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Graph Neural Networks (GNNs) have shown remarkable success in molecular tasks, yet their interpretability remains challenging.
Recap retrosynthetic combinatorial analysis procedure: a powerful new technique for identifying privileged molecular fragments with useful applications in combinatorial chemistry
Xiao Qing Lewell, Duncan B Judd, Stephen P Watson, and Michael M Hann · 1998
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Network motifs: simple building blocks of complex networks
Ron Milo, Shai Shen-Orr, Shalev Itzkovitz, Nadav Kashtan, Dmitri Chklovskii, and Uri Alon · 2002
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Network motifs in the transcriptional regulation network of escherichia coli
Shai S Shen-Orr, Ron Milo, Shmoolik Mangan, and Uri Alon · 2002
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Derivation and validation of toxicophores for mutagenicity prediction
Jeroen Kazius, Ross McGuire, and Roberta Bursi · 2005
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Network motifs: theory and experimental approaches
Uri Alon · 2007
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On the art of compiling and using’drug-like’chemical fragment spaces
Jörg Degen, Christof Wegscheid-Gerlach, Andrea Zaliani, and Matthias Rarey · 2008
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Iam graph database repository for graph based pattern recognition and machine learning
Kaspar Riesen and Horst Bunke · 2008
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Large-scale learnable graph convolutional networks
Hongyang Gao, Zhengyang Wang, and Shuiwang Ji · 2018
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Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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An introduction to systems biology: design principles of biological circuits
Uri Alon · 2019
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Explainability techniques for graph convolutional networks
Federico Baldassarre and Hossein Azizpour · 2019
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Graph u-nets
Hongyang Gao and Shuiwang Ji · 2019
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Explainability methods for graph convolutional neural networks
Phillip E Pope, Soheil Kolouri, Mohammad Rostami, Charles E Martin, and Heiko Hoffmann · 2019
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Gnnexplainer: Generating explanations for graph neural networks
Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec · 2019
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Hard masking for explaining graph neural networks
Thorben Funke, Megha Khosla, and Avishek Anand · 2020
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Graphlime: Local interpretable model explanations for graph neural networks
Qiang Huang, Makoto Yamada, Yuan Tian, Dinesh Singh, Dawei Yin, and Yi Chang · 2020
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Hierarchical generation of molecular graphs using structural motifs
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2020
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Parameterized explainer for graph neural network
Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang · 2020
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Tudataset: A collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M Kriege, Franka Bause, Kristian Kersting, Petra Mutzel, and Marion Neumann · 2020
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Self-supervised graph transformer on large-scale molecular data
Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang · 2020
Relex: A model-agnostic relational model explainer
Yue Zhang, David Defazio, and Arti Ramesh · 2021
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Global explainability of gnns via logic combination of learned concepts
Steve Azzolin, Antonio Longa, Pietro Barbiero, Pietro Liò, and Andrea Passerini · 2022
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Generative models for molecular discovery: Recent advances and challenges
Camille Bilodeau, Wengong Jin, Tommi Jaakkola, Regina Barzilay, and Klavs F Jensen · 2022
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Improving graph neural network expressivity via subgraph isomorphism counting
Giorgos Bouritsas, Fabrizio Frasca, Stefanos Zafeiriou, and Michael M Bronstein · 2022
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Geometry-enhanced molecular representation learning for property prediction
Xiaomin Fang, Lihang Liu, Jieqiong Lei, Donglong He, Shanzhuo Zhang, Jingbo Zhou, Fan Wang, Hua Wu, and Haifeng Wang · 2022
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Higher-order explanations of graph neural networks via relevant walks
Thomas Schnake, Oliver Eberle, Jonas Lederer, Shinichi Nakajima, Kristof T Schütt, Klaus-Robert Müller, and Grégoire Montavon · 2020
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Pgm-explainer: Probabilistic graphical model explanations for graph neural networks
Minh N Vu and My T Thai · 2020
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Xgnn: Towards model-level explanations of graph neural networks
Hao Yuan, Jiliang Tang, Xia Hu, and Shuiwang Ji · 2020
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Molecular generative graph neural networks for drug discovery
Pietro Bongini, Monica Bianchini, and Franco Scarselli · 2021
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Mgrnn: Structure generation of molecules based on graph recurrent neural networks
Xin Lai, Peisong Yang, Kunfeng Wang, Qingyuan Yang, and Duli Yu · 2021
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Generative causal explanations for graph neural networks
Wanyu Lin, Hao Lan, and Baochun Li · 2021
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Gcexplainer: Human-in-the-loop concept-based explanations for graph neural networks
Lucie Charlotte Magister, Dmitry Kazhdan, Vikash Singh, and Pietro Liò · 2021
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Yong-Min Shin, Sun-Woo Kim, and Won-Yong Shin · 2022
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Does gnn pretraining help molecular representation?
Ruoxi Sun, Hanjun Dai, and Adams Wei Yu · 2022
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Reinforced causal explainer for graph neural networks
Xiang Wang, Yingxin Wu, An Zhang, Fuli Feng, Xiangnan He, and Tat-Seng Chua · 2022
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Gnninterpreter: A probabilistic generative model-level explanation for graph neural networks
Xiaoqi Wang and Han-Wei Shen · 2022
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Single-step retrosynthesis prediction by leveraging commonly preserved substructures
Lei Fang, Junren Li, Ming Zhao, Li Tan, and Jian-Guang Lou · 2023
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Pharmacophoric-constrained heterogeneous graph transformer model for molecular property prediction
Yinghui Jiang, Shuting Jin, Xurui Jin, Xianglu Xiao, Wenfan Wu, Xiangrong Liu, Qiang Zhang, Xiangxiang Zeng, Guang Yang, and Zhangming Niu · 2023
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polybert: a chemical language model to enable fully machine-driven ultrafast polymer informatics
Christopher Kuenneth and Rampi Ramprasad · 2023
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Graph neural networks for molecules
Yuyang Wang, Zijie Li, and Amir Barati Farimani · 2023
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Global concept-based interpretability for graph neural networks via neuron analysis
Han Xuanyuan, Pietro Barbiero, Dobrik Georgiev, Lucie Charlotte Magister, and Pietro Liò · 2023
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Zhaoning Yu and Hongyang Gao · 2023
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Hierarchical molecular graph self-supervised learning for property prediction
Xuan Zang, Xianbing Zhao, and Buzhou Tang · 2023
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