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Leveraging domain knowledge including fingerprints and functional groups in molecular representation learning is crucial for chemical property prediction and drug discovery.
Chembl: a large-scale bioactivity database for drug discovery
Anna Gaulton, Louisa J Bellis, A Patricia Bento, Jon Chambers, Mark Davies, Anne Hersey, Yvonne Light, Shaun McGlinchey, David Michalovich, Bissan Al-Lazikani, et al · 2012
Earlier work this paper cites.
Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
Earlier work this paper cites.
Zinc 15–ligand discovery for everyone
Teague Sterling and John J Irwin · 2015
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Grasping frequent subgraph mining for bioinformatics applications
Aida Mrzic, Pieter Meysman, Wout Bittremieux, Pieter Moris, Boris Cule, Bart Goethals, and Kris Laukens · 2018
Earlier work this paper cites.
Moleculenet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 2018
Earlier work this paper cites.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
Earlier work this paper cites.
Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2019
Cited alongside, same era.
Graph matching networks for learning the similarity of graph structured objects
Yujia Li, Chenjie Gu, Thomas Dullien, Oriol Vinyals, and Pushmeet Kohli · 2019
Cited alongside, same era.
Deep graph infomax
Petar Velickovic, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm · 2019
Cited alongside, same era.
Infoxlm: An information-theoretic framework for cross-lingual language model pre-training
Zewen Chi, Li Dong, Furu Wei, Nan Yang, Saksham Singhal, Wenhui Wang, Xia Song, Xian-Ling Mao, Heyan Huang, and Ming Zhou · 2020
Cited alongside, same era.
Graph random neural networks for semi-supervised learning on graphs
Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, and Jie Tang · 2020
Directional message passing for molecular graphs
Johannes Klicpera, Janek Groß, and Stephan Günnemann · 2020
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Towards deeper graph neural networks
Meng Liu, Hongyang Gao, and Shuiwang Ji · 2020
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Gcc: Graph contrastive coding for graph neural network pre-training
Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, and Jie Tang · 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
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Communicative representation learning on attributed molecular graphs
Ying Song, Shuangjia Zheng, Zhangming Niu, Zhang-Hua Fu, Yutong Lu, and Yuedong Yang · 2020
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Cited alongside, same era.
Contrastive multi-view representation learning on graphs
Kaveh Hassani and Amir Hosein Khasahmadi · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
Cited alongside, same era.
Gpt-gnn: Generative pre-training of graph neural networks
Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, and Yizhou Sun · 2020
Cited alongside, same era.
Shuangjia Zheng, Yongjian Li, Sheng Chen, Jun Xu, and Yuedong Yang · 2020
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Deep graph contrastive representation learning
Yanqiao Zhu, Yichen Xu, Feng Yu, Qiang Liu, Shu Wu, and Liang Wang · 2020
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