| # 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} | |
| } | |
| ``` | |