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Self-Supervised Graph Transformer for Molecules by tianwei luo is a document available to read on EtoBox.

What is Self-Supervised Graph Transformer for Molecules about?

The document presents GROVER, a self-supervised graph transformer framework designed for molecular representation learning, addressing challenges such as insufficient labeled data and poor generalization in Graph Neural Networks (GNNs). GROVER utilizes self-supervised tasks at node, edge, and graph levels to learn from large unlabelled molecular datasets, achieving significant improvements in molecular property prediction compared to existing methods. With 100 million parameters pre-trained on 10 million mo

Author

tianwei luo

Language

EN