ArticleInternational journal of molecular sciences2021
Prediction of Anti-Glioblastoma Drug-Decorated Nanoparticle Delivery Systems Using Molecular Descriptors and Machine Learning.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 21 citations in OpenAlex.
- Artificial Intelligence in the Design and Development of Nanoparticle Drug Delivery Systems: A Systematic Review.Advances in pharmacological and pharmaceutical sciences · 2026Review
- Computational Phenotypic Drug Discovery for Anticancer Chemotherapy: PTML Modeling of Multi-Cell Inhibitors of Colorectal Cancer Cell Lines.International journal of molecular sciences · 2025Article
- In Silico Approach for Early Antimalarial Drug Discovery: De Novo Design of Virtual Multi-Strain Antiplasmodial Inhibitors.Microorganisms · 2025Article
- Perturbation-Theory Machine Learning for Multi-Target Drug Discovery in Modern Anticancer Research.Current issues in molecular biology · 2025Review
- Artificial Intelligence-Assisted Drug and Biomarker Discovery for Glioblastoma: A Scoping Review of the Literature.Cancers · 2025Review
- Applications of Nanomedicine in Brain Tumor Therapy: Nanocarrierbased Drug Delivery Platforms, Challenges, and Perspectives.Recent patents on nanotechnology · 2025Review
- Artificial intelligence based advancements in nanomedicine for brain disorder management: an updated narrative review.Frontiers in medicine · 2025Review
- Advances and Challenges in Nano-Delivery Systems for Glioblastoma Treatment: A Comprehensive Review.International journal of nanomedicine · 2025Review
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.