model_cards/article.md

# Model documentation & parameters

**Algorithm Version**: Which model version to use.

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

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



# Model card -- PolymerBlocks

**Model Details**: *PolymerBlocks* is a sequence-based molecular generator tuned to generate blocks of polymers (e.g., catalysts and monomers). The model relies on a Variational Autoencoder architecture as described in [Born et al. (2021; *iScience*)](https://www.sciencedirect.com/science/article/pii/S2589004221002376).

**Developers**: Matteo Manica and colleagues from IBM Research.

**Distributors**: Original authors' code integrated into GT4SD.

**Model date**: Not yet published.

**Model version**: Only initial model version. The model has been pre-trained on 500K compounds from PubChem and further fine-tuned on the SMILES representing monomers and catalysts collected in the database presented in [Park et al. (2022)](https://doi.org/10.26434/chemrxiv-2022-811rl).

**Model type**: A sequence-based molecular generator tuned to generate blocks of polymers (e.g., catalysts and monomers).

**Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**: the sequence-based model is a standard GRU-based VAE trained to reconstruct SMILES representation of molecules. Given the nature of the pre-training and fine-tuning data, the model is biased to create molecules that resemble catalysts and monomers employed in ring-opening polymerization.

**Paper or other resource for more information**: Details on the model used and code can be found in [Born et al. (2021; *iScience*)](https://www.sciencedirect.com/science/article/pii/S2589004221002376).

**License**: MIT

**Where to send questions or comments about the model**: Open an issue on [GT4SD repository](https://github.com/GT4SD/gt4sd-core).

**Intended Use. Use cases that were envisioned during development**: Chemical research, in particular discovery and catalysts for polymerization.

**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**: See description in the model versions.

**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
@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}
}
```