Location
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
Document Type
Event
Start Date
22-4-2026 4:00 PM
Description
The challenge of predicting nanoparticle distribution remain a significant hurdle in nanomedicine. This research presents a computational framework for the inverse design of nanoparticles, utilizing ML models to optimize drug delivery systems for tumor targeting. By analyzing the relationship between nanoparticle compositions and biological accumulation, the model identifies optimal configurations to maximize therapeutic efficacy. The results demonstrate that AI-driven inverse design can significantly streamline the development of precision nanocarriers, reducing the need for exhaustive experimental trials.
Included in
GRM-157-180 Precision Engineering: Using AI to Design Nanoparticles that Target Malignant Cells
https://www.kennesaw.edu/ccse/events/computing-showcase/sp26-cday-program.php
The challenge of predicting nanoparticle distribution remain a significant hurdle in nanomedicine. This research presents a computational framework for the inverse design of nanoparticles, utilizing ML models to optimize drug delivery systems for tumor targeting. By analyzing the relationship between nanoparticle compositions and biological accumulation, the model identifies optimal configurations to maximize therapeutic efficacy. The results demonstrate that AI-driven inverse design can significantly streamline the development of precision nanocarriers, reducing the need for exhaustive experimental trials.