Abstract
Supervised pre-training with computed property labels improves graph transformer performance in biochemistry, while limiting model capacity reduces overfitting.
Generated by thinkingmachines/Inkling-Small
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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