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Liquid chromatography\u2013tandem mass spectrometry, one of the most widely used analysis platforms, can detect thousands of molecules in a sample, the vast majority of which remain unidentified even with best-of-class methods. Here we present LC-MS\n                    <jats:sup>2<\/jats:sup>\n                    Struct, a machine learning framework for structural annotation of small-molecule data arising from liquid chromatography\u2013tandem mass spectrometry (LC-MS\n                    <jats:sup>2<\/jats:sup>\n                    ) measurements. LC-MS\n                    <jats:sup>2<\/jats:sup>\n                    Struct jointly predicts the annotations for a set of mass spectrometry features in a sample, using a novel structured prediction model trained to optimally combine the output of state-of-the-art MS\n                    <jats:sup>2<\/jats:sup>\n                    scorers and observed retention orders. We evaluate our method on a dataset covering all publicly available reversed-phase LC-MS\n                    <jats:sup>2<\/jats:sup>\n                    data in the MassBank reference database, including 4,327 molecules measured using 18 different LC conditions from 16 contributors, greatly expanding the chemical analytical space covered in previous multi-MS\n                    <jats:sup>2<\/jats:sup>\n                    scorer evaluations. LC-MS\n                    <jats:sup>2<\/jats:sup>\n                    Struct obtains significantly higher annotation accuracy than earlier methods and improves the annotation accuracy of state-of-the-art MS\n                    <jats:sup>2<\/jats:sup>\n                    scorers by up to 106%. The use of stereochemistry-aware molecular fingerprints improves prediction performance, which highlights limitations in existing approaches and has strong implications for future computational LC-MS\n                    <jats:sup>2<\/jats:sup>\n                    developments.\n                  <\/jats:p>","DOI":"10.1038\/s42256-022-00577-2","type":"journal-article","created":{"date-parts":[[2022,12,19]],"date-time":"2022-12-19T12:31:45Z","timestamp":1671453105000},"page":"1224-1237","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data"],"prefix":"10.1038","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6200-5462","authenticated-orcid":false,"given":"Eric","family":"Bach","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6868-8145","authenticated-orcid":false,"given":"Emma L.","family":"Schymanski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0705-4314","authenticated-orcid":false,"given":"Juho","family":"Rousu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,12,19]]},"reference":[{"key":"577_CR1","doi-asserted-by":"publisher","first-page":"12549","DOI":"10.1073\/pnas.1516878112","volume":"112","author":"RR da Silva","year":"2015","unstructured":"da Silva, R. 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