Papers for

chemical simulation teams

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Graph transformers perform better when trained on computed biochemistry properties

Pre-training with Graph Transformers

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.

Sat 12 SeptMachine Learning

The gist

Understanding complex molecules can help us develop new medicines and materials. The authors study a type of AI model called graph transformers that look at molecules as connected points. They found that teaching these models first by showing them computed chemical properties helps them learn better and perform well on later tasks. Also, keeping the model size smaller helps avoid learning mistakes from too much data.

Open → 2609.13844v1