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Graph Neural Networks with Multiple Feature Extraction Paths for Chemical Property Estimation

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...

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Detalles Bibliográficos
Autores principales: Ishida, Sho, Miyazaki, Tomo, Sugaya, Yoshihiro, Omachi, Shinichiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8197261/
https://www.ncbi.nlm.nih.gov/pubmed/34073745
http://dx.doi.org/10.3390/molecules26113125
Descripción
Sumario: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.