Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/184294
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dc.contributor.authorAntolin, Albert A.-
dc.contributor.authorCascante i Serratosa, Marta-
dc.date.accessioned2022-03-21T18:13:02Z-
dc.date.available2022-03-21T18:13:02Z-
dc.date.issued2021-10-20-
dc.identifier.issn1544-9173-
dc.identifier.urihttps://hdl.handle.net/2445/184294-
dc.description.abstractMichaelis constants (Km) are essential to predict the catalytic rate of enzymes, but are not widely available. A new study in PLOS Biology uses artificial intelligence (AI) to accurately predict Km on a proteome-wide scale, paving the way for dynamic, genome-wide modeling of metabolism.-
dc.format.extent4 p.-
dc.format.mimetypeapplication/pdf-
dc.language.isoeng-
dc.publisherPublic Library of Science (PLoS)-
dc.relation.isformatofReproducció del document publicat a: https://doi.org/10.1371/journal.pbio.3001415-
dc.relation.ispartofPLoS Biology, 2021, vol. 19, num. 10, p. e3001415-
dc.relation.urihttps://doi.org/10.1371/journal.pbio.3001415-
dc.rightscc-by (c) Antolin, Albert A. et al., 2021-
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/-
dc.sourceArticles publicats en revistes (Bioquímica i Biomedicina Molecular)-
dc.subject.classificationCinètica enzimàtica-
dc.subject.classificationIntel·ligència artificial-
dc.subject.otherEnzyme kinetics-
dc.subject.otherArtificial intelligence-
dc.titleAI delivers Michaelis constants as fuel for genome-scale metabolic models-
dc.typeinfo:eu-repo/semantics/article-
dc.typeinfo:eu-repo/semantics/publishedVersion-
dc.identifier.idgrec717408-
dc.date.updated2022-03-21T18:13:02Z-
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess-
Appears in Collections:Articles publicats en revistes (Bioquímica i Biomedicina Molecular)

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