A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Artificial Intelligence‐Based Approaches for AAV Vector Engineering
https://orcid.org/0000-0002-3885-543X
AbstractAdeno‐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.
Artificial Intelligence‐Based Approaches for AAV Vector Engineering
https://orcid.org/0000-0002-3885-543X
AbstractAdeno‐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.
Artificial Intelligence‐Based Approaches for AAV Vector Engineering
https://orcid.org/0000-0002-3885-543X
AbstractAdeno‐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.
Artificial Intelligence‐Based Approaches for AAV Vector Engineering
Advanced Science
Tan, Fangzhi (author) / Dong, Yue (author) / Qi, Jieyu (author) / Yu, Wenwu (author) / Chai, Renjie (author)
Advanced Science ; 12
2025-03-01
Article (Journal)
Electronic Resource
English
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