# 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 f…
