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

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 com...

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Autores principales:	Cristian R. Munteanu, Pablo Gutiérrez-Asorey, Manuel Blanes-Rodríguez, Ismael Hidalgo-Delgado, María de Jesús Blanco Liverio, Brais Castiñeiras Galdo, Ana B. Porto-Pazos, Marcos Gestal, Sonia Arrasate, Humbert González-Díaz
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	decorated nanoparticles drug delivery anti-glioblastoma big data perturbation theory machine learning Biology (General) QH301-705.5 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/bc653968c828438fb3828f4972325001
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spelling	oai:doaj.org-article:bc653968c828438fb3828f49723250012021-11-11T16:58:53ZPrediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning10.3390/ijms2221115191422-00671661-6596https://doaj.org/article/bc653968c828438fb3828f49723250012021-10-01T00:00:00Zhttps://www.mdpi.com/1422-0067/22/21/11519https://doaj.org/toc/1661-6596https://doaj.org/toc/1422-0067The 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.Cristian R. MunteanuPablo Gutiérrez-AsoreyManuel Blanes-RodríguezIsmael Hidalgo-DelgadoMaría de Jesús Blanco LiverioBrais Castiñeiras GaldoAna B. Porto-PazosMarcos GestalSonia ArrasateHumbert González-DíazMDPI AGarticledecorated nanoparticlesdrug deliveryanti-glioblastomabig dataperturbation theorymachine learningBiology (General)QH301-705.5ChemistryQD1-999ENInternational Journal of Molecular Sciences, Vol 22, Iss 11519, p 11519 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	decorated nanoparticles drug delivery anti-glioblastoma big data perturbation theory machine learning Biology (General) QH301-705.5 Chemistry QD1-999
spellingShingle	decorated nanoparticles drug delivery anti-glioblastoma big data perturbation theory machine learning Biology (General) QH301-705.5 Chemistry QD1-999 Cristian R. Munteanu Pablo Gutiérrez-Asorey Manuel Blanes-Rodríguez Ismael Hidalgo-Delgado María de Jesús Blanco Liverio Brais Castiñeiras Galdo Ana B. Porto-Pazos Marcos Gestal Sonia Arrasate Humbert González-Díaz Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning
description	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.
format	article
author	Cristian R. Munteanu Pablo Gutiérrez-Asorey Manuel Blanes-Rodríguez Ismael Hidalgo-Delgado María de Jesús Blanco Liverio Brais Castiñeiras Galdo Ana B. Porto-Pazos Marcos Gestal Sonia Arrasate Humbert González-Díaz
author_facet	Cristian R. Munteanu Pablo Gutiérrez-Asorey Manuel Blanes-Rodríguez Ismael Hidalgo-Delgado María de Jesús Blanco Liverio Brais Castiñeiras Galdo Ana B. Porto-Pazos Marcos Gestal Sonia Arrasate Humbert González-Díaz
author_sort	Cristian R. Munteanu
title	Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning
title_short	Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning
title_full	Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning
title_fullStr	Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning
title_full_unstemmed	Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning
title_sort	prediction of anti-glioblastoma drug-decorated nanoparticle delivery systems using molecular descriptors and machine learning
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/bc653968c828438fb3828f4972325001
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Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning

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