# EnzyREx: deep learning enables reaction-based comprehensive enzyme screening

“EnzyREx: deep learning enables reaction-based comprehensive enzyme screening” (2026), by Keisuke Hirota et al. Genetics and Biochemistry research paper. Original abstract below, unchanged. A free PDF is available.

- Authors: Keisuke Hirota, Takuji Yamada
- Published: November 3, 2026
- Field: Genetics and Biochemistry
- DOI: https://doi.org/10.1186/s13059-026-04312-7
- Free PDF: https://www.biorxiv.org/content/biorxiv/early/2025/07/31/2025.07.28.667344.full.pdf

## Abstract

Orphan enzymes for which the corresponding genes remain unidentified represent a major gap in functional annotation in genomic analysis. We present EnzyREx, an AI-based framework for comprehensive enzyme screening, to explore novel enzyme candidates. EnzyREx consists of two deep learning models to classify proteins as enzymes and non-enzymes and predict catalytic capability for enzyme‒reaction pairs. Both models achieved performance comparable to or better than that of existing software. When applied to 1,255 microbiome-related orphan reactions, EnzyREx identified candidate proteins for 897 reactions. EnzyREx provides a scalable approach for linking unannotated proteins to orphan reactions and extending functional annotation.

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