Learning to Group Auxiliary Datasets for Molecule
Authors: Tinglin Huang, Ziniu Hu, Rex Ying
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments demonstrate the efficiency and effectiveness of Mol Group, showing an average improvement of 4.41%/3.47% for GIN/Graphormer trained with the group of molecule datasets selected by Mol Group on 11 target molecule datasets. |
| Researcher Affiliation | Academia | Tinglin Huang1 Ziniu Hu2 Rex Ying1 1Yale University, 2University of California, Los Angeles |
| Pseudocode | Yes | The pseudo-code is presented in Algo.1. |
| Open Source Code | Yes | Source code is available at https://github.com/Graph-and-Geometric-Learning/Mol Group. |
| Open Datasets | Yes | Our study utilizes 15 molecule datasets of varying sizes obtained from Molecule Net [51, 18] and Chem BL [33], which can be categorized into three groups: medication, quantum mechanics, and chemical analysis. All the involved datasets can be accessed and downloaded from OGB4 or Molecule Net repository5. (Footnote 4: https://ogb.stanford.edu/, Footnote 5: https://moleculenet.org/) |
| Dataset Splits | Yes | We follow the original split setting, where qm8 and qm9 are randomly split, and scaffold splitting is used for the others. |
| Hardware Specification | Yes | The experiments are conducted on a single Linux server with The Intel Xeon Gold 6240 36-Core Processor, 361G RAM, and 4 NVIDIA A100-40GB. |
| Software Dependencies | Yes | Our method is implemented on Py Torch 1.10.0 and Python 3.9.13. |
| Experiment Setup | Yes | As for GIN [53], we fix the batch size as 128 and train the model for 50 epochs. We use Adam [24] with a learning rate of 0.001 for optimization. The hidden size and number of layers are set as 300 and 5 respectively. We set the dropout rate as 0.5 and apply batchnorm [21] in each layer. All the results are reported after 5 different random seeds. As for Graphormer [57], we fix the batch size as 128 and train the model for 30 epochs. Adam W [31] with a learning rate of 0.0001 is used as the optimizer. The hidden size, number of layers, and number of attention heads are set as 512, 5, and 8 respectively. We set the dropout rate and attention dropout rate as 0.1 and 0.3. |