--- 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-* ---

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


License: Apache 2.0 Github Paper

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