{
  "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.\n\nResults: 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.\n\nAvailability and implementation: Source code is available at https://github.com/ykxhs/GERO.",
  "abstract_shown": true,
  "affiliations": [
    "School of Artificial Intelligence, Wuhan University, No. 299, Bayi Road, Luojia Hill Street, Wuhan, Hubei, 430072, China",
    "Wuhan University School of Computer Science, , No. 299, Bayi Road, Luojia Hill Street, Wuhan, Hubei, 430072, China"
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Yueyi Cai",
      "orcid": "0009-0004-3713-523X"
    },
    {
      "affs": [
        1
      ],
      "name": "Feng Yang",
      "orcid": "0000-0003-2193-8551"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Juan Liu",
      "orcid": "0000-0001-9344-7415"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ahqhx0wtt4hc5.bib",
  "cited_by": 0,
  "doi": "10.1093/bioinformatics/btag764",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13716482/",
  "id": "ahqhx0wtt4hc5",
  "issns": [
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    "1367-4811"
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  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "AcademicSubjects/SCI01060"
    ],
    [
      "heading",
      "Data and Text Mining"
    ],
    [
      "heading",
      "Original Paper"
    ],
    [
      "openalex",
      "1312|Machine Learning in Bioinformatics"
    ]
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  "openalex": "W7162066491",
  "pdf_url": "https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btag764/71892487/btag764.pdf",
  "pmcid": "PMC13716482",
  "pmid": "42865019",
  "published": "2026-10-12",
  "publisher": "Oxford University Press (OUP)",
  "ris": "https://research.draconitic.com/paper/ahqhx0wtt4hc5.ris",
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    {
      "first_seen": "2026-10-30T12:19:38Z",
      "native_id": "10.1093/bioinformatics/btag764",
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      "url": "https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag764/8911835"
    },
    {
      "first_seen": "2026-11-01T12:23:25Z",
      "native_id": "PMC13716482",
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      "url": "https://doi.org/10.1093/bioinformatics/btag764"
    }
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  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Machine Learning in Bioinformatics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "Joint enzyme–reaction retrieval and catalytic optima prediction via multimodal fusion",
  "title_text": "Joint enzyme–reaction retrieval and catalytic optima prediction via multimodal fusion",
  "type": "peer-reviewed",
  "updated_at": "2026-11-01T12:23:25Z",
  "url": "https://research.draconitic.com/paper/ahqhx0wtt4hc5",
  "venue": "Bioinformatics",
  "versions": []
}