across tasks.