Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning
dc.contributor.author | Munteanu, Cristian R. | |
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 | Castiñeiras Galdo, Brais | |
dc.contributor.author | Porto-Pazos, Ana B. | |
dc.contributor.author | Gestal, Marcos | |
dc.contributor.author | Arrasate Gil, Sonia | |
dc.contributor.author | González Díaz, Humberto | |
dc.date.accessioned | 2021-11-24T12:58:21Z | |
dc.date.available | 2021-11-24T12:58:21Z | |
dc.date.issued | 2021-10-26 | |
dc.identifier.citation | International Journal of Molecular Sciences 22(21) : (2021) // Article ID 11519 | es_ES |
dc.identifier.issn | 1422-0067, | |
dc.identifier.uri | http://hdl.handle.net/10810/54063 | |
dc.description.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. View Full-Text | 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). | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | |
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.date.updated | 2021-11-11T14:57:25Z | |
dc.rights.holder | 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | es_ES |
dc.relation.publisherversion | https://www.mdpi.com/1422-0067/22/21/11519/htm | es_ES |
dc.identifier.doi | 10.3390/ijms222111519 | |
dc.departamentoes | Química Orgánica e Inorgánica | |
dc.departamentoeu | Kimika Organikoa eta Ez-Organikoa |
Files in this item
This item appears in the following Collection(s)
Except where otherwise noted, this item's license is described as 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).