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license: apache-2.0
dataset_info:
features:
- name: final_seq
dtype: string
- name: fitness_score
dtype: float64
- name: aav_type
dtype: string
splits:
- name: AAV2_Thermostability
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num_examples: 21134
- name: AAV2_Kidney_Tropism
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- name: AAV2_production_main_merged_final
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num_examples: 322326
- name: AAV9_THLE_tr
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- name: AAV9_HepG2_bind
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num_examples: 76867
- name: AAV9_HepG2_tr
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- name: AAV9_Liver
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- name: AAV9_Production
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num_examples: 98538
- name: AAV9_THLE_bind
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num_examples: 91509
download_size: 53906832
dataset_size: 625109186
configs:
- config_name: default
data_files:
- split: AAV2_Thermostability
path: data/AAV2_Thermostability-*
- split: AAV2_Kidney_Tropism
path: data/AAV2_Kidney_Tropism-*
- split: AAV2_production_main_merged_final
path: data/AAV2_production_main_merged_final-*
- split: AAV9_THLE_tr
path: data/AAV9_THLE_tr-*
- split: AAV9_HepG2_bind
path: data/AAV9_HepG2_bind-*
- split: AAV9_HepG2_tr
path: data/AAV9_HepG2_tr-*
- split: AAV9_Liver
path: data/AAV9_Liver-*
- split: AAV9_Production
path: data/AAV9_Production-*
- split: AAV9_THLE_bind
path: data/AAV9_THLE_bind-*
pretty_name: o
---
<h1 align="center">AAVGen: Precision Engineering of Adeno-associated Virus for Renal Selective Targeting</h1>
</br>
<p align="center">
<a href="https://opensource.org/licenses/Apache-2.0">
<img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg" alt="License: Apache 2.0">
</a>
<a href="https://www.python.org/downloads/">
<img src="https://img.shields.io/badge/python-3.8+-blue.svg" alt="Python 3.8+">
</a>
<a href="https://github.com/mohammad-gh009/AAVGen">
<img src="https://img.shields.io/badge/GitHub-Code-blue.svg?logo=githu" alt="Github">
</a>
<a href="https://arxiv.org/abs/2602.18915">
<img src="https://img.shields.io/badge/arXiv-2508.18579-b31b1b.svg" alt="arXive">
</a>
</p>
<p align="center">
<img src="https://raw.githubusercontent.com/mohammad-gh009/AAVGen/main/assets/Logo.png" alt="Logo" width="500">
</p>
---
## Overview
This is the curated and processed dataset used to train **AAVGen**, a generative AI framework for de novo design of adeno-associated virus (AAV) capsids with enhanced multi-trait profiles. The dataset aggregates experimental fitness measurements for AAV2 and AAV9 capsid variants across multiple functional properties, including production efficiency, kidney tropism, and thermostability.
The dataset contains **820,993 total examples** (with repetitive sequences in different splits) across 9 splits, covering two AAV serotypes (AAV2 and AAV9).
</br>
---
## 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 thermostability. Our results demonstrate that AAVGen produces a diverse library of novel VP1 protein sequences. In silico validations revealed that the majority of the generated variants have superior performance across all three employed indices, indicating successful multi-objective optimization. Furthermore, structural analysis via AlphaFold3 confirms that the generated sequences preserve the canonical capsid folding despite sequence diversification. AAVGen establishes a foundation for data-driven viral vector engineering, accelerating the development of next-generation AAV vectors with tailored functional characteristics.
</br>
---
## Dataset Structure
### Fields
| Field | Type | Description |
|---|---|---|
| `final_seq` | `string` | VP1 capsid protein amino acid sequence |
| `fitness_score` | `float64` | Experimentally measured scores for the given assay |
| `aav_type` | `string` | AAV serotype identifier (e.g., `AAV2`, `AAV9`) |
---
### Splits
The dataset is divided into **9 splits** organized by serotype and assay type:
#### AAV2 — sourced from [Ogden et al.](https://www.science.org/doi/10.1126/science.aaw2900) and [Bryant et al.](https://www.nature.com/articles/s41587-021-00948-1)
| Split | Description |
|---|---|
| `AAV2_Thermostability` | Thermostability fitness scores for AAV2 variants |
| `AAV2_Kidney_Tropism` | Kidney tropism fitness scores for AAV2 variants |
| `AAV2_production_main_merged_final` | Production efficiency fitness scores for AAV2 variants (merged from multiple experimental replicates) |
#### AAV9 — sourced from [Eid et al.](https://www.nature.com/articles/s41587-022-01390-x)
| Split | Description |
|---|---|
| `AAV9_THLE_tr` | Transduction efficiency in THLE-2 (normal liver) cells |
| `AAV9_HepG2_bind` | Binding efficiency in HepG2 (hepatocellular carcinoma) cells |
| `AAV9_HepG2_tr` | Transduction efficiency in HepG2 cells |
| `AAV9_Liver` | In vivo liver tropism fitness scores |
| `AAV9_Production` | Production efficiency fitness scores for AAV9 variants |
| `AAV9_THLE_bind` | Binding efficiency in THLE-2 cells |
---
## Usage
```python
from datasets import load_dataset
# Load a specific split
ds = load_dataset("mohammad-gh009/AAVGen", split="AAV2_Kidney_Tropism")
# Load all splits
ds = load_dataset("mohammad-gh009/AAVGen")
print(ds)
```
---
## Source Studies
This dataset aggregates and processes data from the following published studies:
1. **Ogden et al.** — Comprehensive AAV capsid fitness landscape via deep mutational scanning. *Science*, 2019.
2. **Bryant et al.** — Deep diversification of an AAV capsid protein by machine learning. *Nature Biotechnology*, 2021.
3. **Eid et al.** — In vivo evolution of AAV capsids by massively parallel sequencing and selection. *Nature Biotechnology*, 2022.
---
## Citation
If you use this dataset, please cite the AAVGen paper:
```bibtex
@misc{ghaffarzadehesfahani2026aavgenprecisionengineeringadenoassociated,
title={AAVGen: Precision Engineering of Adeno-associated Viral Capsids for Renal Selective Targeting},
author={Mohammadreza Ghaffarzadeh-Esfahani and Yousof Gheisari},
year={2026},
eprint={2602.18915},
archivePrefix={arXiv},
primaryClass={q-bio.QM},
url={https://arxiv.org/abs/2602.18915},
}
```
---
## License
This dataset is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). |