{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T16:51:08Z","timestamp":1780332668897,"version":"3.54.1"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"Supplement_1","license":[{"start":{"date-parts":[[2022,6,27]],"date-time":"2022-06-27T00:00:00Z","timestamp":1656288000000},"content-version":"vor","delay-in-days":3,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["61872297"],"award-info":[{"award-number":["61872297"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shaanxi Provincial Key Research & Development Program, China","award":["2020KW-063"],"award-info":[{"award-number":["2020KW-063"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,24]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Motivation<\/jats:title><jats:p>During lead compound optimization, it is crucial to identify pathways where a drug-like compound is metabolized. Recently, machine learning-based methods have achieved inspiring progress to predict potential metabolic pathways for drug-like compounds. However, they neglect the knowledge that metabolic pathways are dependent on each other. Moreover, they are inadequate to elucidate why compounds participate in specific pathways.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>To address these issues, we propose a novel Multi-Label Graph Learning framework of Metabolic Pathway prediction boosted by pathway interdependence, called MLGL-MP, which contains a compound encoder, a pathway encoder and a multi-label predictor. The compound encoder learns compound embedding representations by graph neural networks. After constructing a pathway dependence graph by re-trained word embeddings and pathway co-occurrences, the pathway encoder learns pathway embeddings by graph convolutional networks. Moreover, after adapting the compound embedding space into the pathway embedding space, the multi-label predictor measures the proximity of two spaces to discriminate which pathways a compound participates in. The comparison with state-of-the-art methods on KEGG pathways demonstrates the superiority of our MLGL-MP. Also, the ablation studies reveal how its three components contribute to the model, including the pathway dependence, the adapter between compound embeddings and pathway embeddings, as well as the pre-training strategy. Furthermore, a case study illustrates the interpretability of MLGL-MP by indicating crucial substructures in a compound, which are significantly associated with the attending metabolic pathways. It is anticipated that this work can boost metabolic pathway predictions in drug discovery.<\/jats:p><\/jats:sec><jats:sec><jats:title>Availability and implementation<\/jats:title><jats:p>The code and data underlying this article are freely available at https:\/\/github.com\/dubingxue\/MLGL-MP.<\/jats:p><\/jats:sec>","DOI":"10.1093\/bioinformatics\/btac222","type":"journal-article","created":{"date-parts":[[2022,4,14]],"date-time":"2022-04-14T11:10:15Z","timestamp":1649934615000},"page":"i325-i332","source":"Crossref","is-referenced-by-count":21,"title":["MLGL-MP: a Multi-Label Graph Learning framework enhanced by pathway interdependence for Metabolic Pathway prediction"],"prefix":"10.1093","volume":"38","author":[{"given":"Bing-Xue","family":"Du","sequence":"first","affiliation":[{"name":"School of Life Sciences, Northwestern Polytechnical University , Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng-Cheng","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Life Sciences, Northwestern Polytechnical University , Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bei","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Life Sciences, Northwestern Polytechnical University , Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siu-Ming","family":"Yiu","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The University of Hong Kong , Hong Kong 999077 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Arnold K","family":"Nyamabo","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University , Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Computer Science, Northwestern Polytechnical University , Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2303-273X","authenticated-orcid":false,"given":"Jian-Yu","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Life Sciences, Northwestern Polytechnical University , Xi\u2019an 710072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2022,6,27]]},"reference":[{"key":"2023041407551149400_","doi-asserted-by":"crossref","first-page":"2547","DOI":"10.1093\/bioinformatics\/btz954","article-title":"A deep learning architecture for metabolic pathway prediction","volume":"36","author":"Baranwal","year":"2020","journal-title":"Bioinformatics"},{"key":"2023041407551149400_","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1111\/cns.13207","article-title":"B vitamins in the nervous system: current knowledge of the biochemical modes of action and synergies of thiamine, pyridoxine, and cobalamin","volume":"26","author":"Calder\u00f3n-Ospina","year":"2020","journal-title":"CNS Neurosci. 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