Update README.md

README.md

@@ -3,4 +3,30 @@ license: mit
 tags:
 - medical
 - drug-discovery
----
+---
+# AetherCell: Pre-trained Weights
+
+This repository contains the official **pre-trained weights** (model checkpoints) for **AetherCell**, a generative foundation model for virtual cell perturbation and in vivo drug discovery.
+
+### 📄 Project Links
+- **Code Repository**: [GitHub - AetherCell](https://github.com/Wenyuan-AI4science/AetherCell) (Primary entry point for usage)
+- **Training Data**: [Zenodo Record](https://zenodo.org/records/18295255)
+- **Paper**: *(Coming Soon)*
+
+## 💡 Model Description
+AetherCell is a deep generative foundation model that unifies context-rich clinical RNA-seq with perturbation-dense L1000 assays. It utilizes a **Satellite-Backbone VAE architecture** to:
+1.  Construct a unified transcriptomic manifold from 519,609 RNA-seq samples.
+2.  Predict mechanism-specific cellular responses to chemical and genetic perturbations.
+3.  Support downstream tasks like drug sensitivity prediction (IC50) and virtual screening.
+
+## 📂 Files Included
+* **Backbone VAE**: Weights for the global RNA-seq manifold.
+* **Satellite VAE**: Weights for the L1000 platform anchoring.
+* **Generative Modules**: Conditioned perturbation predictors.
+
+## 📚 Citation
+If you use this model in your research, please cite our work:
+*(Pending publication details)*
+---
+
+*For questions or issues, please open an issue on our [GitHub Repository](https://github.com/Wenyuan-AI4science/AetherCell).*