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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Bibliographic Details
Main Authors: 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
Format: Article
Language:English
Published: MDPI AG 2021-10-01
Series:International Journal of Molecular Sciences
Subjects:
decorated nanoparticles
drug delivery
anti-glioblastoma
big data
perturbation theory
machine learning
Online Access:https://www.mdpi.com/1422-0067/22/21/11519

Internet

https://www.mdpi.com/1422-0067/22/21/11519

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