Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning

Molecular representation learning is a crucial task in predicting molecular properties. Molecules are often modeled as graphs where atoms and chemical bonds are represented as nodes and edges, respectively, and Graph Neural Networks (GNNs) have been commonly utilized to predict atom-related properti...

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Hauptverfasser:	Chen, Jiahao, Liu, Yurou, Li, Jiangmeng, Su, Bing, Wen, Jirong
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Artificial Intelligence Computer Science - Learning Quantitative Biology - Quantitative Methods
Online-Zugang:	Volltext bestellen
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