model_cards/article.md

Model documentation & parameters

Algorithm Version: Which model version to use.

Target binding energy: The desired binding energy.

Primer SMILES: A SMILES string used to prime the generation.

Maximal sequence length: The maximal number of SMILES tokens in the generated molecule.

Number of points: Number of points to sample with the Gaussian Process.

Number of steps: Number of optimization steps in the Gaussian Process optimization.

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

Model card -- AdvancedManufacturing

Model Details: AdvancedManufacturing is a sequence-based molecular generator tuned to generate catalysts. The model relies on a recurrent Variational Autoencoder with a binding-energy predictor trained on the latent code. The framework uses Gaussian Processes for generating targeted molecules.

Developers: Oliver Schilter and colleagues from IBM Research.

Distributors: Original authors' code integrated into GT4SD.

Model date: Not yet published.

Model version: Different types of models trained on NCCR data using SMILES or SELFIES, potentially also with augmentation.

Model type: A sequence-based molecular generator tuned to generate catalysts. The model relies on a recurrent Variational Autoencoder with a binding-energy predictor trained on the latent code. The framework uses Gaussian Processes for generating targeted molecules.

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

Paper or other resource for more information: TBD

License: MIT

Where to send questions or comments about the model: Open an issue on GT4SD repository.

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: Data provided through NCCR.

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

TBD, temporarily please cite:

@article{manica2022gt4sd,
  title={GT4SD: Generative Toolkit for Scientific Discovery},
  author={Manica, Matteo and Cadow, Joris and Christofidellis, Dimitrios and Dave, Ashish and Born, Jannis and Clarke, Dean and Teukam, Yves Gaetan Nana and Hoffman, Samuel C and Buchan, Matthew and Chenthamarakshan, Vijil and others},
  journal={arXiv preprint arXiv:2207.03928},
  year={2022}
}