Prediction of Nanoparticle Sizes for Arbitrary Methacrylates Using Artificial Neuronal Networks

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.

• Publication year

  2021

• Venue

  Advancement of science

• Publication date

  2021-10-23

• Fields of study

  Medicine, Materials Science, Engineering

• Identifiers
  DOI 10.1002/advs.202102429PMID 34687160PMCID 8655218
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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