GNN-Retro: Retrosynthetic Planning with Graph Neural Networks
Authors: Peng Han, Peilin Zhao, Chan Lu, Junzhou Huang, Jiaxiang Wu, Shuo Shang, Bin Yao, Xiangliang Zhang4014-4021
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments on the USPTO dataset show that our framework could outperform the state-of-the-art methods with a large margin under the same settings. |
| Researcher Affiliation | Collaboration | 1 University of Electronic Science and Technology of China 2 King Abdullah University of Science and Technology 3 Aalborg University 4 Tencent AI Lab 5 Shanghai Jiao Tong University 6 University of Notre Dame |
| Pseudocode | No | The paper describes mathematical formulations and processes but does not include any explicit pseudocode blocks or algorithm listings. |
| Open Source Code | No | The paper does not provide any statement regarding the release of source code for the described methodology or a link to a code repository. |
| Open Datasets | Yes | The public reaction dataset United States Patent Office (USPTO) is used in our method with the same preprocessing as (Chen et al. 2020). |
| Dataset Splits | Yes | There are about 1.3 million reactions after the deduplication and filtration, which are randomly separated into training/validation/testing sets with proportion 80%/10%/10% respectively. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for experiments are provided. |
| Software Dependencies | No | The paper mentions 'Adam' as an optimizer but does not provide specific version numbers for any programming languages, libraries, or frameworks used (e.g., Python, PyTorch, TensorFlow). |
| Experiment Setup | Yes | For every target molecule, we at most run the one-step reactions 500 times, which is the same as (Chen et al. 2020). The embedding of the molecule is fixed as 128. We set the weight λ of partial ordering loss as 1. The slack variable ϵ is set as 7. For the threshold τ, we select it from the range [0 : 0.1 : 1.0]. The weight α is also selected from the range [0 : 0.1 : 1.0]. and Adam (Kingma and Ba 2015) is utilized as the optimizer to minimize the loss L with learning rate 0.001. |