# Model documentation & parameters

**Algorithm**: Which model to use (GCPN or GraphAF).

**Algorithm Version**: Which model checkpoint to use (trained on different datasets).

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


# Model card -- GCPN

**Model Details**: GCPN is a graph-based molecular generative model that can be optimized with RL for goal-directed graph generation.

**Developers**: Jiaxuan You, Bowen Liu and co-authors from Stanford.

**Distributors**: Code provided by TorchDrug developers, wrapped and distributed by GT4SD Team (2023) from IBM Research.

**Model date**: Published in 2018.

**Model version**: Models trained by GT4SD team on the tasks provided by TorchDrug repo [(see their tutorial)](https://torchdrug.ai/docs/tutorials/generation.html). 
- **ZINC_250k**: 250,000 drug-like molecules with a maximum atom number of 38, taken from [ZINC](https://zinc.docking.org).
- **QED**: ZINC dataset, but the model was optimized with Proximal Policy Optimization (PPO) to generate molecules with high QED scores.
- **pLogP**: ZINC dataset, but the model was optimized with Proximal Policy Optimization (PPO) to generate molecules with high pLogP scores.

**Model type**: A graph-based molecular generative model that can be optimized with RL for goal-directed graph generation.

**Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**: Default parameters as provided in [(TorchDrug tutorial)](https://torchdrug.ai/docs/tutorials/generation.html).

**Paper or other resource for more information**: [Graph Convolutional Policy Network for
Goal-Directed Molecular Graph Generation (NeurIPS 2018)](https://proceedings.neurips.cc/paper/2018/file/d60678e8f2ba9c540798ebbde31177e8-Paper.pdf).

**License**: TorchDrug: Apache-2.0 license.

**Where to send questions or comments about the model**: Open an issue on [TorchDrug repository](https://github.com/DeepGraphLearning/torchdrug) or ask original authors.

**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.

**Factors**: Not applicable.

**Metrics**: Validation loss on decoding correct molecules.

**Datasets**: 250,000 drug-like molecules from [ZINC](https://zinc.docking.org) (with a maximum atom number of 38).

**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{you2018graph,
  title={Graph convolutional policy network for goal-directed molecular graph generation},
  author={You, Jiaxuan and Liu, Bowen and Ying, Zhitao and Pande, Vijay and Leskovec, Jure},
  journal={Advances in neural information processing systems},
  volume={31},
  year={2018}
}
```


# Model card -- GraphAF

**Model Details**: GraphAF is a flow-based autoregressive graph molecular generative model that can be optimized with RL for goal-directed graph generation.

**Developers**: Chence Shi, Minkai Xu and co-authors from Peking and Shanghai University and MILA.

**Distributors**: Code provided by TorchDrug developers, wrapped and distributed by GT4SD Team (2023) from IBM Research.

**Model date**: Published in 2020.

**Model version**: Models trained by GT4SD team on the tasks provided by TorchDrug repo [(see their tutorial)](https://torchdrug.ai/docs/tutorials/generation.html). 
- **ZINC_250k**: 250,000 drug-like molecules with a maximum atom number of 38, taken from [ZINC](https://zinc.docking.org).
- **QED**: ZINC dataset, but the model was optimized with Proximal Policy Optimization (PPO) to generate molecules with high QED scores.
- **pLogP**: ZINC dataset, but the model was optimized with Proximal Policy Optimization (PPO) to generate molecules with high pLogP scores.

**Model type**: A flow-based autoregressive graph molecular generative model that can be optimized with RL for goal-directed graph generation.

**Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**: Default parameters as provided in [(TorchDrug tutorial)](https://torchdrug.ai/docs/tutorials/generation.html).

**Paper or other resource for more information**: [GraphAF: a flow-based autoregressive model for molecular graph generation (*ICLR 2020*)](https://openreview.net/pdf?id=S1esMkHYPr).

**License**: TorchDrug: Apache-2.0 license.

**Where to send questions or comments about the model**: Open an issue on [TorchDrug repository](https://github.com/DeepGraphLearning/torchdrug) or ask original authors.

**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.

**Factors**: Not applicable.

**Metrics**: Validation loss on decoding correct molecules.

**Datasets**: 250,000 drug-like molecules from [ZINC](https://zinc.docking.org) (with a maximum atom number of 38).

**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{shi2020graphaf,
  author    = {Chence Shi and Minkai Xu and Zhaocheng Zhu and Weinan Zhang and Ming Zhang and Jian Tang},
  title     = {GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation},
  booktitle = {International Conference on Learning Representations, {ICLR} 2020},
  year      = {2020},
  url       = {https://openreview.net/forum?id=S1esMkHYPr}
}
```