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Capillary Electrophoresis Mass Spectrometry Approaches for Characterization of the Protein and Metabolite Corona Acquired by Nanomaterials

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Published on: October 27, 2020

Decoding Nanomaterial-Biosystem Interactions through Machine Learning.

Sagar Dhoble¹, Tzu-Hsien Wu¹, Kenry^1,2,3

¹Department of Pharmacology and Toxicology, R. Ken Coit College of Pharmacy, University of Arizona, Tucson, AZ 85721, USA.

Angewandte Chemie (International Ed. in English)

|April 30, 2024

Summary

This summary is machine-generated.

Machine learning decodes complex nanomaterial-biosystem interactions for advanced nanomedicine and theranostics. This approach addresses challenges and unlocks new opportunities in nanomedicine applications.

Keywords:

cells machine learning nanomaterial-biosystem interactions nanomaterials proteins

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Area of Science:

Biomaterials Science
Nanotechnology
Computational Biology

Background:

Nanomaterial-biosystem interactions are crucial for nanomedicine and theranostic applications.
These interactions are complex, influenced by nanomaterial properties, biosystem characteristics, and microenvironment factors.
Existing experimental and computational methods offer insights but leave many questions unanswered.

Purpose of the Study:

To highlight the application of machine learning in understanding nanomaterial-biosystem interactions.
To review the development and use of machine learning for decoding these complex relationships.
To provide perspectives on current challenges and future opportunities in this interdisciplinary field.

Main Methods:

Review of existing literature on nanomaterial-biosystem interactions.
Analysis of machine learning methodologies applied to this domain.
Synthesis of experimental and computational findings.

Main Results:

Machine learning offers a powerful framework to analyze the multifaceted factors governing nanomaterial-biosystem interactions.
The integration of machine learning can accelerate the discovery and optimization of nanomedicine and theranostic agents.
Key challenges include data standardization and model interpretability.

Conclusions:

Machine learning presents a timely opportunity to advance the field of nanomedicine by decoding complex nanomaterial-biosystem interactions.
Further research should focus on developing robust machine learning models and addressing data limitations.
The potential for machine learning to revolutionize theranostics and nanomedicine is significant.