--- 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 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-* pretty_name: o ---

AAVGen: Precision Engineering of Adeno-associated Virus for Renal Selective Targeting


License: Apache 2.0 Python 3.8+ Github arXive

Logo

--- ## 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).
--- ## 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.
--- ## 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).