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http://hdl.handle.net/11531/101288| Título : | AI-Driven De Novo Design and Development of Nontoxic DYRK1A Inhibitors |
| Autor : | González García, Eduardo Varas Pardo, Pablo González Naranjo, Pedro Ulzurrun de Asanza Vega, María Eugenia Marcos Ayuso, Guillermo Pérez Martín, Concepción Páez Prosper, Juan Antonio Ríos Insua, David Rodríguez Santana, Simón Campillo Martin, Nuria Eugenia |
| Fecha de publicación : | 22-may-2025 |
| Resumen : | Dual-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. Dual-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. |
| Descripción : | Artículos en revistas |
| URI : | https://doi.org/10.1021/acs.jmedchem.5c00512 |
| ISSN : | 0022-2623 |
| Aparece en las colecciones: | Artículos |
Ficheros en este ítem:
| Fichero | Descripción | Tamaño | Formato | |
|---|---|---|---|---|
| IIT-25-127R.pdf | 5,4 MB | Adobe PDF | Visualizar/Abrir | |
| IIT-25-127R_preview.pdf | 3,18 kB | Adobe PDF | Visualizar/Abrir |
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