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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.15577526 |
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Table of Contents:
- <p>The interface between nanomaterials and the immune system is a critical aspect of the success of<br>nanomedicine, particularly for applications like drug delivery and diagnostic imaging. The complexity<br>of immune-nano interactions, which are influenced by the physicochemical properties of nanoparticles<br>and the dynamic responses of immune cells, necessitates sophisticated modeling techniques. Agentbased models (ABMs) have been widely employed to simulate such interactions due to their ability to<br>represent individual agents, such as immune cells and nanoparticles, and track their interactions over<br>time. However, traditional ABMs often struggle with accurately simulating the nonlinear, highdimensional relationships that govern these complex biological processes. By integrating artificial intelligence (AI) into ABMs, it becomes possible to enhance the predictive accuracy of these models, enabling more efficient designs for nanomedicine applications. This article explores the integration of AI with agent-based models to simulate immune-nano interactions, discussing the methodology, advantages, and challenges associated with this approach</p>