ArticlePatterns (New York, N.Y.)2026
EnzymeHunter: Achieving fine-grained enzyme function prediction with a hierarchically aware contrastive learning framework.
Article in Patterns (New York, N.Y.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- EnzymeHunter: Achieving fine-grained enzyme function prediction with a hierarchically aware contrastive learning framework.Patterns (New York, N.Y.) · 2026Article
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8 authors.
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Abstract
Accurate enzyme function annotation is a grand challenge due to the vast number of uncharacterized proteins and the difficulty of distinguishing subtle functions. We introduce EnzymeHunter, a deep-learning framework that achieves fine-grained prediction via a hierarchically aware contrastive learning strategy. By integrating sequence and structural information and using the Enzyme Commission (EC) hierarchy to guide its loss function, our model learns a functionally coherent embedding space where distances reflect precise levels of catalytic similarity. EnzymeHunter significantly outperforms state-of-the-art models, particularly in challenging scenarios, achieving fine-grained precision down to the fourth EC level, maintaining robust performance in low-homology cases, and accurately predicting rare enzyme classes. In a proteome-wide application to
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