Updated 29 h ago · first seen 15 Sept 2026
paper_01M2JK0BS5QH80E9J86ZQCH2DZ
Abstract
This article investigates pre-training strategies for graph transformers in the biochemistry domain. By conducting comprehensive experiments, the study reveals that supervised pre-training using computed properties as labels provides the highest performance gain on downstream tasks. The results also highlight the importance of constraining model capacity to mitigate overfitting in graph transformers.
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New paper: Pre-training with Graph Transformers
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