AIdsorb is a Python package for deep learning on molecular point clouds.
This package aims to provide a simple, easy-to-use and reproduce interface for:
-
📥 Creating molecular point clouds
-
🤖 Training DL algorithms on molecular point clouds
Important
It is strongly recommended to perform the installation inside a virtual environment.
Assuming an activated virtual environment:
pip install aidsorb
Note
Refer to the 📚 Documentation for more information.
Here is a summary of what you can do from the command line:
-
Visualize a molecular point cloud:
aidsorb visualize path/to/structure
-
Create and prepare point clouds:
aidsorb create path/to/structures path/to/pcd_data # Create and store point clouds aidsorb prepare path/to/pcd_data # Split point clouds to train, valdation and test
-
Train and test a model:
aidsorb-lit fit --config=path/to/config.yaml aidsorb-lit test --config=path/to/config.yaml --ckpt_path=path/to/ckpt
🙌 We welcome contributions from the community to help improve and expand this project!
You can start by 🛠️ opening an issue for:
- 🐛 Reporting bugs
- 🌟 Suggesting new features
- 📚 Improving documentation
- 🎨 Adding your example to the Gallery
We appreciate your efforts to submit well-documented 🔃 pull requests and participate in discussions.
💪 Together, we can make this project even better!
- To cite the software, please refer to the citation file or click the citation button.
- To cite the paper, please use the following BibTeX entry:
Show BibTex entry
@article{Sarikas2024,
title = {Gas adsorption meets geometric deep learning: points, set and match},
volume = {14},
ISSN = {2045-2322},
url = {http://dx.doi.org/10.1038/s41598-024-76319-8},
DOI = {10.1038/s41598-024-76319-8},
number = {1},
journal = {Scientific Reports},
publisher = {Springer Science and Business Media LLC},
author = {Sarikas, Antonios P. and Gkagkas, Konstantinos and Froudakis, George E.},
year = {2024},
month = nov
}
AIdosrb is released under the GNU General Public License v3.0 only.