Quantum QSAR for drug discovery

Autores
Giraldo, Alejandro; Ruiz, Daniel; Caruso, Mariano; Bellomo, Guido
Año de publicación
2025
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Quantitative Structure-Activity Relationship (QSAR) modeling is key in drug discovery, but classical methods face limitations when handling high-dimensional data and capturing complex molecular interactions. This research proposes enhancing QSAR techniques through Quantum Support Vector Machines (QSVMs), which leverage quantum computing principles to process information in Hilbert spaces. By using quantum data encoding and quantum kernel functions, we aim to develop more accurate and efficient predictive models.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
QSAR
classification
drug discovery
support vector machines
quantum kernel
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/190661
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/190661
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Quantum QSAR for drug discoveryQSAR cuántico para el descubrimiento de fármacosGiraldo, AlejandroRuiz, DanielCaruso, MarianoBellomo, GuidoCiencias InformáticasQSARclassificationdrug discoverysupport vector machinesquantum kernelQuantitative Structure-Activity Relationship (QSAR) modeling is key in drug discovery, but classical methods face limitations when handling high-dimensional data and capturing complex molecular interactions. This research proposes enhancing QSAR techniques through Quantum Support Vector Machines (QSVMs), which leverage quantum computing principles to process information in Hilbert spaces. By using quantum data encoding and quantum kernel functions, we aim to develop more accurate and efficient predictive models.Sociedad Argentina de Informática e Investigación Operativa2025-08info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf19-27http://sedici.unlp.edu.ar/handle/10915/190661enginfo:eu-repo/semantics/altIdentifier/url/https://revistas.unlp.edu.ar/JAIIO/article/view/19788info:eu-repo/semantics/altIdentifier/issn/2451-7496info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2026-02-26T11:39:46Zoai:sedici.unlp.edu.ar:10915/190661Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292026-02-26 11:39:46.81SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Quantum QSAR for drug discovery
QSAR cuántico para el descubrimiento de fármacos
title Quantum QSAR for drug discovery
spellingShingle Quantum QSAR for drug discovery
Giraldo, Alejandro
Ciencias Informáticas
QSAR
classification
drug discovery
support vector machines
quantum kernel
title_short Quantum QSAR for drug discovery
title_full Quantum QSAR for drug discovery
title_fullStr Quantum QSAR for drug discovery
title_full_unstemmed Quantum QSAR for drug discovery
title_sort Quantum QSAR for drug discovery
dc.creator.none.fl_str_mv Giraldo, Alejandro
Ruiz, Daniel
Caruso, Mariano
Bellomo, Guido
author Giraldo, Alejandro
author_facet Giraldo, Alejandro
Ruiz, Daniel
Caruso, Mariano
Bellomo, Guido
author_role author
author2 Ruiz, Daniel
Caruso, Mariano
Bellomo, Guido
author2_role author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
QSAR
classification
drug discovery
support vector machines
quantum kernel
topic Ciencias Informáticas
QSAR
classification
drug discovery
support vector machines
quantum kernel
dc.description.none.fl_txt_mv Quantitative Structure-Activity Relationship (QSAR) modeling is key in drug discovery, but classical methods face limitations when handling high-dimensional data and capturing complex molecular interactions. This research proposes enhancing QSAR techniques through Quantum Support Vector Machines (QSVMs), which leverage quantum computing principles to process information in Hilbert spaces. By using quantum data encoding and quantum kernel functions, we aim to develop more accurate and efficient predictive models.
Sociedad Argentina de Informática e Investigación Operativa
description Quantitative Structure-Activity Relationship (QSAR) modeling is key in drug discovery, but classical methods face limitations when handling high-dimensional data and capturing complex molecular interactions. This research proposes enhancing QSAR techniques through Quantum Support Vector Machines (QSVMs), which leverage quantum computing principles to process information in Hilbert spaces. By using quantum data encoding and quantum kernel functions, we aim to develop more accurate and efficient predictive models.
publishDate 2025
dc.date.none.fl_str_mv 2025-08
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
Objeto de conferencia
http://purl.org/coar/resource_type/c_5794
info:ar-repo/semantics/documentoDeConferencia
format conferenceObject
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/190661
url http://sedici.unlp.edu.ar/handle/10915/190661
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://revistas.unlp.edu.ar/JAIIO/article/view/19788
info:eu-repo/semantics/altIdentifier/issn/2451-7496
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
19-27
dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
instacron:UNLP
reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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score 12.665996

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