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The recent success of graph neural networks has significantly boosted molecular property prediction, advancing activities such as drug discovery.
SMILES. 2. Algorithm for generation of unique SMILES notation. In Journal of Chemical Information and Computer Sciences
David Weininger, Arthur Weininger, and Joseph L Weininger. 1989 · 1989
Earlier work this paper cites.
Visualizing data using t-SNE. In Journal of Machine Learning Research
Laurens van der Maaten and Geoffrey Hinton. 2008 · 2008
Earlier work this paper cites.
Early anomaly detection by learning and forecasting behavior. In arXiv preprint arXiv:2010.10016
Tong Zhao, Bo Ni, Wenhao Yu, and Meng Jiang. 2020 · 2010
Earlier work this paper cites.
Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling
Greg Landrum. 2013 · 2013
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality. In Advances in Neural Information Processing Systems (NeurIPS)
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
Earlier work this paper cites.
Similarity maps-a visualization strategy for molecular fingerprints and machine-learning methods. In Journal of Cheminformatics
Sereina Riniker and Gregory A Landrum. 2013 · 2013
Earlier work this paper cites.
Computational methods in drug discovery. In Pharmacological Reviews
Gregory Sliwoski, Sandeepkumar Kothiwale, Jens Meiler, and Edward W Lowe. 2014 · 2014
Earlier work this paper cites.
An analysis of the attrition of drug candidates from four major pharmaceutical companies. In Nature Reviews Drug Discovery
Michael J Waring, John Arrowsmith, Andrew R Leach, Paul D Leeson, Sam Mandrell, Robert M Owen, Garry Pairaudeau, William D Pennie, Stephen D Pickett, Jibo Wang, et al · 2015
Earlier work this paper cites.
The SIDER database of drugs and side effects. In Nucleic Acids Research
Michael Kuhn, Ivica Letunic, Lars Juhl Jensen, and Peer Bork. 2016 · 2016
Earlier work this paper cites.
Matching networks for one shot learning. In Advances in Neural Information Processing Systems (NeurIPS)
Oriol Vinyals, Charles Blundell, Timothy Lillicrap, Daan Wierstra, et al · 2016
Earlier work this paper cites.
Low data drug discovery with one-shot learning. In ACS Central Science
Han Altae-Tran, Bharath Ramsundar, Aneesh S Pappu, and Vijay Pande. 2017 · 2017
Earlier work this paper cites.
Model-agnostic meta-learning for fast adaptation of deep networks. In International Conference on Machine Learning (ICML)
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
Earlier work this paper cites.
Neural message passing for quantum chemistry. In International Conference on Machine Learning (ICML)
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017 · 2017
Earlier work this paper cites.
Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks. In International Conference for Learning Representation
Thomas N Kipf and Max Welling. 2017 · 2017
Cited alongside, same era.
A structured self-attentive sentence embedding. In International Conference for Learning Representation (ICLR)
Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, Mo Yu, Bing Xiang, Bowen Zhou, and Yoshua Bengio. 2017 · 2017
Cited alongside, same era.
Seq2seq fingerprint: An unsupervised deep molecular embedding for drug discovery. In ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (BCB)
Zheng Xu, Sheng Wang, Feiyun Zhu, and Junzhou Huang. 2017 · 2017
Cited alongside, same era.
Meta-learning with differentiable closed-form solvers. In International Conference for Learning Representation (ICLR)
Luca Bertinetto, Joao F Henriques, Philip HS Torr, and Andrea Vedaldi. 2018 · 2018
Cited alongside, same era.
Molecular geometry prediction using a deep generative graph neural network. In Scientific Reports
Elman Mansimov, Omar Mahmood, Seokho Kang, and Kyunghyun Cho. 2019 · 2019
Later among the works it cites.
Session-based social recommendation via dynamic graph attention networks. In The ACM International Conference on Web Search and Data Mining
Weiping Song, Zhiping Xiao, Yifan Wang, Laurent Charlin, Ming Zhang, and Jian Tang. 2019 · 2019
Later among the works it cites.
Applications of machine learning in drug discovery and development. In Nature Reviews Drug Discovery
Jessica Vamathevan, Dominic Clark, Paul Czodrowski, Ian Dunham, Edgardo Ferran, George Lee, Bin Li, Anant Madabhushi, Parantu Shah, Michaela Spitzer, et al · 2019
Later among the works it cites.
SMILES-BERT: Large scale unsupervised pre-training for molecular property prediction. In ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (BCB)
Sheng Wang, Yuzhi Guo, Yuhong Wang, Hongmao Sun, and Junzhou Huang. 2019 · 2019
Later among the works it cites.
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Bert: Pre-training of deep bidirectional transformers for language understanding. In Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
Few-shot learning with graph neural networks. In International Conference for Learning Representation (ICLR)
Victor Garcia and Joan Bruna. 2018 · 2018
Cited alongside, same era.
Gradient-based meta-learning with learned layerwise metric and subspace. In International Conference on Machine Learning
Yoonho Lee and Seungjin Choi. 2018 · 2018
Cited alongside, same era.
Learning to compare: relation network for few-shot learning. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip HS Torr, and Timothy M Hospedales. 2018 · 2018
Cited alongside, same era.
Graph attention networks. In International Conference for Learning Representation (ICLR)
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018 · 2018
Cited alongside, same era.
MoleculeNet: a benchmark for molecular machine learning. In Chemical Science
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande. 2018 · 2018
Cited alongside, same era.
Graph neural networks for social recommendation. In The Web Conference
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019 · 2019
Cited alongside, same era.
Edge-labeling graph neural network for few-shot learning. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Jongmin Kim, Taesup Kim, Sungwoong Kim, and Chang D Yoo. 2019 · 2019
Cited alongside, same era.
How powerful are graph neural networks?. In International Conference for Learning Representation (ICLR)
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019 · 2019
Later among the works it cites.
Learning from multiple cities: A meta-learning approach for spatial-temporal prediction. In The Web Conference (WWW)
Huaxiu Yao, Yiding Liu, Ying Wei, Xianfeng Tang, and Zhenhui Li. 2019 · 2019
Later among the works it cites.
Heterogeneous graph neural network. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)
Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, and Nitesh V Chawla. 2019 · 2019
Later among the works it cites.
Identifying structure–property relationships through SMILES syntax analysis with self-attention mechanism. In Journal of Chemical Information and Modeling
Shuangjia Zheng, Xin Yan, Yuedong Yang, and Jun Xu. 2019 · 2019
Later among the works it cites.
Meta-GNN: On few-shot node classification in graph meta-learning. In International Conference on Information and Knowledge Management (CIKM)
Fan Zhou, Chengtai Cao, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, and Ji Geng. 2019 · 2019
Later among the works it cites.
GraSeq: graph and sequence fusion learning for molecular property prediction. In International Conference on Information and Knowledge Management (CIKM)
Zhichun Guo, Wenhao Yu, Chuxu Zhang, Meng Jiang, and Nitesh Chawla. 2020 · 2020
Later among the works it cites.
Strategies for pre-training graph neural networks. In International Conference for Learning Representation (ICLR)
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec. 2020 · 2020
Later among the works it cites.
A comprehensive survey on graph neural networks. In IEEE Transactions on Neural Networks and Learning Systems
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip. 2020 · 2020
Later among the works it cites.
Graph few-shot learning via knowledge transfer. In AAAI Conference on Artificial Intelligence (AAAI)
Huaxiu Yao, Chuxu Zhang, Ying Wei, Meng Jiang, Suhang Wang, Junzhou Huang, Nitesh Chawla, and Zhenhui Li. 2020 · 2020
Later among the works it cites.
Identifying referential intention with heterogeneous contexts. In The Web Conference (WWW)
Wenhao Yu, Mengxia Yu, Tong Zhao, and Meng Jiang. 2020 · 2020
Later among the works it cites.