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Article

Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning

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Computer Science Faculty, University of A Coruna, 15071 A Coruña, Spain
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Centro de Investigación en Tecnologías de la Información y Las Comunicaciones (CITIC), Campus de Elviña s/n, 15071 A Coruña, Spain
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Biomedical Research Institute of A Coruña (INIBIC), University Hospital Complex of A Coruña (CHUAC), 15006 A Coruña, Spain
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IKERDATA S.L., ZITEK, University of Basque Country UPVEHU, Rectorate Building, 48940 Leioa, Spain
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Department of Organic and Inorganic Chemistry, University of the Basque Country (UPV/EHU), 48940 Leioa, Spain
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IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain
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BIOFISIKA, Basque Center for Biophysics, University of Basque Country UPVEHU, 48940 Leioa, Spain
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Authors to whom correspondence should be addressed.
Academic Editor: Sanoj Rejinold
Int. J. Mol. Sci. 2021, 22(21), 11519; https://doi.org/10.3390/ijms222111519
Received: 14 September 2021 / Revised: 8 October 2021 / Accepted: 22 October 2021 / Published: 26 October 2021
(This article belongs to the Special Issue Nanoformulations and Nano Drug Delivery)
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
Keywords: decorated nanoparticles; drug delivery; anti-glioblastoma; big data; perturbation theory; machine learning; ChEMBL database decorated nanoparticles; drug delivery; anti-glioblastoma; big data; perturbation theory; machine learning; ChEMBL database
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MDPI and ACS Style

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

AMA Style

Munteanu CR, Gutiérrez-Asorey P, Blanes-Rodríguez M, Hidalgo-Delgado I, Blanco Liverio MdJ, Castiñeiras Galdo B, Porto-Pazos AB, Gestal M, Arrasate S, González-Díaz H. Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning. International Journal of Molecular Sciences. 2021; 22(21):11519. https://doi.org/10.3390/ijms222111519

Chicago/Turabian Style

Munteanu, Cristian R., Pablo Gutiérrez-Asorey, Manuel Blanes-Rodríguez, Ismael Hidalgo-Delgado, María d.J. Blanco Liverio, Brais Castiñeiras Galdo, Ana B. Porto-Pazos, Marcos Gestal, Sonia Arrasate, and Humbert González-Díaz. 2021. "Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning" International Journal of Molecular Sciences 22, no. 21: 11519. https://doi.org/10.3390/ijms222111519

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