Generative models for designing smart nanomaterials with controlled drug release

Abstract

Smart nanomaterials that are capable of controlled drug release have gained significant attention in recent years due to their potential to revolutionize drug delivery systems. These materials, designed to release therapeutic agents in a controlled and site- specific manner, can significantly enhance the effectiveness of treatments while minimizing adverse side effects. However, the design of such materials remains a complex and resource-intensive process. With the rise of artificial intelligence (AI) and machine learning (ML),  generative models have emerged as an innovative approach to overcome these challenges. These models utilize large datasets and computational algorithms to generate novel nanomaterial designs with optimized properties for controlled drug release. This article explores the role of generative models in nanomaterial design, particularly their potential in optimizing parameters such as particle size, surface charge, and composition, all of which are critical for regulating drug release kinetics. The integration of generative design principles with nanofabrication technologies can facilitate the creation of more efficient and personalized drug delivery systems.