![[2602.18915] AAVGen: Precision Engineering of Adeno-associated Viral Capsids for Renal Selective Targeting](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.18915] AAVGen: Precision Engineering of Adeno-associated Viral Capsids for Renal Selective Targeting
Summary
The article presents AAVGen, a generative AI framework designed for the precise engineering of adeno-associated viral capsids, enhancing their targeting capabilities for renal applications.
Why It Matters
This research addresses significant challenges in gene therapy, particularly the limitations of existing viral vectors. By employing advanced AI techniques, AAVGen could lead to more effective treatments for kidney diseases, showcasing the intersection of AI and biotechnology.
Key Takeaways
- AAVGen utilizes a generative AI framework to design viral capsids.
- The model optimizes multiple properties, including kidney tropism and production fitness.
- In silico validations indicate superior performance of generated variants.
- Structural analysis confirms the preservation of capsid folding despite sequence changes.
- AAVGen sets a foundation for future advancements in viral vector engineering.
Quantitative Biology > Quantitative Methods arXiv:2602.18915 (q-bio) [Submitted on 21 Feb 2026] Title:AAVGen: Precision Engineering of Adeno-associated Viral Capsids for Renal Selective Targeting Authors:Mohammadreza Ghaffarzadeh-Esfahani, Yousof Gheisari View a PDF of the paper titled AAVGen: Precision Engineering of Adeno-associated Viral Capsids for Renal Selective Targeting, by Mohammadreza Ghaffarzadeh-Esfahani and 1 other authors View PDF HTML (experimental) Abstract:Adeno-associated viruses (AAVs) are promising vectors for gene therapy, but their native serotypes face limitations in tissue tropism, immune evasion, and production efficiency. Engineering capsids to overcome these hurdles is challenging due to the vast sequence space and the difficulty of simultaneously optimizing multiple functional properties. The complexity also adds when it comes to the kidney, which presents unique anatomical barriers and cellular targets that require precise and efficient vector engineering. Here, we present AAVGen, a generative artificial intelligence framework for de novo design of AAV capsids with enhanced multi-trait profiles. AAVGen integrates a protein language model (PLM) with supervised fine-tuning (SFT) and a reinforcement learning technique termed Group Sequence Policy Optimization (GSPO). The model is guided by a composite reward signal derived from three ESM-2-based regression predictors, each trained to predict a key property: production fitness, kidney tropism, and ...
