[TorchDrug](https://github.com/DeepGraphLearning/torchdrug) is a PyTorch toolbox on graph models for drug discovery. We, the developers of **GT4SD** (Generative Toolkit for Scientific Discovery), provide access to two graph-based molecular generative models distributed by TorchDrug: - **GCPN**: Graph Convolutional Policy Network ([You et al., (2018), *NeurIPS*](https://proceedings.neurips.cc/paper/2018/hash/d60678e8f2ba9c540798ebbde31177e8-Abstract.html)) - **GraphAF**: GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation ([Shi et al., (2020), *ICLR*](https://openreview.net/forum?id=S1esMkHYPr)) For **examples** and **documentation** of the model parameters, please see below. Moreover, we provide a **model card** ([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)) at the bottom of this page. If you use `GT4SD`, please cite: ```bib @article{manica2023accelerating, title={Accelerating material design with the generative toolkit for scientific discovery}, author={Manica, Matteo and Born, Jannis and Cadow, Joris and Christofidellis, Dimitrios and Dave, Ashish and Clarke, Dean and Teukam, Yves Gaetan Nana and Giannone, Giorgio and Hoffman, Samuel C. and Buchan, Matthew and Chenthamarakshan, Vijil and Donovan, Timothy and Hsu, Hsiang Han and Zipoli, Federico and Schilter, Oliver and Kishimoto, Akihiro and Hamada, Lisa and Padhi, Inkit and Wehden, Karl and McHugh, Lauren and Khrabrov, Alexy and Das, Payel and Takeda, Seiji and Smith, John R.}, journal={npj Computational Materials}, volume={9}, number={1}, pages={69}, year={2023}, doi={10.1038/s41524-023-01028-1}, url={https://doi.org/10.1038/s41524-023-01028-1}, issn={2057-3960} } ```