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• W2969457089 abstract "Accurate prediction of molecular properties is important for new compound design, which is a crucial step in drug discovery. In this paper, molecular graph data is utilized for property prediction based on graph convolution neural networks. In addition, a convolution spatial graph embedding layer (C-SGEL) is introduced to retain the spatial connection information on molecules. And, multiple C-SGELs are stacked to construct a convolution spatial graph embedding network (C-SGEN) for end-to-end representation learning. In order to enhance the robustness of the network, molecular fingerprints are also combined with C-SGEN to build a composite model for predicting molecular properties. Our comparative experiments have shown that our method is accurate and achieves the best results on some open benchmark datasets." @default.
• W2969457089 created "2019-08-29" @default.
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• W2969457089 date "2019-08-22" @default.
• W2969457089 modified "2023-10-08" @default.
• W2969457089 title "Molecule Property Prediction Based on Spatial Graph Embedding" @default.
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• W2969457089 doi "https://doi.org/10.1021/acs.jcim.9b00410" @default.
• W2969457089 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31438677" @default.
• W2969457089 hasPublicationYear "2019" @default.
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