Journal of Nanomedicine & Nanotechnology

Abstract

Utilizing artificial intelligence and computational modeling in Nanomedicine

Adesina Meredith*

The integration of artificial intelligence (AI) and computational modeling in nanomedicine has emerged as a transformative approach to address the complexities and challenges associated with developing effective nanomedical solutions. This abstract provides an overview of the diverse applications and benefits of AI and computational modeling in nanomedicine. AI techniques, including machine learning and deep learning, enable accelerated drug discovery and design by analyzing molecular structures and predicting interactions with biological targets. Additionally, AI-driven models optimize targeted drug delivery by tailoring nanocarrier properties and predicting pharmacokinetics and biodistribution. Computational modeling elucidates nanomaterial-biological interactions, guiding rational design and personalized medicine through molecular dynamics simulations, quantitative structure-activity relationship modeling, and population pharmacokinetics and pharmacodynamics modeling. While the integration of AI and computational modeling offers significant advantages, challenges such as data integration and model validation must be addressed. Future research directions include the development of hybrid models and standards for regulatory approval, with the ultimate goal of translating AI-driven discoveries into clinical practice to improve patient outcomes in nanomedicine.

Published Date: 2024-01-30; Received Date: 2024-01-02