| <img align="right" src="https://raw.githubusercontent.com/GT4SD/gt4sd-core/main/docs/_static/gt4sd_logo.png" alt="logo" width="120" > | |
| [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. | |