update

model_cards/article.md

@@ -11,7 +11,7 @@
 
 **Model Details**: GCPN is a graph-based molecular generative model that can be optimized with RL for goal-directed graph generation.
 
-**Developers**: Jiaxuan You and co-authors from Stanford.
+**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.
 
@@ -31,7 +31,7 @@ Goal-Directed Molecular Graph Generation (NeurIPS 2018)](https://proceedings.neu
 
 **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).
+**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.
 
@@ -63,3 +63,59 @@ Model card prototype inspired by [Mitchell et al. (2019)](https://dl.acm.org/doi
 }
 ```
 
+
+# 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}
+}
+```
+