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Pre-training with Graph Transformers
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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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-12T10:01:03.000Z
First collected: 2026-09-20T16:41:15.630Z. This is not the publication date.