model_cards/article.md

Model documentation & parameters

GeoDiff prompt: Here you can upload a .pkl file with the necessary variables to initialize a GeoDiff generation. Our example file contains five example configurations. NOTE: For details on how to create such files for your custom data, see original paper and this Colab

Prompt ID: Which of the five example configurations to be used. If you use your own file and have the files in a flat dictionary, leave this blank. If your own file should contain multiple examples, create a top-level dictionary with keys as ascending integers and values as example dictionaries.

Number of samples: How many samples should be generated (between 1 and 50).

Model card -- GeoDiff

Model Details: GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation

Developers: Minkai Xu and colleagues from MILA and Stanford University.

Distributors: GT4SD Developers.

Model date: 2022.

Model version: Checkpoints provided by original authors (see their GitHub repo).

Model type: A Geometric Diffusion Model for Molecular Conformation Generation

Information about training algorithms, parameters, fairness constraints or other applied approaches, and features: N.A.

Paper or other resource for more information: N.A.

License: MIT

Where to send questions or comments about the model: Open an issue on GeoDiff repo.

Intended Use. Use cases that were envisioned during development: Chemical research, in particular drug discovery.

Primary intended uses/users: Researchers and computational chemists using the model for model comparison or research exploration purposes.

Out-of-scope use cases: Production-level inference, producing molecules with harmful properties.

Metrics: N.A.

Datasets: N.A.

Ethical Considerations: Unclear, please consult with original authors in case of questions.

Caveats and Recommendations: Unclear, please consult with original authors in case of questions.

Model card prototype inspired by Mitchell et al. (2019)

Citation

@inproceedings{xu2022geodiff,
  author    = {Minkai Xu and Lantao Yu and Yang Song and Chence Shi and Stefano Ermon and Jian Tang},
  title     = {GeoDiff: {A} Geometric Diffusion Model for Molecular Conformation Generation},
  booktitle = {The Tenth International Conference on Learning Representations, {ICLR}},
  year      = {2022},
}