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Molecular "fingerprints" encoding structural information are the workhorse of cheminformatics and machine learning in drug discovery applications.
Variations of box plots
Robert McGill, John W Tukey, and Wayne A Larsen · 1978
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Robert McGill, John W Tukey, and Wayne A Larsen · 1978
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Christian Merkwirth and Thomas Lengauer · 2005
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The use of three-dimensional shape and electrostatic similarity searching in the identification of a melanin-concentrating hormone receptor 1 antagonist
Steven W Muchmore, Andrew J Souers, and Irini Akritopoulou-Zanze · 2006
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Ultrafast shape recognition to search compound databases for similar molecular shapes
Pedro J Ballester and W Graham Richards · 2007
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Comparison of shape-matching and docking as virtual screening tools
Paul CD Hawkins, A Geoffrey Skillman, and Anthony Nicholls · 2007
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Evaluating virtual screening methods: good and bad metrics for the “early recognition” problem
Jean-François Truchon and Christopher I Bayly · 2007
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Recommendations for evaluation of computational methods
Ajay N Jain and Anthony Nicholls · 2008
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Recommendations for evaluation of computational methods
Ajay N Jain and Anthony Nicholls · 2008
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Neural network for graphs: A contextual constructive approach
Alessio Micheli · 2009
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Sebastian G Rohrer and Knut Baumann · 2009
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The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 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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Molecular Descriptors for Chemoinformatics, Volume 41 (2 Volume Set) , volume 41
Roberto Todeschini and Viviana Consonni · 2009
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Molecular shape and medicinal chemistry: a perspective
Anthony Nicholls, Georgia B McGaughey, Robert P Sheridan, Andrew C Good, Gregory Warren, Magali Mathieu, Steven W Muchmore, Scott P Brown, J Andrew Grant, James A Haigh, et al · 2010
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Extended-connectivity fingerprints
David Rogers and Mathew Hahn · 2010
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Statsmodels: Econometric and statistical modeling with python
Multi-task neural networks for QSAR predictions
George E Dahl, Navdeep Jaitly, and Ruslan Salakhutdinov · 2014
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RDKit: Open-source cheminformatics; http://www.rdkit.org, 2014
Greg Landrum · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems
Martın Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al · 2015
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Classifying plankton with deep neural networks
Sander Dieleman · 2015
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Convolutional networks on graphs for learning molecular fingerprints
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Skipper Seabold and Josef Perktold · 2010
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Adaptive subgradient methods for online learning and stochastic optimization
John Duchi, Elad Hazan, and Yoram Singer · 2011
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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Deep learning how I did it: Merck 1st place interview
George Dahl · 2012
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Directory of useful decoys, enhanced (DUD-E): better ligands and decoys for better benchmarking
Michael M Mysinger, Michael Carchia, John J Irwin, and Brian K Shoichet · 2012
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Rethinking molecular similarity: comparing compounds on the basis of biological activity
Paula M Petrone, Benjamin Simms, Florian Nigsch, Eugen Lounkine, Peter Kutchukian, Allen Cornett, Zhan Deng, John W Davies, Jeremy L Jenkins, and Meir Glick · 2012
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PubChem’s BioAssay database
Yanli Wang, Jewen Xiao, Tugba O Suzek, Jian Zhang, Jiyao Wang, Zhigang Zhou, Lianyi Han, Karen Karapetyan, Svetlana Dracheva, Benjamin A Shoemaker, et al · 2012
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David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alán Aspuru-Guzik, and Ryan P Adams · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 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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Geodesic convolutional neural networks on riemannian manifolds
Jonathan Masci, Davide Boscaini, Michael Bronstein, and Pierre Vandergheynst · 2015
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Deeptox: Toxicity prediction using deep learning
Andreas Mayr, Günter Klambauer, Thomas Unterthiner, and Sepp Hochreiter · 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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Going deeper with convolutions
Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich · 2015
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Izhar Wallach, Michael Dzamba, and Abraham Heifets · 2015
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