MolCHG uses a multi-level compositional hierarchical graph with atom-bond cross-view contrastive learning, functional group prediction, and structure tasks to achieve top results on seven of nine MoleculeNet benchmarks.
Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism.Journal of medicinal chemistry, 63(16):8749–8760, 2019
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Multi-level Self-supervised Pretraining on Compositional Hierarchical Graph for Molecular Property Prediction
MolCHG uses a multi-level compositional hierarchical graph with atom-bond cross-view contrastive learning, functional group prediction, and structure tasks to achieve top results on seven of nine MoleculeNet benchmarks.