ReviewCurrent pharmaceutical design2024
Machine-learning-guided Directed Evolution for AAV Capsid Engineering.
Review in Current pharmaceutical design, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 17 citations in OpenAlex.
- AAV-based gene therapies for neovascular AMD.Gene therapy · 2026Review
- Recent Advances in the Non-viral Delivery of Genes to Central Nervous System Disorders.Cellular and molecular neurobiology · 2026Review
- Imaged Capillary Isoelectric Focusing (icIEF) Platform for Characterization of Charge Variants of Adeno-Associated Virus (AAV) Capsids and Impact on their Transduction Efficiency.Current gene therapy · 2026Article
- Viral vector-based gene therapies in the clinic: An update.Bioengineering & translational medicine · 2026Review
- Intelligent design of tumor microenvironment-responsive Adeno-associated virus vectors: overcoming delivery barriers and enabling precision therapy.Medical oncology (Northwood, London, England) · 2025Review
- Cutting-edge treatments in amyotrophic lateral sclerosis: the role of molecular pathogenesis in targeted therapies.Stem cell research & therapy · 2025Review
- Key Challenges and Optimization Practices for Non-clinical Evaluation of AAV based Gene Therapy for Central Nervous System Disease.Pharmaceutical research · 2025Review
- Identification of AAV variants with improved transduction of human vascular endothelial cells by screening AAV capsid libraries in non-human primates.Gene therapy · 2025Article
- Genomic medicine in hepatology: mechanisms and liver treatment strategies.Molecular medicine (Cambridge, Mass.) · 2025Review
- CapBuild: a cloud-native tool for adeno-associated virus capsid engineering.Nucleic acids research · 2025Article
- Revolution of AAV in Drug Discovery: From Delivery System to Clinical Application.Journal of medical virology · 2025Review
- Progress in AAV-Mediated In Vivo Gene Therapy and Its Applications in Central Nervous System Diseases.International journal of molecular sciences · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Target gene delivery is crucial to gene therapy. Adeno-associated virus (AAV) has emerged as a primary gene therapy vector due to its broad host range, long-term expression, and low pathogenicity. However, AAV vectors have some limitations, such as immunogenicity and insufficient targeting. Designing or modifying capsids is a potential method of improving the efficacy of gene delivery, but hindered by weak biological basis of AAV, complexity of the capsids, and limitations of current screening methods. Artificial intelligence (AI), especially machine learning (ML), has great potential to accelerate and improve the optimization of capsid properties as well as decrease their development time and manufacturing costs. This review introduces the traditional methods of designing AAV capsids and the general steps of building a sequence-function ML model, highlights the applications of ML in the development workflow, and summarizes its advantages and challenges.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.