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Prediction of a molecule's 3D conformer ensemble from the molecular graph holds a key role in areas of cheminformatics and drug discovery.
A solution for the best rotation to relate two sets of vectors
Wolfgang Kabsch · 1976
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Distance geometry and molecular conformation , volume 74
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Uff, a full periodic table force field for molecular mechanics and molecular dynamics simulations
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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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Timothy F Havel · 1998
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Mmff vi. mmff94s option for energy minimization studies
Thomas A Halgren · 1999
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Geom: Energy-annotated molecular conformations for property prediction and molecular generation
Simon Axelrod and Rafael Gomez-Bombarelli · 2006
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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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Conformations and 3d pharmacophore searching
Christof H Schwab · 2010
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3d-qsar in drug design-a review
Jitender Verma, Vijay M Khedkar, and Evans C Coutinho · 2010
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Fred pose prediction and virtual screening accuracy
Mark McGann · 2011
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Computational ligand-based rational design: role of conformational sampling and force fields in model development
Jihyun Shim and Alexander D MacKerell Jr · 2011
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Molecular machine learning with conformer ensembles
Simon Axelrod and Rafael Gomez-Bombarelli · 2012
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Use of 3d properties to characterize beyond rule-of-5 property space for passive permeation
Cristiano RW Guimaraes, Alan M Mathiowetz, Marina Shalaeva, Gilles Goetz, and Spiros Liras · 2012
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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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Message passing networks for molecules with tetrahedral chirality
Lagnajit Pattanaik, Octavian E Ganea, Ian Coley, Klavs F Jensen, William H Green, and Connor W Coley · 2012
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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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Exploiting the potential energy landscape to sample free energy
Andrew J Ballard, Stefano Martiniani, Jacob D Stevenson, Sandeep Somani, and David J Wales · 2015
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Pushing the limits of a molecular mechanics force field to probe weak ch··· π \pi interactions in proteins
Arghya Barman, Bruce Batiste, and Donald Hamelberg · 2015
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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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Role of molecular dynamics and related methods in drug discovery
Marco De Vivo, Matteo Masetti, Giovanni Bottegoni, and Andrea Cavalli · 2016
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Pot python optimal transport library
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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End-to-end differentiable learning of protein structure
Mohammed AlQuraishi · 2019
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Exploration of chemical compound, conformer, and reaction space with meta-dynamics simulations based on tight-binding quantum chemical calculations
Stefan Grimme · 2019
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Directional message passing for molecular graphs
Johannes Klicpera, Janek Groß, and Stephan Günnemann · 2019
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Molecular geometry prediction using a deep generative graph neural network
Elman Mansimov, Omar Mahmood, Seokho Kang, and Kyunghyun Cho · 2019
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
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Rémi Flamary and Nicolas Courty · 2017
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Benchmarking commercial conformer ensemble generators
Nils-Ole Friedrich, Christina de Bruyn Kops, Florian Flachsenberg, Kai Sommer, Matthias Rarey, and Johannes Kirchmair · 2017
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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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Inductive representation learning on large graphs
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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Conformation generation: the state of the art
Paul CD Hawkins · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 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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Frank Noé, Simon Olsson, Jonas Köhler, and Hao Wu · 2019
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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, et al · 2019
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Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning
Pablo Gainza, Freyr Sverrisson, Frederico Monti, Emanuele Rodola, D Boscaini, MM Bronstein, and BE Correia · 2020
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Generalization and representational limits of graph neural networks
Vikas Garg, Stefanie Jegelka, and Tommi Jaakkola · 2020
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Improved protein structure prediction using potentials from deep learning
Andrew W Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander WR Nelson, Alex Bridgland, et al · 2020
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A generative model for molecular distance geometry
Gregor Simm and Jose Miguel Hernandez-Lobato · 2020
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Fast end-to-end learning on protein surfaces
Freyr Sverrisson, Jean Feydy, Bruno Correia, and Michael Bronstein · 2020
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Understanding conformational entropy in small molecules
Lucian Chan, Garrett M Morris, and Geoffrey R Hutchison · 2021
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Energy-free machine learning predictions of ab initio
Dominik Lemm, Guido Falk von Rudorff, and O Anatole von Lilienfeld · 2021
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
Kristof T Schütt, Oliver T Unke, and Michael Gastegger · 2021
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Learning neural generative dynamics for molecular conformation generation
Minkai Xu, Shitong Luo, Yoshua Bengio, Jian Peng, and Jian Tang · 2021
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