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The crux of molecular property prediction is to generate meaningful representations of the molecules.
Some properties of line digraphs
Frank Harary and Robert Z. Norman · 1960
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The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service
HL Morgan · 1965
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A reduction of a graph to a canonical form and an algebra arising during this reduction
Boris Weisfeiler and Andrei A Lehman · 1968
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Influence of ph on the toxicity of substituted phenols to fish
J Saarikoski and M Viluksela · 1981
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Smiles. 2. algorithm for generation of unique smiles notation
David Weininger, Arthur Weininger, and Joseph L Weininger · 1989
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The properties of known drugs. 1. molecular frameworks
Guy W Bemis and Mark A Murcko · 1996
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Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors)
Jerome Friedman, Trevor Hastie, Robert Tibshirani, et al · 2000
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Random forests
Leo Breiman · 2001
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Greedy function approximation: a gradient boosting machine
Jerome H Friedman · 2001
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Logistic regression
David G Kleinbaum, K Dietz, M Gail, Mitchel Klein, and Mitchell Klein · 2002
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Esol: estimating aqueous solubility directly from molecular structure
John S Delaney · 2004
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Assigning unique keys to chemical compounds for data integration: Some interesting counter examples
Greeshma Neglur, Robert L Grossman, and Bing Liu · 2005
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Circular fingerprints: flexible molecular descriptors with applications from physical chemistry to adme
Robert C Glen, Andreas Bender, Catrin H Arnby, Lars Carlsson, Scott Boyer, and James Smith · 2006
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Rdkit: Open-source cheminformatics, 2006
Greg Landrum et al · 2006
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Logp—making sense of the value
Sanjivanjit K Bhal · 2007
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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970 million druglike small molecules for virtual screening in the chemical universe database GDB-13
L. C. Blum and J.-L. Reymond · 2009
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Influence relevance voting: an accurate and interpretable virtual high throughput screening method
S Joshua Swamidass, Chloé-Agathe Azencott, Ting-Wan Lin, Hugo Gramajo, Shiou-Chuan Tsai, and Pierre Baldi · 2009
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How to improve r&d productivity: the pharmaceutical industry’s grand challenge
Steven M Paul, Daniel S Mytelka, Christopher T Dunwiddie, Charles C Persinger, Bernard H Munos, Stacy R Lindborg, and Aaron L Schacht · 2010
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Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
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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 · 2011
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Joint learning of words and meaning representations for open-text semantic parsing
Antoine Bordes, Xavier Glorot, Jason Weston, and Yoshua Bengio · 2012
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A bayesian approach to in silico blood-brain barrier penetration modeling
Ines Filipa Martins, Ana L Teixeira, Luis Pinheiro, and Andre O Falcao · 2012
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A survey of multi-view machine learning
Shiliang Sun · 2013
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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
Cited alongside, same era.
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
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz 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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Seq2seq fingerprint: An unsupervised deep molecular embedding for drug discovery
Zheng Xu, Sheng Wang, Feiyun Zhu, and Junzhou Huang · 2017
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Opportunities and obstacles for deep learning in biology and medicine
Travers Ching, Daniel S Himmelstein, Brett K Beaulieu-Jones, Alexandr A Kalinin, Brian T Do, Gregory P Way, Enrico Ferrero, Paul-Michael Agapow, Michael Zietz, Michael M Hoffman, et al · 2018
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Embedding logical queries on knowledge graphs
Will Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, and Jure Leskovec · 2018
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Kelvin Guu, John Miller, and Percy Liang · 2015
Cited alongside, same era.
The sider database of drugs and side effects
Michael Kuhn, Ivica Letunic, Lars Juhl Jensen, and Peer Bork · 2015
Cited alongside, same era.
pkcsm: predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures
Douglas EV Pires, Tom L Blundell, and David B Ascher · 2015
Cited alongside, same era.
Electronic spectra from tddft and machine learning in chemical space
Raghunathan Ramakrishnan, Mia Hartmann, Enrico Tapavicza, and O Anatole Von Lilienfeld · 2015
Cited alongside, same era.
A data-driven approach to predicting successes and failures of clinical trials
Kaitlyn M Gayvert, Neel S Madhukar, and Olivier Elemento · 2016
Cited alongside, same era.
Stanisław Jastrzębski, Damian Leśniak, and Wojciech Marian Czarnecki · 2016
Cited alongside, same era.
Molecular graph convolutions: moving beyond fingerprints
Steven Kearnes, Kevin McCloskey, Marc Berndl, Vijay Pande, and Patrick Riley · 2016
Cited alongside, same era.
Adaptive sampling towards fast graph representation learning
Wenbing Huang, Tong Zhang, Yu Rong, and Junzhou Huang · 2018
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Adaptive graph convolutional neural networks
Ruoyu Li, Sheng Wang, Feiyun Zhu, and Junzhou Huang · 2018
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Seongok Ryu, Jaechang Lim, Seung Hwan Hong, and Woo Youn Kim · 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
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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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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2018
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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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Interpretable deep learning in drug discovery
Kristina Preuer, Günter Klambauer, Friedrich Rippmann, Sepp Hochreiter, and Thomas Unterthiner · 2019
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Deep graph library: Towards efficient and scalable deep learning on graphs
Minjie Wang, Lingfan Yu, Da Zheng, Quan Gan, Yu Gai, Zihao Ye, Mufei Li, Jinjing Zhou, Qi Huang, Chao Ma, Ziyue Huang, Qipeng Guo, Hao Zhang, Haibin Lin, Junbo Zhao, Jinyang Li, Alexander J Smola, and Zheng Zhang · 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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Directional message passing for molecular graphs
Johannes Klicpera, Janek Groß, and Stephan Günnemann · 2020
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Dropedge: Towards deep graph convolutional networks on node classification
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang · 2020
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