A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining
Authors: Shengchao Liu, Weitao Du, Zhi-Ming Ma, Hongyu Guo, Jian Tang
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | By comparing with 17 pretraining baselines, we empirically verify that Molecule SDE can learn an expressive representation with state-of-the-art performance on 26 out of 32 downstream tasks. |
| Researcher Affiliation | Academia | 1Mila Quebec AI Institute, Canada 2Universite de Montreal, Canada 3Chinese Academy of Sciences, China 4National Research Council Canada, Canada 5HEC Montreal, Canada. |
| Pseudocode | No | The paper includes detailed pipeline diagrams (e.g., Figure 3, Figure 4) but does not present any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source codes are available in this repository. |
| Open Datasets | Yes | For pretraining, we use PCQM4Mv2 (Hu et al., 2020a). ... We take 110K for training, 10K for validation, and 11K for testing. |
| Dataset Splits | Yes | We take 110K for training, 10K for validation, and 11K for testing. |
| Hardware Specification | Yes | All the jobs are running on one single V100 GPU card. |
| Software Dependencies | No | The paper mentions software components like GIN and Sch Net models but does not provide specific version numbers for these or other ancillary software dependencies (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | Table 8. Hyperparameter specifications for Molecule SDE. Hyperparameter Value epochs {50, 100} learning rate 2D GNN {1e-5, 1e-6} SDE option {VE, VP} masking ratio M {0, 0.3} number of steps {1000}. |