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Hierarchical Molecular Graph Self-Supervised Learning for property prediction - Communications Chemistry

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Hierarchical Molecular Graph Self-Supervised Learning for property prediction - Communications Chemistry

Molecular graph representation learning has shown considerable strength in molecular analysis and drug discovery. Due to the difficulty of obtaining molecular property labels, pre-training models based on self-supervised learning has become increasingly popular in molecular representation learning. Notably, Graph Neural Networks (GNN) are employed as the backbones to encode implicit representations of molecules in most existing works.

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Hierarchical Molecular Graph Self-Supervised Learning for property prediction - Communications Chemistry

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Hierarchical Molecular Graph Self-Supervised Learning for property prediction - Communications Chemistry

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