Add task categories, paper link and improve metadata

README.md

@@ -1,5 +1,9 @@
 ---
 license: apache-2.0
+pretty_name: AAVGen
+task_categories:
+- text-classification
+- text-generation
 dataset_info:
   features:
   - name: final_seq
@@ -59,7 +63,6 @@ configs:
     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>
@@ -70,14 +73,11 @@ pretty_name: o
   <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">
+    <img src="https://img.shields.io/badge/GitHub-Code-blue.svg?logo=github" 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 href="https://huggingface.co/papers/2602.18915">
+    <img src="https://img.shields.io/badge/arXiv-2602.18915-b31b1b.svg" alt="Paper">
   </a>
 </p>
 
@@ -91,7 +91,9 @@ pretty_name: o
 
 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).
+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>
 
@@ -99,7 +101,7 @@ The dataset contains **820,993 total examples** (with repetitive sequences in di
 
 ## 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.
+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>
 
@@ -128,7 +130,7 @@ The dataset is divided into **9 splits** organized by serotype and assay type:
 |---|---|
 | `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) |
+| `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)
 
@@ -180,7 +182,6 @@ If you use this dataset, please cite the AAVGen paper:
       primaryClass={q-bio.QM},
       url={https://arxiv.org/abs/2602.18915}, 
 }
-
 ```
 
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