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Constructing appropriate representations of molecules lies at the core of numerous tasks such as material science, chemistry and drug designs.
Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
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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
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Massively multitask networks for drug discovery
Bharath Ramsundar, Steven Kearnes, Patrick Riley, Dale Webster, David Konerding, and Vijay Pande · 2015
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Molecular graph convolutions: moving beyond fingerprints
Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, and Patrick Riley · 2016
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Deeper insights into graph convolutional networks for semi-supervised learning
Qimai Li, Zhichao Han, and Xiao-Ming Wu · 2018
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Edge attention-based multi-relational graph convolutional networks
Chao Shang, Qinqing Liu, Ko-Shin Chen, Jiangwen Sun, Jin Lu, Jinfeng Yi, and Jinbo Bi · 2018
Cited alongside, same era.
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
Cited alongside, same era.
Path-augmented graph transformer network
Benson Chen, Regina Barzilay, and Tommi Jaakkola · 2019
Cited alongside, same era.
Smiles transformer: Pre-trained molecular fingerprint for low data drug discovery
Shion Honda, Shoi Shi, and Hiroki R Ueda · 2019
Cited alongside, same era.
Rapid prediction of nmr spectral properties with quantified uncertainty
Eric Jonas and Stefan Kuhn · 2019
Are learned molecular representations ready for prime time?
Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, et al · 2019
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Gresnet: Graph residual network for reviving deep gnns from suspended animation
Jiawei Zhang and Lin Meng · 2019
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Neural message passing for nmr chemical shift prediction
Youngchun Kwon, Dongseon Lee, Youn-Suk Choi, Myeonginn Kang, and Seokho Kang · 2020
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Molecule attention transformer
Łukasz Maziarka, Tomasz Danel, Sławomir Mucha, Krzysztof Rataj, Jacek Tabor, and Stanisław Jastrzębski · 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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Cited alongside, same era.
N-gram graph: Simple unsupervised representation for graphs, with applications to molecules
Shengchao Liu, Mehmet F Demirel, and Yingyu Liang · 2019
Cited alongside, same era.
Molecular property prediction: A multilevel quantum interactions modeling perspective
Chengqiang Lu, Qi Liu, Chao Wang, Zhenya Huang, Peize Lin, and Lixin He · 2019
Cited alongside, same era.
Pushing the boundaries of molecular representation for drug discovery with the graph attention mechanism
Zhaoping Xiong, Dingyan Wang, Xiaohong Liu, Feisheng Zhong, Xiaozhe Wan, Xutong Li, Zhaojun Li, Xiaomin Luo, Kaixian Chen, Hualiang Jiang, et al · 2019
Cited alongside, same era.
Later among the works it cites.
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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Building attention and edge message passing neural networks for bioactivity and physical–chemical property prediction
M Withnall, E Lindelöf, O Engkvist, and H Chen · 2020
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On layer normalization in the transformer architecture
Ruibin Xiong, Yunchang Yang, Di He, Kai Zheng, Shuxin Zheng, Chen Xing, Huishuai Zhang, Yanyan Lan, Liwei Wang, and Tie-Yan Liu · 2020
Later among the works it cites.