Mostrar o rexistro simple do ítem
Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning
dc.contributor.author | Munteanu, Cristian-Robert | |
dc.contributor.author | Gutiérrez-Asorey, Pablo | |
dc.contributor.author | Blanes-Rodríguez, Manuel | |
dc.contributor.author | Hidalgo-Delgado, Ismael | |
dc.contributor.author | Blanco Liverio, María de Jesús | |
dc.contributor.author | Galdo, Brais | |
dc.contributor.author | Porto-Pazos, Ana B. | |
dc.contributor.author | Gestal, M. | |
dc.contributor.author | Arrasate, Sonia | |
dc.contributor.author | González-Díaz, Humberto | |
dc.date.accessioned | 2022-01-24T18:14:02Z | |
dc.date.available | 2022-01-24T18:14:02Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Munteanu, C.R.; Gutiérrez-Asorey, P.; Blanes-Rodríguez, M.; Hidalgo-Delgado, I.; Blanco Liverio, M.d.J.; Castiñeiras Galdo, B.; Porto-Pazos, A.B.; Gestal, M.; Arrasate, S.; González-Díaz, H. Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning. Int. J. Mol. Sci. 2021, 22, 11519. https://doi.org/10.3390/ijms222111519 | es_ES |
dc.identifier.uri | http://hdl.handle.net/2183/29480 | |
dc.description | This article belongs to the Special Issue Nanoformulations and Nano Drug Delivery | es_ES |
dc.description.abstract | [Abstract] The theoretical prediction of drug-decorated nanoparticles (DDNPs) has become a very important task in medical applications. For the current paper, Perturbation Theory Machine Learning (PTML) models were built to predict the probability of different pairs of drugs and nanoparticles creating DDNP complexes with anti-glioblastoma activity. PTML models use the perturbations of molecular descriptors of drugs and nanoparticles as inputs in experimental conditions. The raw dataset was obtained by mixing the nanoparticle experimental data with drug assays from the ChEMBL database. Ten types of machine learning methods have been tested. Only 41 features have been selected for 855,129 drug-nanoparticle complexes. The best model was obtained with the Bagging classifier, an ensemble meta-estimator based on 20 decision trees, with an area under the receiver operating characteristic curve (AUROC) of 0.96, and an accuracy of 87% (test subset). This model could be useful for the virtual screening of nanoparticle-drug complexes in glioblastoma. All the calculations can be reproduced with the datasets and python scripts, which are freely available as a GitHub repository from authors. | es_ES |
dc.description.sponsorship | The APC was funded by IKERDATA, S.L. under grant 3/12/DP/2021/00102—Area 1: Development of innovative business projects, from Provincial Council of Vizcaya (BEAZ for the Creation of Innovative Business Innovative business ventures). This work is supported by the “Collaborative Project in Genomic Data Integration (CICLOGEN)” PI17/01826 funded by the Carlos III Health Institute, from the Spanish National Plan for Scientific and Technical Research and Innovation 2013–2016 and the European Regional Development Funds (FEDER)—“A way to build Europe”. This project was also supported by the General Directorate of Culture, Education and University Management of Xunta de Galicia (Ref. ED431G/01, ED431D 2017/16), the “Galician Network for Colorectal Cancer Research” (Ref. ED431D 2017/23), Competitive Reference Groups (Ref. ED431C 2018/49), and the Spanish Ministry of Economy and Competitiveness via funding of the unique installation BIOCAI (UNLC08-1E-002, UNLC13-13-3503) and the European Regional Development Funds (FEDER). Lastly, the authors also acknowledge research grants from the Ministry of Economy and Competitiveness, MINECO, Spain (FEDER CTQ2016-74881-P), the Basque government (IT1045-16), and the kind support of Ikerbasque, Basque Foundation for Science and Zitek | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431G/01 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431D 2017/16 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431D 2017/23 | es_ES |
dc.description.sponsorship | Xunta de Galicia; ED431C 2018/49 | es_ES |
dc.description.sponsorship | Eusko Jaurlaritza = Gobierno Vasco; IT1045-16 | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation | info:eu-repo/grantAgreement/ISCIII/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013–2016/PI17%2F01826/ES/PROYECTO COLABORATIVO DE INTEGRACION DE DATOS GENOMICOS (CICLOGEN). TECNICAS DE DATA MINING Y DOCKING MOLECULAR PARA ANALISIS DE DATOS INTEGRATIVOS EN CANCER DE COLON/ | |
dc.relation | info:eu-repo/grantAgreement/MEC/Plan Nacional de I+D+i 2008-2011/UNLC08-1E-002/ES/Infraestructura computacional para la Red Gallega de Bioinformática/ | |
dc.relation | info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/UNLC13-1E-2503/ES/Plataforma HPC-PLUS para aplicaciones biomédicas/ | |
dc.relation | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/CTQ2016-74881-P/ES/REACCIONES DE ACTIVACION C-H CATALIZADAS POR METALES DE TRANSICION EN SINTESIS Y FUNCIONALIZACION DE HETEROCICLOS. NUEVAS APLICACIONES SINTETICAS Y MODELOS COMPUTACIONALES/ | |
dc.relation.uri | https://doi.org/10.3390/ijms222111519 | es_ES |
dc.rights | Atribución 4.0 Internacional | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Decorated nanoparticles | es_ES |
dc.subject | Drug delivery | es_ES |
dc.subject | Anti-glioblastoma | es_ES |
dc.subject | Big data | es_ES |
dc.subject | Perturbation theory | es_ES |
dc.subject | Machine learning | es_ES |
dc.subject | ChEMBL database | es_ES |
dc.title | Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.access | info:eu-repo/semantics/openAccess | es_ES |
UDC.journalTitle | International Journal of Molecular Sciences | es_ES |
UDC.volume | 22 | es_ES |
UDC.issue | 21 | es_ES |
UDC.startPage | 11519 | es_ES |
dc.identifier.doi | 10.3390/ijms222111519 |
Ficheiros no ítem
Este ítem aparece na(s) seguinte(s) colección(s)
-
GI-RNASA - Artigos [196]