Machine Learning Approach for Predicting Drug-Like Molecules Targeting Calmodulin Pathway Proteins

Recently, numerous models have been developed to predict drug interactions with molecules. However, integrating diverse data sources and improving the accuracy of biological activity predictions remains a challenge. This work proposes a novel solution that addresses these limitations. Here, we have...

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Autores: Baltasar-Marchueta, Maider, López, Naia, Alicante Martínez, Sara, Barbolla, Iratxe, García Ibarluzea, Markel, Ramis, Rafael, Salomon, Ane Miren, Muguruza-Montero, Arantza, Núñez, Eider, Leonardo, Aritz, Arrasate, Sonia, Sotomayor, Nuria, Montemore, Matthew M., Villarroel, Álvaro, Bergara, Aitor, Lete, Esther, González-Díaz, Humberto
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/410349
Acceso en línea:http://hdl.handle.net/10261/410349
https://api.elsevier.com/content/abstract/scopus_id/105021087379
Access Level:acceso abierto
Palabra clave:Assays
Mathematical methods
Peptides and proteins
Reaction Products
Screening Assays
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network_acronym_str ES
network_name_str España
repository_id_str
dc.title.none.fl_str_mv Machine Learning Approach for Predicting Drug-Like Molecules Targeting Calmodulin Pathway Proteins
title Machine Learning Approach for Predicting Drug-Like Molecules Targeting Calmodulin Pathway Proteins
spellingShingle Machine Learning Approach for Predicting Drug-Like Molecules Targeting Calmodulin Pathway Proteins
Baltasar-Marchueta, Maider
Assays
Mathematical methods
Peptides and proteins
Reaction Products
Screening Assays
title_short Machine Learning Approach for Predicting Drug-Like Molecules Targeting Calmodulin Pathway Proteins
title_full Machine Learning Approach for Predicting Drug-Like Molecules Targeting Calmodulin Pathway Proteins
title_fullStr Machine Learning Approach for Predicting Drug-Like Molecules Targeting Calmodulin Pathway Proteins
title_full_unstemmed Machine Learning Approach for Predicting Drug-Like Molecules Targeting Calmodulin Pathway Proteins
title_sort Machine Learning Approach for Predicting Drug-Like Molecules Targeting Calmodulin Pathway Proteins
dc.creator.none.fl_str_mv Baltasar-Marchueta, Maider
López, Naia
Alicante Martínez, Sara
Barbolla, Iratxe
García Ibarluzea, Markel
Ramis, Rafael
Salomon, Ane Miren
Muguruza-Montero, Arantza
Núñez, Eider
Leonardo, Aritz
Arrasate, Sonia
Sotomayor, Nuria
Montemore, Matthew M.
Villarroel, Álvaro
Bergara, Aitor
Lete, Esther
González-Díaz, Humberto
author Baltasar-Marchueta, Maider
author_facet Baltasar-Marchueta, Maider
López, Naia
Alicante Martínez, Sara
Barbolla, Iratxe
García Ibarluzea, Markel
Ramis, Rafael
Salomon, Ane Miren
Muguruza-Montero, Arantza
Núñez, Eider
Leonardo, Aritz
Arrasate, Sonia
Sotomayor, Nuria
Montemore, Matthew M.
Villarroel, Álvaro
Bergara, Aitor
Lete, Esther
González-Díaz, Humberto
author_role author
author2 López, Naia
Alicante Martínez, Sara
Barbolla, Iratxe
García Ibarluzea, Markel
Ramis, Rafael
Salomon, Ane Miren
Muguruza-Montero, Arantza
Núñez, Eider
Leonardo, Aritz
Arrasate, Sonia
Sotomayor, Nuria
Montemore, Matthew M.
Villarroel, Álvaro
Bergara, Aitor
Lete, Esther
González-Díaz, Humberto
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Eusko Jaurlaritza
Ministerio de Ciencia e Innovación (España)
Universidad del País Vasco
Agencia Estatal de Investigación (España)
European Commission
National Science Foundation (US)
Baltasar-Marchueta, Maider [0000-0002-4833-4267]
Alicante Martínez, Sara [0000-0002-3960-8836]
Barbolla, Iratxe [0000-0002-4124-7697]
Ramis, Rafael [0000-0002-4435-8990]
Muguruza-Montero [0000-0001-8713-4949]
Núñez, Eider [0000-0001-8075-9815]
Montemore, Matthew M. [0000-0002-4157-1745]
Villarroel, Álvaro [0000-0003-1096-7824]
Bergara, Aitor [0000-0003-2707-1856]
González-Díaz, Humberto [0000-0002-9392-2797]
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Assays
Mathematical methods
Peptides and proteins
Reaction Products
Screening Assays
topic Assays
Mathematical methods
Peptides and proteins
Reaction Products
Screening Assays
description Recently, numerous models have been developed to predict drug interactions with molecules. However, integrating diverse data sources and improving the accuracy of biological activity predictions remains a challenge. This work proposes a novel solution that addresses these limitations. Here, we have developed a machine learning model to predict the efficacy of different assays and drugs for diseases related to calmodulin. To achieve this, we have compiled a comprehensive data set including commercialized drugs and experimental compounds targeting CaM complexes. The IFPTML-XGB model achieved high predictive performance, with a test accuracy of 89.1% and a sensitivity of 89.0%, demonstrating its robustness for assay efficacy prediction. We have used the IFPTML modeling technique to identify key factors influencing these activities. We have also synthesized novel riluzole derivatives and have tested them both experimentally and computationally. Biological assays and molecular docking studies have been performed to provide a molecular-scale picture of the molecule–CaM interaction. To validate the model’s utility, we tested it on these derivatives. We have found that the model correctly predicts which derivatives were the most bioactive, indicating that this framework can be used to identify promising candidates for new drug formulations. This research not only improves our understanding of CaM-related diseases, but also provides an effective framework for developing new treatments based on predictive modeling.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/410349
https://api.elsevier.com/content/abstract/scopus_id/105021087379
url http://hdl.handle.net/10261/410349
https://api.elsevier.com/content/abstract/scopus_id/105021087379
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-128286NB-I00
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-137365NB-I00
The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1021/acs.jcim.5c02111
Baltasar-Marchueta, Maider; López, Naia; Alicante Martínez, Sara; Barbolla, Iratxe; García Ibarluzea, Markel; Ramis, Rafael; Salomon, Ane Miren; Muguruza-Montero, Arantza; Núñez, Eider; Leonardo, Aritz; Arrasate, Sonia; Sotomayor, Nuria; Montemore, Matthew M.; Villarroel, Álvaro; Bergara, Aitor; Lete, Esther; González-Díaz, Humberto; maiderbaltasar/CaMIFPTML: CaM IFPTML model development [Software]; Zenodo; v5; https://doi.org/10.5281/zenodo.15423309; http://hdl.handle.net/10261/410349
https://doi.org/10.1021/acs.jcim.5c02111
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv American Chemical Society
publisher.none.fl_str_mv American Chemical Society
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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spelling Machine Learning Approach for Predicting Drug-Like Molecules Targeting Calmodulin Pathway ProteinsBaltasar-Marchueta, MaiderLópez, NaiaAlicante Martínez, SaraBarbolla, IratxeGarcía Ibarluzea, MarkelRamis, RafaelSalomon, Ane MirenMuguruza-Montero, ArantzaNúñez, EiderLeonardo, AritzArrasate, SoniaSotomayor, NuriaMontemore, Matthew M.Villarroel, ÁlvaroBergara, AitorLete, EstherGonzález-Díaz, HumbertoAssaysMathematical methodsPeptides and proteinsReaction ProductsScreening AssaysRecently, numerous models have been developed to predict drug interactions with molecules. However, integrating diverse data sources and improving the accuracy of biological activity predictions remains a challenge. This work proposes a novel solution that addresses these limitations. Here, we have developed a machine learning model to predict the efficacy of different assays and drugs for diseases related to calmodulin. To achieve this, we have compiled a comprehensive data set including commercialized drugs and experimental compounds targeting CaM complexes. The IFPTML-XGB model achieved high predictive performance, with a test accuracy of 89.1% and a sensitivity of 89.0%, demonstrating its robustness for assay efficacy prediction. We have used the IFPTML modeling technique to identify key factors influencing these activities. We have also synthesized novel riluzole derivatives and have tested them both experimentally and computationally. Biological assays and molecular docking studies have been performed to provide a molecular-scale picture of the molecule–CaM interaction. To validate the model’s utility, we tested it on these derivatives. We have found that the model correctly predicts which derivatives were the most bioactive, indicating that this framework can be used to identify promising candidates for new drug formulations. This research not only improves our understanding of CaM-related diseases, but also provides an effective framework for developing new treatments based on predictive modeling.Basque Government/Eusko Jaurlaritza (IT1558-22) and SPRI ELKARTEK grant (CardiCaM KK-2020/00110) are acknowledged for financial support. We also acknowledge Ministry of Science and Innovation (PID2021-128286NB-100, PID2022-137365NB-100 funded by MCIN/AEI/10.13039/501100011033). Technical and human support provided by Servicios Generales de Investigación SGIker (UPV/EHU, MINECO, GV/EJ, ERDF and ESF) is also acknowledged. MBM acknowledges the support of the Basque Government through the predoctoral grant (Programa Predoctoral de Formación de Personal Investigador, reference number PRE_2024_1_0387). M.M.M. acknowledges the National Science Foundation through grant CHE-2154952.Peer reviewedAmerican Chemical SocietyEusko JaurlaritzaMinisterio de Ciencia e Innovación (España)Universidad del País VascoAgencia Estatal de Investigación (España)European CommissionNational Science Foundation (US)Baltasar-Marchueta, Maider [0000-0002-4833-4267]Alicante Martínez, Sara [0000-0002-3960-8836]Barbolla, Iratxe [0000-0002-4124-7697]Ramis, Rafael [0000-0002-4435-8990]Muguruza-Montero [0000-0001-8713-4949]Núñez, Eider [0000-0001-8075-9815]Montemore, Matthew M. [0000-0002-4157-1745]Villarroel, Álvaro [0000-0003-1096-7824]Bergara, Aitor [0000-0003-2707-1856]González-Díaz, Humberto [0000-0002-9392-2797]Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/410349https://api.elsevier.com/content/abstract/scopus_id/105021087379reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-128286NB-I00info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2022-137365NB-I00The underlying dataset has been published as supplementary material of the article in the publisher platform at DOI https://doi.org/10.1021/acs.jcim.5c02111Baltasar-Marchueta, Maider; López, Naia; Alicante Martínez, Sara; Barbolla, Iratxe; García Ibarluzea, Markel; Ramis, Rafael; Salomon, Ane Miren; Muguruza-Montero, Arantza; Núñez, Eider; Leonardo, Aritz; Arrasate, Sonia; Sotomayor, Nuria; Montemore, Matthew M.; Villarroel, Álvaro; Bergara, Aitor; Lete, Esther; González-Díaz, Humberto; maiderbaltasar/CaMIFPTML: CaM IFPTML model development [Software]; Zenodo; v5; https://doi.org/10.5281/zenodo.15423309; http://hdl.handle.net/10261/410349https://doi.org/10.1021/acs.jcim.5c02111Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/4103492026-05-22T06:33:51Z
score 15,811543

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