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Molecular conformer generation is a fundamental task in computational chemistry.
Survey sampling
Leslie Kish · 1965
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Reverse-time diffusion equation models
Brian DO Anderson · 1982
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A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
Michael F Hutchinson · 1989
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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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Annealed importance sampling
Radford M Neal · 2001
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How important is parity violation for molecular and biomolecular chirality?
Martin Quack · 2002
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Caesar: a new conformer generation algorithm based on recursive buildup and local rotational symmetry consideration
Jiabo Li, Tedman Ehlers, Jon Sutter, Shikha Varma-O’Brien, and Johannes Kirchmair · 2007
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The exploration of macrocycles for drug discovery—an underexploited structural class
Edward M Driggers, Stephen P Hale, Jinbo Lee, and Nicholas K Terrett · 2008
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Dg-ammos: A new tool to generate 3d conformation of small molecules using d istance g eometry and a utomated m olecular m echanics o ptimization for in silico s creening
David Lagorce, Tania Pencheva, Bruno O Villoutreix, and Maria A Miteva · 2009
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Conformer generation with omega: algorithm and validation using high quality structures from the protein databank and cambridge structural database
Paul CD Hawkins, A Geoffrey Skillman, Gregory L Warren, Benjamin A Ellingson, and Matthew T Stahl · 2010
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Frog2: Efficient 3d conformation ensemble generator for small compounds
Maria A Miteva, Frederic Guyon, and Pierre Tuffery · 2010
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Pubchem3d: conformer generation
Evan E Bolton, Sunghwan Kim, and Stephen H Bryant · 2011
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Stochastic models, information theory, and Lie groups, volume 2: Analytic methods and modern applications , volume 2
Gregory S Chirikjian · 2011
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Conformer generation with omega: learning from the data set and the analysis of failures
Paul CD Hawkins and Anthony Nicholls · 2012
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Rdkit: A software suite for cheminformatics, computational chemistry, and predictive modeling, 2013
Greg Landrum et al · 2013
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Better informed distance geometry: using what we know to improve conformation generation
Sereina Riniker and Gregory A Landrum · 2015
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On implementing 2d rectangular assignment algorithms
David F Crouse · 2016
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Conformation generation: the state of the art
Paul CD Hawkins · 2017
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Kristof Schütt, Pieter-Jan Kindermans, Huziel Enoc Sauceda Felix, Stefan Chmiela, Alexandre Tkatchenko, and Klaus-Robert Müller · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
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Knowledge-based conformer generation using the cambridge structural database
Jason C Cole, Oliver Korb, Patrick McCabe, Murray G Read, and Robin Taylor · 2018
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Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
A geometric deep learning approach to predict binding conformations of bioactive molecules
Oscar Méndez-Lucio, Mazen Ahmad, Ehecatl Antonio del Rio-Chanona, and Jörg Kurt Wegner · 2021
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Bootstrap your flow
Laurence Illing Midgley, Vincent Stimper, Gregor NC Simm, and José Miguel Hernández-Lobato · 2021
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Improved denoising diffusion probabilistic models
Alex Nichol and Prafulla Dhariwal · 2021
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E (n) equivariant graph neural networks
Vıctor Garcia Satorras, Emiel Hoogeboom, and Max Welling · 2021
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Learning gradient fields for molecular conformation generation
Chence Shi, Shitong Luo, Minkai Xu, and Jian Tang · 2021
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Score-based generative modeling through stochastic differential equations
Yang Song, Jascha Sohl-Dickstein, Diederik P Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole · 2021
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Gfn2-xtb—an accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions
Christoph Bannwarth, Sebastian Ehlert, and Stefan Grimme · 2019
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Spacetime and geometry
Sean M Carroll · 2019
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Score-based generative modeling in latent space
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Geom, energy-annotated molecular conformations for property prediction and molecular generation
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