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Generating point clouds, e.g., molecular structures, in arbitrary rotations, translations, and enumerations remains a challenging task.
Remarks to maurice frechet’s article“sur la definition axiomatique d’une classe d’espace distances vectoriellement applicable sur l’espace de hilbert
Isaac J Schoenberg · 1935
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The hungarian method for the assignment problem
H. W. Kuhn · 1955
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Euclidean distance geometry
John Clifford Gower · 1982
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Mopac: a semiempirical molecular orbital program
James JP Stewart · 1990
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Solving euclidean distance matrix completion problems via semidefinite programming
Abdo Y Alfakih, Amir Khandani, and Henry Wolkowicz · 1999
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Generalized neural-network representation of high-dimensional potential-energy surfaces
J. Behler and M. Parrinello · 2007
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Optimal transport: old and new
Cédric Villani · 2008
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Convex optimization & Euclidean distance geometry
Jon Dattorro · 2010
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Open babel: An open chemical toolbox
Noel M O’Boyle, Michael Banck, Craig A James, Chris Morley, Tim Vandermeersch, and Geoffrey R Hutchison · 2011
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Fast and accurate modeling of molecular atomization energies with machine learning
M. Rupp, A. Tkatchenko, K.-R. Müller, and O. A. Von Lilienfeld · 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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Euclidean distance matrices and applications
Nathan Krislock and Henry Wolkowicz · 2012
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On-the-fly learning and sampling of ligand binding by high-throughput molecular simulations
S. Doerr and G. De Fabritiis · 2014
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Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole von Lilienfeld · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Mdtraj: A modern open library for the analysis of molecular dynamics trajectories
R. T. McGibbon, K. A. Beauchamp, M. P. Harrigan, C. Klein, J. M. Swails, C. X. Hernández, C. R. Schwantes, L. P. Wang, T. J. Lane, and V. S. Pande · 2015
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 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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Riemannian svrg: Fast stochastic optimization on riemannian manifolds
Hongyi Zhang, Sashank J Reddi, and Suvrit Sra · 2016
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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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Pointnet: Deep learning on point sets for 3d classification and segmentation
Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas · 2017
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Predicting electronic structure properties of transition metal complexes with neural networks
Machine learning of energetic material properties
Brian C Barnes, Daniel C Elton, Zois Boukouvalas, DeCarlos E Taylor, William D Mattson, Mark D Fuge, and Peter W Chung · 2018
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Applying machine learning techniques to predict the properties of energetic materials
Daniel C Elton, Zois Boukouvalas, Mark S Butrico, Mark D Fuge, and Peter W Chung · 2018
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Tuning the molecular weight distribution from atom transfer radical polymerization using deep reinforcement learning
Haichen Li, Christopher R Collins, Thomas G Ribelli, Krzysztof Matyjaszewski, Geoffrey J Gordon, Tomasz Kowalewski, and David J Yaron · 2018
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Deep reinforcement learning for de novo drug design
Mariya Popova, Olexandr Isayev, and Alexander Tropsha · 2018
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Can we accelerate medicinal chemistry by augmenting the chemist with big data and artificial intelligence?
Edward J Griffen, Alexander G Dossetter, Andrew G Leach, and Shane Montague · 2018
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Jon Paul Janet and Heather J Kulik · 2017
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
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Ani-1: an extensible neural network potential with dft accuracy at force field computational cost
J. S. Smith, O. Isayev, and A. E. Roitberg · 2017
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Objective-reinforced generative adversarial networks (organ) for sequence generation models
Gabriel Lima Guimaraes, Benjamin Sanchez-Lengeling, Carlos Outeiral, Pedro Luis Cunha Farias, and Alán Aspuru-Guzik · 2017
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Protein-protein association and binding mechanism resolved in atomic detail
N. Plattner, S. Doerr, G. De Fabritiis, and F. Noé · 2017
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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Improved training of wasserstein gans
Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, and Aaron C Courville · 2017
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Automatic chemical design using a data-driven continuous representation of molecules
R. Gómez-Bombarelli, J. N. Wei, D. Duvenaud, J. M. Hernández-Lobato, B. Sánchez-Lengeling, D. Sheberla, J. Aguilera-Iparraguirre, T. D Hirzel, R. P Adams, and A. Aspuru-Guzik · 2018
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Reweighted autoencoded variational bayes for enhanced sampling (rave)
João Marcelo Lamim Ribeiro, Pablo Bravo, Yihang Wang, and Pratyush Tiwary · 2018
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Generating equilibrium molecules with deep neural networks
Niklas WA Gebauer, Michael Gastegger, and Kristof T Schütt · 2018
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Schnet–a deep learning architecture for molecules and materials
Kristof T Schütt, Huziel E Sauceda, P-J Kindermans, Alexandre Tkatchenko, and K-R Müller · 2018
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Pointflow: 3d point cloud generation with continuous normalizing flows
Guandao Yang, Xun Huang, Zekun Hao, Ming-Yu Liu, Serge Belongie, and Bharath Hariharan · 2019
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Learning continuous and data-driven molecular descriptors by translating equivalent chemical representations
R. Winter, F. Montanari, F. Noé, and D.-A. Clevert · 2019
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Efficient multi-objective molecular optimization in a continuous latent space
R. Winter, F. Montanari, A. Steffen, H. Briem, F. Noé, and D. A. Clevert · 2019
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Neural networks-based variationally enhanced sampling
L. Bonati, Y.-Y.Zhang, and M. Parrinello · 2019
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Targeted adversarial learning optimized sampling
J. Zhang, Y. I. Yang, and F. Noé · 2019
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Boltzmann generators - sampling equilibrium states of many-body systems with deep learning
Frank Noé, Simon Olsson, Jonas Köhler, and Hao Wu · 2019
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Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Niklas WA Gebauer, Michael Gastegger, and Kristof T Schütt · 2019
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