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dc.contributor.authorUlzurrun de Asanza Vega, María Eugeniaes-ES
dc.contributor.authorRíos Insua, Davides-ES
dc.contributor.authorRodríguez Santana, Simónes-ES
dc.date.accessioned2025-07-10T14:21:28Z-
dc.date.available2025-07-10T14:21:28Z-
dc.date.issued2025-05-22es_ES
dc.identifier.issn0022-2623es_ES
dc.identifier.urihttps:doi.org10.1021acs.jmedchem.5c00512es_ES
dc.identifier.urihttp://hdl.handle.net/11531/100553-
dc.descriptionArtículos en revistases_ES
dc.description.abstractes-ES
dc.description.abstractDual-specificity tyrosine-phosphorylation-regulated kinase 1A (DYRK1A) is implicated in several human diseases, including DYRK1A syndrome, cancer, and neurodegenerative disorders such as Alzheimer’s disease, making it a relevant therapeutic target. In this study, we combine artificial intelligence with traditional drug discovery methods to design nontoxic DYRK1A inhibitors. An ensemble QSAR model was used to predict binding affinities, while a directed message passing neural network evaluated toxicity. Novel compounds were generated using a hierarchical graph-based generative model and subsequently refined through molecular docking, chemical synthesis, and experimental validation. This pipeline led to the identification of pyrazolyl-1H-pyrrolo[2,3-b]pyridine 1 as a potent inhibitor, from which a new derivative series was developed. Enzymatic assays confirmed nanomolar DYRK1A inhibition, and additional assays demonstrated antioxidant and anti-inflammatory properties. Overall, the resulting compounds exhibit strong DYRK1A inhibition and favorable pharmacological profiles.en-GB
dc.language.isoen-GBes_ES
dc.sourceRevista: Journal of Medicinal Chemistry, Periodo: 1, Volumen: online, Número: 10, Página inicial: 10346, Página final: 10364es_ES
dc.subject.otherInstituto de Investigación Tecnológica (IIT)es_ES
dc.titleAI-Driven De Novo Design and Development of Nontoxic DYRK1A Inhibitorses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.description.versioninfo:eu-repo/semantics/publishedVersiones_ES
dc.rights.holderes_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.keywordses-ES
dc.keywordsAI-driven drug design, Molecular generative models, DYRK1A inhibitionen-GB
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