Mostrar el registro sencillo del ítem
dc.contributor.author
Di Filippo, Juan Ignacio
dc.contributor.author
Cavasotto, Claudio Norberto
dc.date.available
2022-12-30T00:15:28Z
dc.date.issued
2021-08
dc.identifier.citation
Di Filippo, Juan Ignacio; Cavasotto, Claudio Norberto; Guided structure-based ligand identification and design via artificial intelligence modeling; Informa Healthcare; Expert Opinion On Drug Discovery; 17; 1; 8-2021; 71-78
dc.identifier.issn
1746-0441
dc.identifier.uri
http://hdl.handle.net/11336/182867
dc.description.abstract
Introduction: The implementation of Artificial Intelligence (AI) methodologies to drug discovery (DD) are on the rise. Several applications have been developed for structure-based DD, where AI methods provide an alternative framework for the identification of ligands for validated therapeutic targets, as well as the de novo design of ligands through generative models. Areas covered: Herein, the authors review the contributions between the 2019 to present period regarding the application of AI methods to structure-based virtual screening (SBVS) which encompasses mainly molecular docking applications–binding pose prediction and binary classification for ligand or hit identification-, as well as de novo drug design driven by machine learning (ML) generative models, and the validation of AI models in structure-based screening. Studies are reviewed in terms of their main objective, used databases, implemented methodology, input and output, and key results. Expert opinion: More profound analyses regarding the validity and applicability of AI methods in DD have begun to appear. In the near future, we expect to see more structure-based generative models- which are scarce in comparison to ligand-based generative models-, the implementation of standard guidelines for validating the generated structures, and more analyses regarding the validation of AI methods in structure-based DD.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Informa Healthcare
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
ARTIFICIAL INTELLIGENCE
dc.subject
DRUG DISCOVERY
dc.subject
MACHINE LEARNING
dc.subject
MOLECULAR DOCKING
dc.subject
STRUCTURE-BASED VIRTUAL SCREENING
dc.subject.classification
Medicina Química
dc.subject.classification
Medicina Básica
dc.subject.classification
CIENCIAS MÉDICAS Y DE LA SALUD
dc.title
Guided structure-based ligand identification and design via artificial intelligence modeling
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2022-09-21T14:10:03Z
dc.journal.volume
17
dc.journal.number
1
dc.journal.pagination
71-78
dc.journal.pais
Reino Unido
dc.journal.ciudad
Londres
dc.description.fil
Fil: Di Filippo, Juan Ignacio. Universidad Austral; Argentina. Universidad Austral. Facultad de Ciencias Biomédicas. Instituto de Investigaciones en Medicina Traslacional. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Medicina Traslacional; Argentina
dc.description.fil
Fil: Cavasotto, Claudio Norberto. Universidad Austral; Argentina. Universidad Austral. Facultad de Ciencias Biomédicas. Instituto de Investigaciones en Medicina Traslacional. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones en Medicina Traslacional; Argentina
dc.journal.title
Expert Opinion On Drug Discovery
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1080/17460441.2021.1979514
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/full/10.1080/17460441.2021.1979514
Archivos asociados