Joint enzyme–reaction retrieval and catalytic optima prediction via multimodal fusion
- 1School of Artificial Intelligence, Wuhan University, No. 299, Bayi Road, Luojia Hill Street, Wuhan, Hubei, 430072, China
- 2Wuhan University School of Computer Science, , No. 299, Bayi Road, Luojia Hill Street, Wuhan, Hubei, 430072, China
Abstract
Motivation: Enzyme–reaction retrieval prioritizes candidate biocatalysts for experimental follow-up, where recommendations should indicate catalytic compatibility and suitable pH and temperature conditions. Existing retrieval models optimize matching scores, whereas catalytic optima predictors are typically enzyme-level regressors because condition annotations are sparse. This separation leaves ranked pairs unevaluated for condition suitability and optima predictions without reaction context.
Results: We present GERO, a multimodal fusion framework integrating enzyme sequence semantics, sequence-derived pocket geometry, and molecular reaction representations through feature-gated fusion for condition-aware retrieval and catalytic optima estimation. We define the tolerance-restricted hit rate (Hit@k-TR), which requires both correct top-k retrieval and condition predictions within predefined tolerances. Across enzyme- and reaction-similarity splits, GERO improves Hit@k-TR over two-stage baselines under the primary tolerances. Benchmark examples and an iodinin biosynthesis case study illustrate candidate ranking and assay-condition estimation for experimental prioritization.
Availability and implementation: Source code is available at https://github.com/ykxhs/GERO.
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BibTeX
@article{cai2026joint,
title = {{Joint enzyme–reaction retrieval and catalytic optima prediction via multimodal fusion}},
author = {Yueyi Cai and Feng Yang and Juan Liu},
journal = {Bioinformatics},
year = {2026},
month = oct,
doi = {10.1093/bioinformatics/btag764},
url = {https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag764/8911835},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Joint enzyme–reaction retrieval and catalytic optima prediction via multimodal fusion
AU - Yueyi Cai
AU - Feng Yang
AU - Juan Liu
PY - 2026
DA - 2026/10/12
JO - Bioinformatics
DO - 10.1093/bioinformatics/btag764
UR - https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag764/8911835
AB - Motivation: Enzyme–reaction retrieval prioritizes candidate biocatalysts for experimental follow-up, where recommendations should indicate catalytic compatibility and suitable pH and temperature conditions. Existing retrieval models optimize matching scores, whereas catalytic optima predictors are typically enzyme-level regressors because condition annotations are sparse. This separation leaves ranked pairs unevaluated for condition suitability and optima predictions without reaction context. Results: We present GERO, a multimodal fusion framework integrating enzyme sequence semantics, sequence-derived pocket geometry, and molecular reaction representations through feature-gated fusion for condition-aware retrieval and catalytic optima estimation. We define the tolerance-restricted hit rate (Hit@k-TR), which requires both correct top-k retrieval and condition predictions within predefined tolerances. Across enzyme- and reaction-similarity splits, GERO improves Hit@k-TR over two-stage baselines under the primary tolerances. Benchmark examples and an iodinin biosynthesis case study illustrate candidate ranking and assay-condition estimation for experimental prioritization. Availability and implementation: Source code is available at https://github.com/ykxhs/GERO.
SN - 1367-4803
SN - 1367-4811
ER -