AetherCell

AetherCell is a generative framework for virtual cell perturbation and drug discovery from transcriptomic data.

This Hugging Face repository provides the packaged model weights and the accompanying Python API for local inference.
For full documentation, workflows, benchmarks, and project updates, please refer to the main GitHub repository.

Supported tasks

Virtual perturbation for drug and gene perturbations
Drug response prediction for cancer cell lines
Drug repurposing for disease-oriented candidate ranking

Repository purpose

Use this repository to:

download AetherCell model assets
access the Python API for local workflows
load the weights required by the main AetherCell codebase

Getting started

Please follow the installation and usage instructions in the GitHub repository.
The GitHub repo contains the latest environment setup, inference examples, and workflow entry points.

Responsible use

AetherCell is intended for research use only.
It is not intended for clinical diagnosis, patient stratification, or treatment decision-making.
Any predictions or hypotheses generated by the system should be independently validated.

Citation

If you use AetherCell in your research, please cite the associated manuscript or repository.
Formal citation details will be added after publication.

For questions or issues, please open an issue on the GitHub repository.

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