README.md

---
license: apache-2.0
pretty_name: AAVGen
task_categories:
- text-classification
- text-generation
dataset_info:
  features:
  - name: final_seq
    dtype: string
  - name: fitness_score
    dtype: float64
  - name: aav_type
    dtype: string
  splits:
  - name: AAV2_Thermostability
    num_bytes: 15965239
    num_examples: 21134
  - name: AAV2_Kidney_Tropism
    num_bytes: 18874049
    num_examples: 24984
  - name: AAV2_production_main_merged_final
    num_bytes: 244970433
    num_examples: 322326
  - name: AAV9_THLE_tr
    num_bytes: 41275248
    num_examples: 54096
  - name: AAV9_HepG2_bind
    num_bytes: 58649521
    num_examples: 76867
  - name: AAV9_HepG2_tr
    num_bytes: 25121012
    num_examples: 32924
  - name: AAV9_Liver
    num_bytes: 75247823
    num_examples: 98621
  - name: AAV9_Production
    num_bytes: 75184494
    num_examples: 98538
  - name: AAV9_THLE_bind
    num_bytes: 69821367
    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-*
---

<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://github.com/mohammad-gh009/AAVGen">
    <img src="https://img.shields.io/badge/GitHub-Code-blue.svg?logo=github" alt="Github">
  </a>
  <a href="https://huggingface.co/papers/2602.18915">
    <img src="https://img.shields.io/badge/arXiv-2602.18915-b31b1b.svg" alt="Paper">
  </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** across 9 splits, covering two AAV serotypes (AAV2 and AAV9).

The model and findings were presented in the paper [AAVGen: Precision Engineering of Adeno-associated Viral Capsids for Renal Selective Targeting](https://huggingface.co/papers/2602.18915).

</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. 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.

</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 |

#### 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).