{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T06:48:18Z","timestamp":1791874098523,"version":"4.3.4"},"reference-count":0,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000},"content-version":"am","delay-in-days":1,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Motivation<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Availability and implementation<\/jats:title>\n                    <jats:p>Source code is available at https:\/\/github.com\/ykxhs\/GERO.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Supplementary information<\/jats:title>\n                    <jats:p>Supplementary data are available at XXXX online.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btag764","type":"journal-article","created":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T11:47:48Z","timestamp":1791632868000},"source":"Crossref","is-referenced-by-count":0,"title":["Joint enzyme-reaction retrieval and catalytic optima prediction via multimodal fusion"],"prefix":"10.1093","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-3713-523X","authenticated-orcid":false,"given":"Yueyi","family":"Cai","sequence":"first","affiliation":[{"name":"Wuhan University School of Artificial Intelligence, , Wuhan, 430072, Hubei,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2193-8551","authenticated-orcid":false,"given":"Feng","family":"Yang","sequence":"additional","affiliation":[{"name":"Wuhan University School of Computer Science, , Wuhan, 430072, Hubei,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9344-7415","authenticated-orcid":false,"given":"Juan","family":"Liu","sequence":"additional","affiliation":[{"name":"Wuhan University School of Artificial Intelligence, , Wuhan, 430072, Hubei,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,10,12]]},"container-title":["Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btag764\/71892487\/btag764.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article-pdf\/doi\/10.1093\/bioinformatics\/btag764\/71892487\/btag764.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T06:06:41Z","timestamp":1791871601000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/bioinformatics\/advance-article\/doi\/10.1093\/bioinformatics\/btag764\/8911835"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,12]]},"references-count":0,"URL":"https:\/\/doi.org\/10.1093\/bioinformatics\/btag764","relation":{},"ISSN":["1367-4811"],"issn-type":[{"value":"1367-4811","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,12]]},"article-number":"btag764"}}