Decoding enzyme-substrate specificity with EZSpecificity!

We are pleased to share that our group has published a new Preview article in Chem Catalysis, highlighting a recent breakthrough AI method for enzyme–substrate specificity prediction.

The Preview discusses EZSpecificity (Zhao et. al. (2025). Nature, 647, 639), an advanced deep-learning framework based on cross-attention graph neural networks that enables accurate prediction of enzyme–substrate relationships by jointly modeling enzyme structures and substrate graphs. This method addresses a major challenge in enzyme specificity prediction and has broad implications for biocatalyst discovery, enzyme engineering, and metabolic pathway design.

Our Preview provides a critical perspective on the methodological innovations underlying EZSpecificity, its performance advantages relative to the current methods, and its potential impact for future AI-guided enzyme research.

Read the Preview👉 https://doi.org/10.1016/j.checat.2025.101633