Virtual Screening of Argentinian Natural Products to Identify Anti-Cancer Aurora Kinase A Inhibitors: A Combined Machine Learning and Molecular Docking Approach

Autores
Cartagena, Génesis; Jadán, Evelin; Guarimata, Juan Diego
Año de publicación
2025
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The Aurora kinase A (Aurora-A), overexpressed in cancer cells, represents a promising anti-cancer therapeutic target due to its role in mitotic progression and chromosome instability. Aurora-A contains a recently described drug pocket within its Targeting Protein for Xklp2 (TPX2) interaction site, offering a promising target for small-molecule disruption and selective inhibition. In this study, 1281 natural products from Argentina’s database (NaturAr), encompassing chemically diverse and structurally rich metabolites, were evaluated using a machine learning model based on molecular fingerprints and variational autoencoders (VAEs) to predict inhibitory activity with high-throughput efficiency. From this initial screening, 624 compounds were classified as active type against Aurora-A, and subsequently subjected to molecular docking using FRED software (v4.3.0.3) against the Aurora-A crystal structure (PDB: 5OSD), focusing on the TPX2-binding interface. Among them, 117 compounds with various scaffolds showed better binding scores than the cocrystallized ligand, highlighting their potential to interact with the druggable target site through stable and specific molecular contacts. This workflow effectively prioritized compounds of natural origin from Argentina for the discovery of new Aurora-A kinase inhibitors, demonstrating the value of integrating AI-driven screening with structurebased modeling. These findings highlight the identification of novel scaffolds with high binding potential, offering promising starting points for the development of selective Aurora-A inhibitors.
Centro de Química Inorgánica
Materia
Química
aurora kinase A
machine learning
molecular docking
Argentinian natural products
anti-cancer compounds
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:10915/189013
Acceder
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oai_identifier_str oai:sedici.unlp.edu.ar:10915/189013
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Virtual Screening of Argentinian Natural Products to Identify Anti-Cancer Aurora Kinase A Inhibitors: A Combined Machine Learning and Molecular Docking ApproachCartagena, GénesisJadán, EvelinGuarimata, Juan DiegoQuímicaaurora kinase Amachine learningmolecular dockingArgentinian natural productsanti-cancer compoundsThe Aurora kinase A (Aurora-A), overexpressed in cancer cells, represents a promising anti-cancer therapeutic target due to its role in mitotic progression and chromosome instability. Aurora-A contains a recently described drug pocket within its Targeting Protein for Xklp2 (TPX2) interaction site, offering a promising target for small-molecule disruption and selective inhibition. In this study, 1281 natural products from Argentina’s database (NaturAr), encompassing chemically diverse and structurally rich metabolites, were evaluated using a machine learning model based on molecular fingerprints and variational autoencoders (VAEs) to predict inhibitory activity with high-throughput efficiency. From this initial screening, 624 compounds were classified as active type against Aurora-A, and subsequently subjected to molecular docking using FRED software (v4.3.0.3) against the Aurora-A crystal structure (PDB: 5OSD), focusing on the TPX2-binding interface. Among them, 117 compounds with various scaffolds showed better binding scores than the cocrystallized ligand, highlighting their potential to interact with the druggable target site through stable and specific molecular contacts. This workflow effectively prioritized compounds of natural origin from Argentina for the discovery of new Aurora-A kinase inhibitors, demonstrating the value of integrating AI-driven screening with structurebased modeling. These findings highlight the identification of novel scaffolds with high binding potential, offering promising starting points for the development of selective Aurora-A inhibitors.Centro de Química Inorgánica2025-11-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/189013enginfo:eu-repo/semantics/altIdentifier/issn/2673-4583info:eu-repo/semantics/altIdentifier/doi/10.3390/ecsoc-29-26728info: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:UNLP2025-12-23T11:54:04Zoai:sedici.unlp.edu.ar:10915/189013Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-12-23 11:54:05.147SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Virtual Screening of Argentinian Natural Products to Identify Anti-Cancer Aurora Kinase A Inhibitors: A Combined Machine Learning and Molecular Docking Approach
title Virtual Screening of Argentinian Natural Products to Identify Anti-Cancer Aurora Kinase A Inhibitors: A Combined Machine Learning and Molecular Docking Approach
spellingShingle Virtual Screening of Argentinian Natural Products to Identify Anti-Cancer Aurora Kinase A Inhibitors: A Combined Machine Learning and Molecular Docking Approach
Cartagena, Génesis
Química
aurora kinase A
machine learning
molecular docking
Argentinian natural products
anti-cancer compounds
title_short Virtual Screening of Argentinian Natural Products to Identify Anti-Cancer Aurora Kinase A Inhibitors: A Combined Machine Learning and Molecular Docking Approach
title_full Virtual Screening of Argentinian Natural Products to Identify Anti-Cancer Aurora Kinase A Inhibitors: A Combined Machine Learning and Molecular Docking Approach
title_fullStr Virtual Screening of Argentinian Natural Products to Identify Anti-Cancer Aurora Kinase A Inhibitors: A Combined Machine Learning and Molecular Docking Approach
title_full_unstemmed Virtual Screening of Argentinian Natural Products to Identify Anti-Cancer Aurora Kinase A Inhibitors: A Combined Machine Learning and Molecular Docking Approach
title_sort Virtual Screening of Argentinian Natural Products to Identify Anti-Cancer Aurora Kinase A Inhibitors: A Combined Machine Learning and Molecular Docking Approach
dc.creator.none.fl_str_mv Cartagena, Génesis
Jadán, Evelin
Guarimata, Juan Diego
author Cartagena, Génesis
author_facet Cartagena, Génesis
Jadán, Evelin
Guarimata, Juan Diego
author_role author
author2 Jadán, Evelin
Guarimata, Juan Diego
author2_role author
author
dc.subject.none.fl_str_mv Química
aurora kinase A
machine learning
molecular docking
Argentinian natural products
anti-cancer compounds
topic Química
aurora kinase A
machine learning
molecular docking
Argentinian natural products
anti-cancer compounds
dc.description.none.fl_txt_mv The Aurora kinase A (Aurora-A), overexpressed in cancer cells, represents a promising anti-cancer therapeutic target due to its role in mitotic progression and chromosome instability. Aurora-A contains a recently described drug pocket within its Targeting Protein for Xklp2 (TPX2) interaction site, offering a promising target for small-molecule disruption and selective inhibition. In this study, 1281 natural products from Argentina’s database (NaturAr), encompassing chemically diverse and structurally rich metabolites, were evaluated using a machine learning model based on molecular fingerprints and variational autoencoders (VAEs) to predict inhibitory activity with high-throughput efficiency. From this initial screening, 624 compounds were classified as active type against Aurora-A, and subsequently subjected to molecular docking using FRED software (v4.3.0.3) against the Aurora-A crystal structure (PDB: 5OSD), focusing on the TPX2-binding interface. Among them, 117 compounds with various scaffolds showed better binding scores than the cocrystallized ligand, highlighting their potential to interact with the druggable target site through stable and specific molecular contacts. This workflow effectively prioritized compounds of natural origin from Argentina for the discovery of new Aurora-A kinase inhibitors, demonstrating the value of integrating AI-driven screening with structurebased modeling. These findings highlight the identification of novel scaffolds with high binding potential, offering promising starting points for the development of selective Aurora-A inhibitors.
Centro de Química Inorgánica
description The Aurora kinase A (Aurora-A), overexpressed in cancer cells, represents a promising anti-cancer therapeutic target due to its role in mitotic progression and chromosome instability. Aurora-A contains a recently described drug pocket within its Targeting Protein for Xklp2 (TPX2) interaction site, offering a promising target for small-molecule disruption and selective inhibition. In this study, 1281 natural products from Argentina’s database (NaturAr), encompassing chemically diverse and structurally rich metabolites, were evaluated using a machine learning model based on molecular fingerprints and variational autoencoders (VAEs) to predict inhibitory activity with high-throughput efficiency. From this initial screening, 624 compounds were classified as active type against Aurora-A, and subsequently subjected to molecular docking using FRED software (v4.3.0.3) against the Aurora-A crystal structure (PDB: 5OSD), focusing on the TPX2-binding interface. Among them, 117 compounds with various scaffolds showed better binding scores than the cocrystallized ligand, highlighting their potential to interact with the druggable target site through stable and specific molecular contacts. This workflow effectively prioritized compounds of natural origin from Argentina for the discovery of new Aurora-A kinase inhibitors, demonstrating the value of integrating AI-driven screening with structurebased modeling. These findings highlight the identification of novel scaffolds with high binding potential, offering promising starting points for the development of selective Aurora-A inhibitors.
publishDate 2025
dc.date.none.fl_str_mv 2025-11-11
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Articulo
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/189013
url http://sedici.unlp.edu.ar/handle/10915/189013
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/issn/2673-4583
info:eu-repo/semantics/altIdentifier/doi/10.3390/ecsoc-29-26728
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
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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