Artificial Intelligence-Based Approaches for AAV Vector Engineering

Abstract

Adeno-associated virus (AAV) has emerged as a leading vector for gene therapy due to its broad host range, low pathogenicity, and ability to facilitate long-term gene expression. However, AAV vectors face limitations, including immunogenicity and insufficient targeting specificity. To enhance the efficacy of gene therapy, researchers have been modifying the AAV vector using various methods. Traditional experimental approaches for optimizing AAV vector are often time-consuming, resource-intensive, and difficult to replicate. The advancement of artificial intelligence (AI), particularly machine learning, offers significant potential to accelerate capsid optimization while reducing development time and manufacturing costs. This review compares traditional and AI-based methods of AAV vector engineering and highlights recent research in AAV engineering using AI algorithms.