Predicting Molecular Conformation via Dynamic Graph Score Matching
Authors: Shitong Luo, Chence Shi, Minkai Xu, Jian Tang
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments across multiple tasks show that the DGSM outperforms state-of-the-art baselines by a large margin, and it is capable of generating conformations for a broader range of systems such as proteins and multi-molecular complexes. |
| Researcher Affiliation | Academia | Shitong Luo*1, Chence Shi*2,3, Minkai Xu2,3, Jian Tang2,4,5 1Peking University 2Mila Québec AI Institute 3Université de Montréal 4HEC Montréal 5CIFAR AI Research Chair luost@pku.edu.cn , chence.shi@umontreal.ca minkai.xu@umontreal.ca , jian.tang@hec.ca |
| Pseudocode | Yes | Algorithm 1 Annealed Langevin dynamics |
| Open Source Code | No | The paper does not include an unambiguous statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Following previous works [34, 43], we use the GEOM-QM9 and GEOM-Drugs [1] datasets for this task. |
| Dataset Splits | No | The paper mentions 'train-test split' but does not explicitly state the use of a distinct 'validation' dataset split for hyperparameter tuning or early stopping. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | The threshold δ of COV score is 0.5Å for GEOM-QM9 and 1.25Å for GEOM-Drugs following Xu et al. [43]. We here take the prior distribution as a standard Gaussian N(R0 | 0, I). Then, we update the conformation by running T steps of Langevin dynamic to get a sample from each noise conditional score network sθ(R, σi) sequentially with a special step size schedule αi = ε σ2 i /σ2 L. |