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Effective molecular representation learning is of great importance to facilitate molecular property prediction, which is a fundamental task for the drug and material industry.
Smiles, a chemical language and information system. 1. introduction to methodology and encoding rules
David Weininger · 1988
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Effects of geometric isomerism and ligand substitution in bifunctional dinuclear platinum complexes on binding properties and conformational changes in dna
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The properties of known drugs. 1. molecular frameworks
Guy W Bemis and Mark A Murcko · 1996
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Merck molecular force field. i. basis, form, scope, parameterization, and performance of MMFF94
Thomas A. Halgren · 1996
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The use of the area under the ROC curve in the evaluation of machine learning algorithms
Andrew P. Bradley · 1997
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Reoptimization of MDL keys for use in drug discovery
Joseph L. Durant, Burton A. Leland, Douglas R. Henry, and James G. Nourse · 2002
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Interactions of cisplatin and transplatin with proteins: Comparison of binding kinetics, binding sites and reactivity of the pt-protein adducts of cisplatin and transplatin towards biological nucleophiles
Tal Peleg-Shulman, Yousef Najajreh, and Dan Gibson · 2002
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Radial basis functions: theory and implementations
Martin D Buhmann · 2003
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ESOL: estimating aqueous solubility directly from molecular structure
John S. Delaney · 2004
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Rdkit: Open-source cheminformatics
Greg Landrum et al · 2006
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970 million druglike small molecules for virtual screening in the chemical universe database gdb-13
Lorenz C Blum and Jean-Louis Reymond · 2009
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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. Patrícia Bento, Jon Chambers, Mark Davies, Anne Hersey, Yvonne Light, Shaun McGlinchey, David Michalovich, Bissan Al-Lazikani, and John P. Overington · 2012
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A bayesian approach to
Ines Filipa Martins, Ana L. Teixeira, Luis Pinheiro, and André O. Falcão · 2012
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Enumeration of 166 billion organic small molecules in the chemical universe database GDB-17
Lars Ruddigkeit, Ruud van Deursen, Lorenz C. Blum, and Jean-Louis Reymond · 2012
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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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Recurrent neural network regularization
Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals · 2014
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Molecular fingerprint similarity search in virtual screening
Adrià Cereto-Massagué, María José Ojeda, Cristina Valls, Miquel Mulero, Santiago Garcia-Vallvé, and Gerard Pujadas · 2015
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A. Efros · 2015
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Convolutional networks on graphs for learning molecular fingerprints
David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P. Adams · 2015
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 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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ZINC 15 - ligand discovery for everyone
Teague Sterling and John J. Irwin · 2015
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Lei Jimmy Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton · 2016
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A data-driven approach to predicting successes and failures of clinical trials
Smiles2vec: Predicting chemical properties from text representations
Garrett B Goh, Nathan Hodas, Charles Siegel, and Abhinav Vishnu · 2018
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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N-gram graph: Simple unsupervised representation for graphs, with applications to molecules
Shengchao Liu, Mehmet Furkan Demirel, and Yingyu Liang · 2019
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Deep learning for the life sciences: applying deep learning to genomics, microscopy, drug discovery, and more
Bharath Ramsundar, Peter Eastman, Patrick Walters, and Vijay Pande · 2019
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Gated graph recursive neural networks for molecular property prediction
Hiroyuki Shindo and Yuji Matsumoto · 2019
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Kaitlyn M Gayvert, Neel S Madhukar, and Olivier Elemento · 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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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2016
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The SIDER database of drugs and side effects
Michael Kuhn, Ivica Letunic, Lars Juhl Jensen, and Peer Bork · 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
Govindan Subramanian, Bharath Ramsundar, Vijay Pande, and Rajiah Aldrin Denny · 2016
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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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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 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, Andrew Palmer, Volker Settels, Tommi Jaakkola, Klavs Jensen, and Regina Barzilay · 2019
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Deberta: Decoding-enhanced BERT with disentangled attention
Pengcheng He, Xiaodong Liu, Jianfeng Gao, and Weizhu Chen · 2020
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Strategies for pre-training graph neural networks
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Deeppurpose: a deep learning based drug repurposing toolkit
Kexin Huang, Tianfan Fu, Cao Xiao, Lucas Glass, and Jimeng Sun · 2020
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Learn molecular representations from large-scale unlabeled molecules for drug discovery
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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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Molecular property prediction: recent trends in the era of artificial intelligence
Jie Shen and Christos A Nicolaou · 2020
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Heterogeneous molecular graph neural networks for predicting molecule properties
Zeren Shui and George Karypis · 2020
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Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization
Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang · 2020
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A compact review of molecular property prediction with graph neural networks
Oliver Wieder, Stefan Kohlbacher, Mélaine Kuenemann, Arthur Garon, Pierre Ducrot, Thomas Seidel, and Thierry Langer · 2020
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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, and Mingyue Zheng · 2020
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Jintang Li, Kun Xu, Liang Chen, Zibin Zheng, and Xiao Liu · 2021
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