Graph neural networks with multiple feature extraction paths for chemical property estimation

Sho Ishida, Tomo Miyazaki, Yoshihiro Sugaya, Shinichiro Omachi

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Feature extraction is essential for chemical property estimation of molecules using machine learning. Recently, graph neural networks have attracted attention for feature extraction from molecules. However, existing methods focus only on specific structural information, such as node relationship. In this paper, we propose a novel graph convolutional neural network that performs feature extraction with simultaneously considering multiple structures. Specifically, we propose feature extraction paths specialized in node, edge, and three-dimensional structures. Moreover, we propose an attention mechanism to aggregate the features extracted by the paths. The attention aggregation enables us to select useful features dynamically. The experimental results showed that the proposed method outperformed previous methods.

Original languageEnglish
Article number3125
Issue number11
Publication statusPublished - 2021


  • Chemical property estimation
  • Graph neural networks
  • Molecular data
  • Multiple feature extraction

ASJC Scopus subject areas

  • Analytical Chemistry
  • Chemistry (miscellaneous)
  • Molecular Medicine
  • Pharmaceutical Science
  • Drug Discovery
  • Physical and Theoretical Chemistry
  • Organic Chemistry


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