This is a collection of recent parpers of graph neural network applied in drug discovery
- Review
- Articles
- Graph convolutional networks for computational drug development and discovery. BRIEF BIOINFORM. 2019. Link
- Strategies For Pre-training Graph Neural Networks. ICLR. 2020. Link
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Convolutional networks on graphs for learning molecular fingerprints. NIPS. 2015. Link
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Molecular graph convolutions: moving beyond fingerprints. J. Comput.-Aided Mol. Des. 2016. Link
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Neural message passing for quantum chemistry. JMLR. 2017. Link
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Deeply learning molecular structure-property relationships using attention- and gate-augmented graph convolutional network. arXiv. 2018. Link
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Edge attention-based multi-relational graph convolutional networks. arXiv. 2018. Link
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Graph warp module an auxiliary module for boosting the power of graph neural networks in molecular graph analysis. arXiv. 2019. Link
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Analyzing Learned Molecular Representations for Property Prediction. J CHEM INF MODEL 2019. Link
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Graph networks as a universal machine learning framework for molecules and crystals. CHEM MATER. 2019. Link
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Molecular activity prediction using graph convolutional deep neural network considering distance on a molecular graph. aRxiv. 2019. Link
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Multitask learning on graph neural networks applied to molecular property predictions. aRxiv. 2019. Link
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Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism. J MED CHEM. 2019. Link
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Molecular geometry prediction using a deep generative graph neural network. Sci Rep. 2019. Link
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Practical high-quality electrostatic potential surfaces for drug. J MED CHEM. 2019. Link
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Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction. J CHEMINFORMATICS. 2020. Link
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A deep learning approach to antibiotic discovery. CELL. 2020 Link
- Structural learning of proteins using graph convolutional neural networks. bioRxiv. 2019. Link
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Predicting organic reaction outcomes with weisfeiler-lehman network. NIPS. 2017. Link
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A graph-convolutional neural network model for the prediction of chemical reactivity. CHEM SCI. 2019. Link
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Graph transformation policy network for chemical reaction prediction. KDD. 2019. Link
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Integrating deep neural networks and symbolic inference for organic reactivity prediction. chemArix. 2020. Link
- Rapid and accurate prediction of pka values of C–H acids using graph convolutional neural networks. JACS. 2019. Link
- Interpretable retrosynthesis prediction in two steps. ChemRxiv. 2020. Link
- MolGAN: An implicit generative model for small molecular graphs. aRxiv. 2018. Link
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Graphvae: Towards generation of small graphs using variational autoencoders. ICANN. 2018. Link
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Junction tree variational autoencoder for molecular graph generation. PMLR. 2018. Link
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Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation. J CHEMINFORMATICS. 2019. Link
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Hierarchical graph-to-graph translation for molecules. aRxiv. 2019. Link
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Learning Multimodal Graph-to-graph Translation For Molecular Optimization. ICLR. 2019. Link
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Core: Automatic molecule optimization using copy & refine strategy. AAAI. 2020. Link
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Graph convolutional policy network for goal-directed molecular graph generation. NIPS. 2018. Link
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Multi-objective de novo drug design with conditional graph generative model. J CHEMINFORMATICS. 2018. Link
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MolecularRNN: Generating realistic molecular graphs with optimized properties. aRxiv. 2019. Link
- GraphNVP:An invertible flow model for generating molecular graphs. aRxiv. 2019. Link
- A model to search for synthesizable molecules. NIPS. 2019. Link
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Atomic convolutional networks for predicting protein-ligand binding affinity. aRxiv. 2017. Link
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PotentialNet for molecular property prediction. ACS CENTRAL SCI. 2018. Link
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Interpretable drug target prediction using deep neural representation. IJCAI. 2018. Link
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Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences. BIOINFORMATICS. 2019. Link
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GraphDTA: Prediction of drug target binding affinity using graph neural networks. bioRxiv . 2019. Link
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Graph convolutional neural networks for predicting drug-target interactions. J CHEM INF MODEL. 2019. Link
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Predicting drug−target interaction using a novel graph neural network with 3d structure-embedded graph representation. J CHEM INF MODEL. 2019. Link
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AGL-Score: Algebraic graph learning score for protein−ligand binding scoring, ranking, docking, and screening. J CHEM INF MODEL. 2019. Link
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Target identification among known drugs by deep learning from heterogeneous networks. CHEM SCI. 2020. Link
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Enhancing drug-drug interaction extraction from texts by molecular structure information. aRxiv. 2018. Link
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MR-GNN: Multi-resolution and dual graph neural network for predicting structured entity interactions. aRxiv. 2018. Link
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Drug similarity integration through attentive multi-view graph auto-encoders. aRxiv. 2018. Link
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Modeling polypharmacy side effects with graph convolutional networks. BIOINFORMATICS. 2018. Link
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GENN: Predicting correlated drug-drug interactions with graph energy neural networks. aRxiv. 2019. Link
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Tri-graph information propagation for polypharmacy side effect prediction. aRxiv. 2019. Link
- Protein interface prediction using graph convolutional networks. NIPS. 2017. Link
- Chemically interpretable graph interaction network for prediction of pharmacokinetic properties of drug-like molecules. chemRxiv. 2020. Link
- Graph neural representational learning of RNA secondary structures for predicting RNA-protein interactions. bioRxiv. 2020. Link
- Graph convolutional network and convolutional neural network based method for predicting lncRNA-disease associations. Cells. 2019. Link
- Graph convolution for predicting associations between miRNA and drug resistance. BIOINFORMATICS. 2020. Link
- GCN-MF: Disease-gene association identification by graph convolutional networks and matrix factorization. KDD. 2019. Link