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Predicting interactions between structured entities lies at the core of numerous tasks such as drug regimen and new material design.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
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Convolutional networks on graphs for learning molecular fingerprints
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Diffusion-convolutional neural networks
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Convolutional neural networks on graphs with fast localized spectral filtering
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Molecular graph convolutions: moving beyond fingerprints
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Semi-supervised classification with graph convolutional networks
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Structural deep network embedding
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Low data drug discovery with one-shot learning
Han Altae-Tran, Bharath Ramsundar, Aneesh S Pappu, and Vijay Pande · 2017
Semi-supervised classification with graph convolutional networks. arxiv preprint
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Graph edit distance computation via graph neural networks
Yunsheng Bai, Hao Ding, Song Bian, Ting Chen, Yizhou Sun, and Wei Wang · 2018
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Deep learning improves prediction of drug-drug and drug-food interactions
J. Y. Ryu, H. U. Kim, and S. Y. Lee · 2018
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Petar Velic̈kovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2018
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Link prediction based on graph neural networks
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Graph convolution over pruned dependency trees improves relation extraction
Yuhao Zhang, Peng Qi, and Christopher D Manning · 2018
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Modeling polypharmacy side effects with graph convolutional networks
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