https://github.com/yangnianzu0515/MoleOOD

Official implementation for the paper "Learning Substructure Invariance for Out-of-Distribution Molecular Representations" (NeurIPS 2022).
https://github.com/yangnianzu0515/MoleOOD

graph-classification molecule-representation-learning out-of-distribution-generalization pytorch

Last synced: about 1 month ago
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Official implementation for the paper "Learning Substructure Invariance for Out-of-Distribution Molecular Representations" (NeurIPS 2022).

• Host: GitHub
• URL: https://github.com/yangnianzu0515/MoleOOD
• Owner: yangnianzu0515
• License: mit
• Created: 2022-11-01T10:34:56.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2023-02-16T03:32:15.000Z (over 1 year ago)
• Last Synced: 2024-02-15T09:38:05.249Z (4 months ago)
• Topics: graph-classification, molecule-representation-learning, out-of-distribution-generalization, pytorch
• Language: Python
• Homepage:
• Size: 105 MB
• Stars: 50
• Watchers: 2
• Forks: 6
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-python-chemistry - MoleOOD - a robust molecular representation learning framework against distribution shifts. (Machine Learning)
• awesome-python-chemistry - MoleOOD - a robust molecular representation learning framework against distribution shifts. (Machine Learning)