ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2021
Prediction of Nanoparticle Sizes for Arbitrary Methacrylates Using Artificial Neuronal Networks.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Machine learning reshapes the paradigm of nanomedicine research.Acta pharmaceutica Sinica. B · 2026Review
- Structure-Aware Machine Learning for Polymers: A Hierarchical Graph Network for Predicting Properties From Statistical Ensembles.Macromolecular rapid communications · 2026Article
- Graph Neural Networks for Polymer Characterization and Property Prediction: Opportunities and Challenges.Journal of chemical information and modeling · 2026Review
- Machine learning empowered formulation design, optimization and characterization of nanoparticulate drug delivery systems: Current applications, challenges, and future perspectives.Acta pharmaceutica Sinica. B · 2026Review
- Machine Learning Enabled Image Analysis of Time-Temperature Sensing Colloidal Arrays.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Improvement of High-Throughput Experimentation Using Synthesis Robots by the Implementation of Tailor-Made Sensors.Polymers · 2022Article
- Prediction of Nanoparticle Sizes for Arbitrary Methacrylates Using Artificial Neuronal Networks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Particle sizes represent one of the key factors influencing the usability and specific targeting of nanoparticles in medical applications such as vectors for drug or gene therapy. A multi-layered graph convolutional network combined with a fully connected neuronal network is presented for the prediction of the size of nanoparticles based only on the polymer structure, the degree of polymerization, and the formulation parameters. The model is capable of predicting particle sizes obtained by nanoprecipitation of different poly(methacrylates). This includes polymers the network has not been trained with, indicating the high potential for generalizability of the model. By utilizing this model, a significant amount of time and resources can be saved in formulation optimization without extensive primary testing of material properties.
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What OpenQuestion holds
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.