Artificial Intelligence Deconstructs Drug Targeting in Vivo by Leveraging a Transformer Platform | ||
| dc.conicet.fuente | Elsevier | |
| dc.conicet.organizacion | Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur | |
| dc.conicet.paisOrganizacion | Argentina | |
| dc.conicet.produccionid | 11110844 | |
| dc.contributor.author | Fernandez, Ariel | |
| dc.date.accessioned | 2023-09-19T10:04:41Z | |
| dc.date.available | 2023-09-19T10:04:41Z | |
| dc.date.issued | 2021-07 | |
| dc.date.updated | 2023-09-14T17:39:40Z | |
| dc.description.abstract | Lead optimization in structure-based drug design ultimately requires that the therapeutic agent be evaluated in the cellular context. However, the in vivo control of the target structure remains unyielding to computational modeling. This situation may change as transformer technologies enable a deconstruction of in vivo cooperativity steering drug-induced protein folding. | es |
| dc.description.fil | Fil: Fernandez, Ariel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Química del Sur. Universidad Nacional del Sur. Departamento de Química. Instituto de Química del Sur; Argentina | |
| dc.format | application/pdf | es |
| dc.identifier.citation | Fernandez, Ariel; Artificial Intelligence Deconstructs Drug Targeting in Vivo by Leveraging a Transformer Platform; American Chemical Society; ACS Medicinal Chemistry Letters; 12; 7; 7-2021; 1052-1055 | es |
| dc.identifier.institucional | CONICET | |
| dc.identifier.issn | 1948-5875 | es |
| dc.identifier.repo | CONICET Digital | |
| dc.identifier.uri | http://hdl.handle.net/11336/211913 | |
| dc.journal.number | 7 | es |
| dc.journal.pagination | 1052-1055 | es |
| dc.journal.pais | Estados Unidos | es |
| dc.journal.title | ACS Medicinal Chemistry Letters | es |
| dc.journal.volume | 12 | es |
| dc.language.iso | eng | es |
| dc.publisher | American Chemical Society | es |
| dc.relation.alternativeid | info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1021/acsmedchemlett.1c00237 | |
| dc.rights | info:eu-repo/semantics/restrictedAccess | * |
| dc.rights.uri | https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ | * |
| dc.subject | ARTIFICIAL INTELLIGENCE | es |
| dc.subject | DRUG-INDUCED PROTEIN FOLDING | es |
| dc.subject | IN VIVO PROTEIN FOLDING | es |
| dc.subject | MOLECULAR DYNAMICS | es |
| dc.subject | STRUCTURE-BASED DRUG DESIGN | es |
| dc.subject | TRANSFORMER | es |
| dc.subject.classification | Ciencias de la Información y Bioinformática | es |
| dc.subject.classification | Ciencias de la Computación e Información | es |
| dc.subject.classification | CIENCIAS NATURALES Y EXACTAS | es |
| dc.title | Artificial Intelligence Deconstructs Drug Targeting in Vivo by Leveraging a Transformer Platform | es |
| dc.type | info:eu-repo/semantics/article | |
| dc.type | info:ar-repo/semantics/artículo | |
| dc.type | info:eu-repo/semantics/publishedVersion | |
Archivos
Bloque original
Cargando...
- Nombre:
- CONICET_Digital_Nro.1fa36fad-e025-4e96-bed7-e17befdf2737_B.pdf
- Tamaño:
- 1.55 MB
- Formato:
- Adobe Portable Document Format