GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation
Authors: Chence Shi*, Minkai Xu*, Zhaocheng Zhu, Weinan Zhang, Ming Zhang, Jian Tang
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that Graph AF is able to generate 68% chemically valid molecules even without chemical knowledge rules and 100% valid molecules with chemical rules. The training process of Graph AF is two times faster than the existing state-of-the-art approach GCPN. After fine-tuning the model for goal-directed property optimization with reinforcement learning, Graph AF achieves state-of-the-art performance on both chemical property optimization and constrained property optimization. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Peking University, China 2Shanghai Jiao Tong University, China 3Mila Qu ebec AI Institute, Canada 4Universit e de Montr eal, Canada 5HEC Montr eal, Canada 6CIFAR AI Research Chair |
| Pseudocode | Yes | We summarize the detailed training algorithm into Appendix B. Algorithm 1 Parallel Training Algorithm of Graph AF |
| Open Source Code | Yes | Code is available at https://github.com/Deep Graph Learning/Graph AF |
| Open Datasets | Yes | We use the ZINC250k molecular dataset (Irwin et al., 2012) for training. |
| Dataset Splits | No | The paper mentions using the ZINC250k dataset for training and evaluates metrics on generated molecules, but it does not specify explicit train/validation/test dataset splits (e.g., percentages or counts for each split) or refer to standard predefined splits for reproducibility. |
| Hardware Specification | Yes | To achieve the results in Table 2, JT-VAE and GCPN take around 24 and 8 hours, respectively, while Graph AF only takes 4 hours. a machine with 1 Tesla V100 GPU and 32 CPU cores. |
| Software Dependencies | No | Graph AF is implemented in Py Torch (Paszke et al., 2017). We use the open-source chemical software RDkit (Landrum, 2016) to preprocess molecules. We use Adam (Kingma & Ba, 2014) to optimize our model. The paper mentions software and frameworks but does not provide specific version numbers for PyTorch or RDkit. |
| Experiment Setup | Yes | The R-GCN is implemented with 3 layers, and the embedding dimension is set as 128. The max graph size is set as 48 empirically. For density modeling, we train our model for 10 epochs with a batch size of 32 and a learning rate of 0.001. We use Adam (Kingma & Ba, 2014) to optimize our model. gamma is set to 0.97 for QED optimization and 0.9 for penalized log P optimization respectively. We fine-tune the pretrained model for 200 iterations with a fixed batch size of 64 using Adam optimizer. We also adopt a linear learning rate warm-up to stabilize the training. We use Adam with a learning rate of 0.0001 to optimize the model. |