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Machine learning techniques have recently been adopted in various applications in medicine, biology, chemistry, and material engineering.
The generation of a unique machine description for chemical structures-a technique developed at chemical abstracts service
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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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ESOL: Estimating Aqueous Solubility Directly from Molecular Structure
John S. Delaney · 2004
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Kernel methods for pattern analysis
John Shawe-Taylor, Nello Cristianini, et al · 2004
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Decoding by linear programming
Emmanuel J CANDES and Terence TAO · 2005
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The restricted isometry property and its implications for compressed sensing
Emmanuel J Candes · 2008
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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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Compressed learning: Universal sparse dimensionality reduction and learning in the measurement domain
Robert Calderbank, Sina Jafarpour, and Robert Schapire · 2009
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Maximum unbiased validation (muv) data sets for virtual screening based on pubchem bioactivity data
Sebastian G Rohrer and Knut Baumann · 2009
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Molecular descriptors for chemoinformatics: volume I: alphabetical listing/volume II: appendices, references
Roberto Todeschini and Viviana Consonni · 2009
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Thousands of chemical starting points for antimalarial lead identification
Francisco-Javier Gamo, Laura M. Sanz, Jaume Vidal, Cristina de Cozar, Emilio Alvarez, Jose-Luis Lavandera, Dana E. Vanderwall, Darren V. S. Green, Vinod Kumar, Samiul Hasan, James R. Brown, Catherine E. Peishoff, Lon R. Cardon, and Jose F. Garcia-Bustos · 2010
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The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid
Johannes Hachmann, Roberto Olivares-Amaya, Sule Atahan-Evrenk, Carlos Amador-Bedolla, Roel S. Sánchez-Carrera, Aryeh Gold-Parker, Leslie Vogt, Anna M. Brockway, and Alán Aspuru-Guzik · 2011
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Weisfeiler-lehman graph kernels
Nino Shervashidze, Pascal Schweitzer, Erik Jan van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt · 2011
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Deep learning how i did it: Merck 1st place interview
George Dahl · 2012
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Merck molecular activity challenge
Merck · 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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Baselines and bigrams: Simple, good sentiment and topic classification
Sida Wang and Christopher D Manning · 2012
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
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Tox21 data challenge 2014
Tox21 Data Challenge · 2014
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Deep learning as an opportunity in virtual screening
Thomas Unterthiner, Andreas Mayr, Günter Klambauer, Marvin Steijaert, Jörg K Wegner, Hugo Ceulemans, and Sepp Hochreiter · 2014
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Convolutional Networks on Graphs for Learning Molecular Fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alan Aspuru-Guzik, and Ryan P Adams · 2015
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
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Deep neural nets as a method for quantitative structure–activity relationships
Junshui Ma, Robert P Sheridan, Andy Liaw, George E Dahl, and Vladimir Svetnik · 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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Towards universal paraphrastic sentence embeddings
John Wieting, Mohit Bansal, Kevin Gimpel, and Karen Livescu · 2015
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A latent variable model approach to pmi-based word embeddings
Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, and Andrej Risteski · 2016
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Representation learning on graphs: Methods and applications
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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Grammar variational autoencoder
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
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Quantum-chemical insights from deep tensor neural networks
Kristof T Schütt, Farhad Arbabzadah, Stefan Chmiela, Klaus R Müller, and Alexandre Tkatchenko · 2017
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A compressed sensing view of unsupervised text embeddings, bag-of-n-grams, and lstm
Sanjeev Arora, Mikhail Khodak, Nikunj Saunshi, and Kiran Vodrahalli · 2018
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Linear algebraic structure of word senses, with applications to polysemy
Sanjeev Arora, Yuanzhi Li, Yingyu Liang, Tengyu Ma, and Andrej Risteski · 2018
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A simple but tough-to-beat baseline for sentence embeddings
Sanjeev Arora, Yingyu Liang, and Tengyu Ma · 2016
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Integrated deep learned transcriptomic and structure-based predictor of clinical trials outcomes
Artem V Artemov, Evgeny Putin, Quentin Vanhaelen, Alexander Aliper, Ivan V Ozerov, and Alex Zhavoronkov · 2016
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Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
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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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Automatic chemical design using a data-driven continuous representation of molecules
Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik · 2016
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node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
Cited alongside, same era.
Stanisław Jastrzębski, Damian Leśniak, and Wojciech Marian Czarnecki · 2016
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Machine learning for molecular and materials science
Keith T Butler, Daniel W Davies, Hugh Cartwright, Olexandr Isayev, and Aron Walsh · 2018
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Next-generation machine learning for biological networks
Diogo M Camacho, Katherine M Collins, Rani K Powers, James C Costello, and James J Collins · 2018
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The rise of deep learning in drug discovery
Hongming Chen, Ola Engkvist, Yinhai Wang, Marcus Olivecrona, and Thomas Blaschke · 2018
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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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Practical model selection for prospective virtual screening
Shengchao Liu, Moayad Alnammi, Spencer S Ericksen, Andrew F Voter, James L Keck, F Michael Hoffmann, Scott A Wildman, and Anthony Gitter · 2018
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Learning a Local-Variable Model of Aromatic and Conjugated Systems
Matthew K. Matlock, Na Le Dang, and S. Joshua Swamidass · 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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On the dimensionality of word embedding
Zi Yin and Yuanyuan Shen · 2018
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Hierarchical graph representation learning withdifferentiable pooling
Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L Hamilton, and Jure Leskovec · 2018
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Graph neural networks: A review of methods and applications
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, and Maosong Sun · 2018
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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Restricted isometry property under high correlations
Shiva Prasad Kasiviswanathan and Mark Rudelson · 2019
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Deep Learning for the Life Sciences
Bharath Ramsundar, Peter Eastman, Patrick Walters, Vijay Pande, Karl Leswing, and Zhenqin Wu · 2019
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S Yu · 2019
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