# Model documentation & parameters

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

**Primer SMILES**: The SMILES string for priming the generation (**has** to be provided).

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

**Sampling uniquely**: Generate unique sample sequences if set to true.

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


# Model card -- REINVENT

**Model Details**: *REINVENT* is a collection of tools for *de novo* drug design. Here, we showcase the SMILES-based generative model. For details, see [Blaschke et al. (2020); *J. Chem. Inf. Model.*](https://pubs.acs.org/doi/10.1021/acs.jcim.0c00915).

**Developers**: Thomas Blaschke and colleagues from AstraZeneca.

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

**Model date**: 2020, see the [REINVENT 2.0 paper](https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.0c00915)

**Model version**: N.A.

**Model type**: A sequence-based molecular generator from the REINVENT toolbox.

**Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**: 
The code for getting unique sample sequences, randomizing scaffolds, and generation of the dataset as well as the dataloader was taken from the original implementation of [Molecular Reinvent](https://github.com/MolecularAI/Reinvent). Our implementation does not include [BaseAction](https://github.com/MolecularAI/Reinvent/blob/982b26dd6cfeb8aa84b6d7e4a8c2a7edde2bad36/running_modes/lib_invent/rl_actions/sample_model.py#:~:text=class%20BaseAction(abc.ABC)%3A) as a parent class for the [ReinventBase](/gt4sd/algorithms/conditional_generation/reinvent/reinvent_core/core.py) where we have added all the functions of [Molecular Reinvent](https://github.com/MolecularAI/Reinvent).


**Paper or other resource for more information**: 
[REINVENT 2.0 -- Blaschke et al. (2020); *J. Chem. Inf. Model.*](https://pubs.acs.org/doi/10.1021/acs.jcim.0c00915).

**License**: MIT

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

**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
@article{blaschke2020reinvent,
  title={REINVENT 2.0: an AI tool for de novo drug design},
  author={Blaschke, Thomas and Ar{\'u}s-Pous, Josep and Chen, Hongming and Margreitter, Christian and Tyrchan, Christian and Engkvist, Ola and Papadopoulos, Kostas and Patronov, Atanas},
  journal={Journal of chemical information and modeling},
  volume={60},
  number={12},
  pages={5918--5922},
  year={2020},
  publisher={ACS Publications}
}
```