Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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. |