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

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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https://doaj.org/article/bc653968c828438fb3828f4972325001

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