Artificial Intelligence-Enhanced Nanomedicine Design and Deep Reinforcement Learning in Pharmacokinetics

Abstract

This chapter explores the potential of Artificial Intelligence (AI) in nanomedicine design, focusing on Deep Reinforcement Learning (DRL) for optimizing pharmacokinetics. Nanomedicine uses nanoscale materials for disease diagnosis, treatment, and monitoring, presenting unique challenges and opportunities due to biological system complexity. Deep Learning (DRL) principles are used to design nanoparticles with optimal properties for targeted drug delivery and controlled release. AI is practically applied in nanomedicine design, including AI-driven platforms for predicting biodistribution, metabolism, and clearance. The chapter also discusses the integration of DRL with other AI techniques and ethical considerations, emphasizing transparency, reproducibility, and collaboration between AI experts, clinicians, and regulatory bodies.