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How to obtain informative representations of molecules is a crucial prerequisite in AI-driven drug design and discovery.
How should relative changes be measured?
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970 million druglike small molecules for virtual screening in the chemical universe database GDB-13
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A bayesian approach to in silico blood-brain barrier penetration modeling
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Freesolv: a database of experimental and calculated hydration free energies, with input files
David L Mobley and J Peter Guthrie · 2014
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Sequence to sequence learning with neural networks
Ilya Sutskever, Oriol Vinyals, and Quoc V Le · 2014
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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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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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The sider database of drugs and side effects
Michael Kuhn, Ivica Letunic, Lars Juhl Jensen, and Peer Bork · 2015
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Electronic spectra from tddft and machine learning in chemical space
Raghunathan Ramakrishnan, Mia Hartmann, Enrico Tapavicza, and O Anatole Von Lilienfeld · 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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Zinc 15–ligand discovery for everyone
Teague Sterling and John J Irwin · 2015
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Izhar Wallach, Michael Dzamba, and Abraham Heifets · 2015
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A data-driven approach to predicting successes and failures of clinical trials
Kaitlyn M Gayvert, Neel S Madhukar, and Olivier Elemento · 2016
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Stanisław Jastrzębski, Damian Leśniak, and Wojciech Marian Czarnecki · 2016
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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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Toxcast chemical landscape: paving the road to 21st century toxicology
Ann M Richard, Richard S Judson, Keith A Houck, Christopher M Grulke, Patra Volarath, Inthirany Thillainadarajah, Chihae Yang, James Rathman, Matthew T Martin, John F Wambaugh, et al · 2016
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Computational modeling of β \beta -secretase 1 (bace-1) inhibitors using ligand based approaches
Govindan Subramanian, Bharath Ramsundar, Vijay Pande, and Rajiah Aldrin Denny · 2016
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Improving language understanding with unsupervised learning
Alec Radford, Karthik Narasimhan, Time Salimans, and Ilya Sutskever · 2018
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Seongok Ryu, Jaechang Lim, Seung Hwan Hong, and Woo Youn Kim · 2018
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Horovod: fast and easy distributed deep learning in TensorFlow
Alexander Sergeev and Mike Del Balso · 2018
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Petar Veličković, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm · 2018
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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
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https://tripod.nih.gov/tox21/challenge/
Tox21 challenge, 2017 · 2017
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Supervised community detection with line graph neural networks
Zhengdao Chen, Xiang Li, and Joan Bruna · 2017
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Convolutional embedding of attributed molecular graphs for physical property prediction
Connor W Coley, Regina Barzilay, William H Green, Tommi S Jaakkola, and Klavs F Jensen · 2017
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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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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2019
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Semi-supervised graph classification: A hierarchical graph perspective
Jia Li, Yu Rong, Hong Cheng, Helen Meng, Wenbing Huang, and Junzhou Huang · 2019
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N-gram graph: Simple unsupervised representation for graphs, with applications to molecules
Shengchao Liu, Mehmet F Demirel, and Yingyu Liang · 2019
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Molecular property prediction: A multilevel quantum interactions modeling perspective
Chengqiang Lu, Qi Liu, Chao Wang, Zhenya Huang, Peize Lin, and Lixin He · 2019
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Detect rumors on twitter by promoting information campaigns with generative adversarial learning
Jing Ma, Wei Gao, and Kam-Fai Wong · 2019
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On asymptotic behaviors of graph cnns from dynamical systems perspective
Kenta Oono and Taiji Suzuki · 2019
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Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization
Fan-Yun Sun, Jordan Hoffman, Vikas Verma, and Jian Tang · 2019
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Smiles-bert: Large scale unsupervised pre-training for molecular property prediction
Sheng Wang, Yuzhi Guo, Yuhong Wang, Hongmao Sun, and Junzhou Huang · 2019
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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
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Analyzing learned molecular representations for property prediction
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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Rumor detection on social media with bi-directional graph convolutional networks
Tian Bian, Xi Xiao, Tingyang Xu, Peilin Zhao, Wenbing Huang, Yu Rong, and Junzhou Huang · 2020
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Tackling over-smoothing for general graph convolutional networks
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Transformers are graph neural networks, 2020
Chaitanya Joshi · 2020
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Directional message passing for molecular graphs
Johannes Klicpera, Janek Groß, and Stephan Günnemann · 2020
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Graph representation learning via graphical mutual information maximization
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Dropedge: Towards deep graph convolutional networks on node classification
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