# 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](https://openreview.net/forum?id=PzcvxEMzvQC) and this [Colab](https://colab.research.google.com/drive/1pLYYWQhdLuv1q-JtEHGZybxp2RBF8gPs#scrollTo=-3-P4w5sXkRU)

**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](https://openreview.net/forum?id=PzcvxEMzvQC): 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](https://github.com/MinkaiXu/GeoDiff)).

**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`](https://github.com/MinkaiXu/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)](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)

## Citation
```bib
@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},
}
```